Integrate single cell data across individuals
Anthony Hung
2020-09-08
Last updated: 2020-09-22
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Knit directory: OAStrain/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 71d27f7 | Anthony Hung | 2020-09-22 | pseudobulk limma de, integrate across individuals, add n100 for pwr analysis |
| html | 71d27f7 | Anthony Hung | 2020-09-22 | pseudobulk limma de, integrate across individuals, add n100 for pwr analysis |
| Rmd | 0aa15d4 | Anthony Hung | 2020-09-10 | normalize the topic memberships to add up to 1 for each cell |
Introduction
I would like to analyze all the samples together in order to determine cell type heterogeneity between samples jointly. However, they cluster by individual currently. Treat each individual as a separate sample and perform integration to solve this problem for this analysis.
Per tips from Kenneth Barr for integration, first detect variable features within each sample you are integrating separately, then integrate across the individuals using the union of both sets of variable features.
library(Seurat)
library(gridExtra)
#split data into each individual/sample
#load single-cell data from pilot
#ANT1.2 <- readRDS("data/ANT1_2.rds")
SCT_integrated <- readRDS("data/SCT_ANT12_integrated.rds")
NA18855_Unstrain <- subset(SCT_integrated, labels == "NA18855_Unstrain")
NA18855_Strain <- subset(SCT_integrated, labels == "NA18855_Strain")
NA18856_Unstrain <- subset(SCT_integrated, labels == "NA18856_Unstrain")
NA19160_Unstrain <- subset(SCT_integrated, labels == "NA19160_Unstrain")
NA19160_Strain <- subset(SCT_integrated, labels == "NA19160_Strain")
seurat.list <- list(NA18855_Unstrain, NA18856_Unstrain, NA19160_Unstrain)
for (i in 1:length(seurat.list)) {
seurat.list[[i]] <- SCTransform(seurat.list[[i]], verbose = FALSE)
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Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reachedSCT.features <- SelectIntegrationFeatures(object.list = seurat.list, nfeatures = 5000)
seurat.list <- PrepSCTIntegration(object.list = seurat.list, anchor.features = SCT.features,
verbose = FALSE)
#find anchors
seurat.anchors <- FindIntegrationAnchors(object.list = seurat.list, normalization.method = "SCT",
anchor.features = SCT.features, verbose = FALSE)
SCT.integrated <- IntegrateData(anchorset = seurat.anchors, normalization.method = "SCT",
verbose = FALSE)Warning: Adding a command log without an assay associated with it#visualized integrated
SCT.integrated <- RunPCA(SCT.integrated, verbose = FALSE, npcs = 100)
DimPlot(SCT.integrated, reduction = "pca", group.by = c("labels"))
| Version | Author | Date |
|---|---|---|
| 71d27f7 | Anthony Hung | 2020-09-22 |
ElbowPlot(SCT.integrated, ndims = 100) #38 PCs?
| Version | Author | Date |
|---|---|---|
| 71d27f7 | Anthony Hung | 2020-09-22 |
SCT.integrated <- FindNeighbors(SCT.integrated, dims = 1:38)Computing nearest neighbor graphComputing SNNSCT.integrated <- FindClusters(SCT.integrated, resolution = 0.4)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1203
Number of edges: 67982
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6616
Number of communities: 3
Elapsed time: 0 secondsDimPlot(SCT.integrated, group.by = c("labels"), reduction = "pca")
| Version | Author | Date |
|---|---|---|
| 71d27f7 | Anthony Hung | 2020-09-22 |
DimPlot(SCT.integrated, group.by = c("seurat_clusters"), reduction = "pca")
| Version | Author | Date |
|---|---|---|
| 71d27f7 | Anthony Hung | 2020-09-22 |
SCT.integrated <- RunUMAP(SCT.integrated, dims = 1:38)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session21:28:41 UMAP embedding parameters a = 0.9922 b = 1.11221:28:41 Read 1203 rows and found 38 numeric columns21:28:41 Using Annoy for neighbor search, n_neighbors = 3021:28:41 Building Annoy index with metric = cosine, n_trees = 500% 10 20 30 40 50 60 70 80 90 100%[----|----|----|----|----|----|----|----|----|----|**************************************************|
21:28:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b7fd264
21:28:42 Searching Annoy index using 1 thread, search_k = 3000
21:28:42 Annoy recall = 100%
21:28:42 Commencing smooth kNN distance calibration using 1 thread
21:28:43 Initializing from normalized Laplacian + noise
21:28:43 Commencing optimization for 500 epochs, with 45072 positive edges
21:28:46 Optimization finishedfor(neighbors in 2:200){
for(distance in seq(0,0.2, 0.01)){
print(paste0(neighbors, " ", distance))
SCT.integrated <- RunUMAP(SCT.integrated, dims = 1:38,
n.neighbors = neighbors,
min.dist = distance)
plot(DimPlot(SCT.integrated, group.by = c("seurat_clusters")))
}
}[1] "2 0"21:28:46 UMAP embedding parameters a = 1.933 b = 0.7905
21:28:46 Read 1203 rows and found 38 numeric columns
21:28:46 Using Annoy for neighbor search, n_neighbors = 2
21:28:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87930e0fc
21:28:46 Searching Annoy index using 1 thread, search_k = 200
21:28:46 Annoy recall = 100%
21:28:47 Commencing smooth kNN distance calibration using 1 thread
21:28:47 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:28:47 Initializing from PCA
21:28:47 PCA: 2 components explained 21.2% variance
21:28:47 Commencing optimization for 500 epochs, with 1950 positive edges
21:28:48 Optimization finished[1] "2 0.01"21:28:48 UMAP embedding parameters a = 1.896 b = 0.8006
21:28:48 Read 1203 rows and found 38 numeric columns
21:28:48 Using Annoy for neighbor search, n_neighbors = 2
21:28:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ab05e1d
21:28:48 Searching Annoy index using 1 thread, search_k = 200
21:28:48 Annoy recall = 100%
21:28:49 Commencing smooth kNN distance calibration using 1 thread
21:28:49 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:28:49 Initializing from PCA
21:28:49 PCA: 2 components explained 21.2% variance
21:28:49 Commencing optimization for 500 epochs, with 1950 positive edges
21:28:50 Optimization finished
| Version | Author | Date |
|---|---|---|
| 71d27f7 | Anthony Hung | 2020-09-22 |
[1] "2 0.02"21:28:50 UMAP embedding parameters a = 1.859 b = 0.8109
21:28:50 Read 1203 rows and found 38 numeric columns
21:28:50 Using Annoy for neighbor search, n_neighbors = 2
21:28:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fb4c9bb
21:28:50 Searching Annoy index using 1 thread, search_k = 200
21:28:51 Annoy recall = 100%
21:28:51 Commencing smooth kNN distance calibration using 1 thread
21:28:51 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:28:51 Initializing from PCA
21:28:51 PCA: 2 components explained 21.2% variance
21:28:51 Commencing optimization for 500 epochs, with 1950 positive edges
21:28:52 Optimization finished
[1] "2 0.03"21:28:52 UMAP embedding parameters a = 1.822 b = 0.8212
21:28:52 Read 1203 rows and found 38 numeric columns
21:28:52 Using Annoy for neighbor search, n_neighbors = 2
21:28:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87103852d7
21:28:53 Searching Annoy index using 1 thread, search_k = 200
21:28:53 Annoy recall = 100%
21:28:53 Commencing smooth kNN distance calibration using 1 thread
21:28:53 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:28:53 Initializing from PCA
21:28:53 PCA: 2 components explained 21.2% variance
21:28:53 Commencing optimization for 500 epochs, with 1950 positive edges
21:28:54 Optimization finished
[1] "2 0.04"21:28:54 UMAP embedding parameters a = 1.786 b = 0.8316
21:28:54 Read 1203 rows and found 38 numeric columns
21:28:54 Using Annoy for neighbor search, n_neighbors = 2
21:28:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a1beb70
21:28:55 Searching Annoy index using 1 thread, search_k = 200
21:28:55 Annoy recall = 100%
21:28:55 Commencing smooth kNN distance calibration using 1 thread
21:28:56 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:28:56 Initializing from PCA
21:28:56 PCA: 2 components explained 21.2% variance
21:28:56 Commencing optimization for 500 epochs, with 1950 positive edges
21:28:56 Optimization finished
[1] "2 0.05"21:28:57 UMAP embedding parameters a = 1.75 b = 0.8421
21:28:57 Read 1203 rows and found 38 numeric columns
21:28:57 Using Annoy for neighbor search, n_neighbors = 2
21:28:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723b1a65b
21:28:57 Searching Annoy index using 1 thread, search_k = 200
21:28:57 Annoy recall = 100%
21:28:57 Commencing smooth kNN distance calibration using 1 thread
21:28:58 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:28:58 Initializing from PCA
21:28:58 PCA: 2 components explained 21.2% variance
21:28:58 Commencing optimization for 500 epochs, with 1950 positive edges
21:28:59 Optimization finished
[1] "2 0.06"21:28:59 UMAP embedding parameters a = 1.715 b = 0.8526
21:28:59 Read 1203 rows and found 38 numeric columns
21:28:59 Using Annoy for neighbor search, n_neighbors = 2
21:28:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87269367b4
21:28:59 Searching Annoy index using 1 thread, search_k = 200
21:28:59 Annoy recall = 100%
21:28:59 Commencing smooth kNN distance calibration using 1 thread
21:29:00 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:00 Initializing from PCA
21:29:00 PCA: 2 components explained 21.2% variance
21:29:00 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:01 Optimization finished
[1] "2 0.07"21:29:01 UMAP embedding parameters a = 1.68 b = 0.8631
21:29:01 Read 1203 rows and found 38 numeric columns
21:29:01 Using Annoy for neighbor search, n_neighbors = 2
21:29:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87225ccbdd
21:29:01 Searching Annoy index using 1 thread, search_k = 200
21:29:01 Annoy recall = 100%
21:29:01 Commencing smooth kNN distance calibration using 1 thread
21:29:02 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:02 Initializing from PCA
21:29:02 PCA: 2 components explained 21.2% variance
21:29:02 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:03 Optimization finished
[1] "2 0.08"21:29:03 UMAP embedding parameters a = 1.645 b = 0.8737
21:29:03 Read 1203 rows and found 38 numeric columns
21:29:03 Using Annoy for neighbor search, n_neighbors = 2
21:29:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745c6727a
21:29:03 Searching Annoy index using 1 thread, search_k = 200
21:29:03 Annoy recall = 100%
21:29:04 Commencing smooth kNN distance calibration using 1 thread
21:29:04 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:04 Initializing from PCA
21:29:04 PCA: 2 components explained 21.2% variance
21:29:04 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:05 Optimization finished
[1] "2 0.09"21:29:05 UMAP embedding parameters a = 1.611 b = 0.8844
21:29:05 Read 1203 rows and found 38 numeric columns
21:29:05 Using Annoy for neighbor search, n_neighbors = 2
21:29:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b12cd82
21:29:05 Searching Annoy index using 1 thread, search_k = 200
21:29:05 Annoy recall = 100%
21:29:06 Commencing smooth kNN distance calibration using 1 thread
21:29:06 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:06 Initializing from PCA
21:29:06 PCA: 2 components explained 21.2% variance
21:29:06 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:07 Optimization finished
[1] "2 0.1"21:29:07 UMAP embedding parameters a = 1.577 b = 0.8951
21:29:07 Read 1203 rows and found 38 numeric columns
21:29:07 Using Annoy for neighbor search, n_neighbors = 2
21:29:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773230601
21:29:08 Searching Annoy index using 1 thread, search_k = 200
21:29:08 Annoy recall = 100%
21:29:08 Commencing smooth kNN distance calibration using 1 thread
21:29:09 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:09 Initializing from PCA
21:29:09 PCA: 2 components explained 21.2% variance
21:29:09 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:09 Optimization finished
[1] "2 0.11"21:29:10 UMAP embedding parameters a = 1.544 b = 0.9058
21:29:10 Read 1203 rows and found 38 numeric columns
21:29:10 Using Annoy for neighbor search, n_neighbors = 2
21:29:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871150de74
21:29:10 Searching Annoy index using 1 thread, search_k = 200
21:29:10 Annoy recall = 100%
21:29:10 Commencing smooth kNN distance calibration using 1 thread
21:29:11 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:11 Initializing from PCA
21:29:11 PCA: 2 components explained 21.2% variance
21:29:11 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:12 Optimization finished
[1] "2 0.12"21:29:12 UMAP embedding parameters a = 1.51 b = 0.9165
21:29:12 Read 1203 rows and found 38 numeric columns
21:29:12 Using Annoy for neighbor search, n_neighbors = 2
21:29:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764453347
21:29:12 Searching Annoy index using 1 thread, search_k = 200
21:29:12 Annoy recall = 100%
21:29:13 Commencing smooth kNN distance calibration using 1 thread
21:29:13 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:13 Initializing from PCA
21:29:13 PCA: 2 components explained 21.2% variance
21:29:13 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:14 Optimization finished
[1] "2 0.13"21:29:14 UMAP embedding parameters a = 1.478 b = 0.9272
21:29:14 Read 1203 rows and found 38 numeric columns
21:29:14 Using Annoy for neighbor search, n_neighbors = 2
21:29:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716f4a523
21:29:14 Searching Annoy index using 1 thread, search_k = 200
21:29:15 Annoy recall = 100%
21:29:15 Commencing smooth kNN distance calibration using 1 thread
21:29:16 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:16 Initializing from PCA
21:29:16 PCA: 2 components explained 21.2% variance
21:29:16 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:17 Optimization finished
[1] "2 0.14"21:29:17 UMAP embedding parameters a = 1.446 b = 0.938
21:29:17 Read 1203 rows and found 38 numeric columns
21:29:17 Using Annoy for neighbor search, n_neighbors = 2
21:29:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e29a1f6
21:29:17 Searching Annoy index using 1 thread, search_k = 200
21:29:17 Annoy recall = 100%
21:29:18 Commencing smooth kNN distance calibration using 1 thread
21:29:18 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:18 Initializing from PCA
21:29:18 PCA: 2 components explained 21.2% variance
21:29:18 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:19 Optimization finished
[1] "2 0.15"21:29:19 UMAP embedding parameters a = 1.414 b = 0.9488
21:29:19 Read 1203 rows and found 38 numeric columns
21:29:19 Using Annoy for neighbor search, n_neighbors = 2
21:29:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763169376
21:29:20 Searching Annoy index using 1 thread, search_k = 200
21:29:20 Annoy recall = 100%
21:29:20 Commencing smooth kNN distance calibration using 1 thread
21:29:21 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:21 Initializing from PCA
21:29:21 PCA: 2 components explained 21.2% variance
21:29:21 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:22 Optimization finished
[1] "2 0.16"21:29:22 UMAP embedding parameters a = 1.383 b = 0.9596
21:29:22 Read 1203 rows and found 38 numeric columns
21:29:22 Using Annoy for neighbor search, n_neighbors = 2
21:29:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b2c2291
21:29:22 Searching Annoy index using 1 thread, search_k = 200
21:29:22 Annoy recall = 100%
21:29:23 Commencing smooth kNN distance calibration using 1 thread
21:29:23 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:23 Initializing from PCA
21:29:23 PCA: 2 components explained 21.2% variance
21:29:23 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:24 Optimization finished
[1] "2 0.17"21:29:25 UMAP embedding parameters a = 1.352 b = 0.9704
21:29:25 Read 1203 rows and found 38 numeric columns
21:29:25 Using Annoy for neighbor search, n_neighbors = 2
21:29:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ee4db67
21:29:25 Searching Annoy index using 1 thread, search_k = 200
21:29:25 Annoy recall = 100%
21:29:25 Commencing smooth kNN distance calibration using 1 thread
21:29:26 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:26 Initializing from PCA
21:29:26 PCA: 2 components explained 21.2% variance
21:29:26 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:27 Optimization finished
[1] "2 0.18"21:29:27 UMAP embedding parameters a = 1.321 b = 0.9813
21:29:27 Read 1203 rows and found 38 numeric columns
21:29:27 Using Annoy for neighbor search, n_neighbors = 2
21:29:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87538d43c2
21:29:27 Searching Annoy index using 1 thread, search_k = 200
21:29:27 Annoy recall = 100%
21:29:28 Commencing smooth kNN distance calibration using 1 thread
21:29:28 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:28 Initializing from PCA
21:29:28 PCA: 2 components explained 21.2% variance
21:29:28 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:29 Optimization finished
[1] "2 0.19"21:29:30 UMAP embedding parameters a = 1.292 b = 0.9921
21:29:30 Read 1203 rows and found 38 numeric columns
21:29:30 Using Annoy for neighbor search, n_neighbors = 2
21:29:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769af16b4
21:29:30 Searching Annoy index using 1 thread, search_k = 200
21:29:30 Annoy recall = 100%
21:29:30 Commencing smooth kNN distance calibration using 1 thread
21:29:31 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:31 Initializing from PCA
21:29:31 PCA: 2 components explained 21.2% variance
21:29:31 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:32 Optimization finished
[1] "2 0.2"21:29:32 UMAP embedding parameters a = 1.262 b = 1.003
21:29:32 Read 1203 rows and found 38 numeric columns
21:29:32 Using Annoy for neighbor search, n_neighbors = 2
21:29:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e605225
21:29:32 Searching Annoy index using 1 thread, search_k = 200
21:29:32 Annoy recall = 100%
21:29:33 Commencing smooth kNN distance calibration using 1 thread
21:29:34 Found 228 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:34 Initializing from PCA
21:29:34 PCA: 2 components explained 21.2% variance
21:29:34 Commencing optimization for 500 epochs, with 1950 positive edges
21:29:35 Optimization finished
[1] "3 0"21:29:35 UMAP embedding parameters a = 1.933 b = 0.7905
21:29:35 Read 1203 rows and found 38 numeric columns
21:29:35 Using Annoy for neighbor search, n_neighbors = 3
21:29:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a0560e4
21:29:35 Searching Annoy index using 1 thread, search_k = 300
21:29:35 Annoy recall = 100%
21:29:36 Commencing smooth kNN distance calibration using 1 thread
21:29:37 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:37 Initializing from PCA
21:29:37 PCA: 2 components explained 21.2% variance
21:29:37 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:38 Optimization finished
[1] "3 0.01"21:29:38 UMAP embedding parameters a = 1.896 b = 0.8006
21:29:38 Read 1203 rows and found 38 numeric columns
21:29:38 Using Annoy for neighbor search, n_neighbors = 3
21:29:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736a534dd
21:29:38 Searching Annoy index using 1 thread, search_k = 300
21:29:38 Annoy recall = 100%
21:29:39 Commencing smooth kNN distance calibration using 1 thread
21:29:39 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:39 Initializing from PCA
21:29:39 PCA: 2 components explained 21.2% variance
21:29:39 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:41 Optimization finished
[1] "3 0.02"21:29:41 UMAP embedding parameters a = 1.859 b = 0.8109
21:29:41 Read 1203 rows and found 38 numeric columns
21:29:41 Using Annoy for neighbor search, n_neighbors = 3
21:29:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f1cd95b
21:29:41 Searching Annoy index using 1 thread, search_k = 300
21:29:41 Annoy recall = 100%
21:29:42 Commencing smooth kNN distance calibration using 1 thread
21:29:43 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:43 Initializing from PCA
21:29:43 PCA: 2 components explained 21.2% variance
21:29:43 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:44 Optimization finished
[1] "3 0.03"21:29:44 UMAP embedding parameters a = 1.822 b = 0.8212
21:29:44 Read 1203 rows and found 38 numeric columns
21:29:44 Using Annoy for neighbor search, n_neighbors = 3
21:29:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728b0199
21:29:44 Searching Annoy index using 1 thread, search_k = 300
21:29:44 Annoy recall = 100%
21:29:45 Commencing smooth kNN distance calibration using 1 thread
21:29:45 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:45 Initializing from PCA
21:29:45 PCA: 2 components explained 21.2% variance
21:29:45 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:46 Optimization finished
[1] "3 0.04"21:29:47 UMAP embedding parameters a = 1.786 b = 0.8316
21:29:47 Read 1203 rows and found 38 numeric columns
21:29:47 Using Annoy for neighbor search, n_neighbors = 3
21:29:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749a1eb15
21:29:47 Searching Annoy index using 1 thread, search_k = 300
21:29:47 Annoy recall = 100%
21:29:47 Commencing smooth kNN distance calibration using 1 thread
21:29:48 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:48 Initializing from PCA
21:29:48 PCA: 2 components explained 21.2% variance
21:29:48 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:49 Optimization finished
[1] "3 0.05"21:29:49 UMAP embedding parameters a = 1.75 b = 0.8421
21:29:49 Read 1203 rows and found 38 numeric columns
21:29:49 Using Annoy for neighbor search, n_neighbors = 3
21:29:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876210edd0
21:29:50 Searching Annoy index using 1 thread, search_k = 300
21:29:50 Annoy recall = 100%
21:29:50 Commencing smooth kNN distance calibration using 1 thread
21:29:51 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:51 Initializing from PCA
21:29:51 PCA: 2 components explained 21.2% variance
21:29:51 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:52 Optimization finished
[1] "3 0.06"21:29:52 UMAP embedding parameters a = 1.715 b = 0.8526
21:29:52 Read 1203 rows and found 38 numeric columns
21:29:52 Using Annoy for neighbor search, n_neighbors = 3
21:29:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723c07291
21:29:52 Searching Annoy index using 1 thread, search_k = 300
21:29:53 Annoy recall = 100%
21:29:53 Commencing smooth kNN distance calibration using 1 thread
21:29:54 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:54 Initializing from PCA
21:29:54 PCA: 2 components explained 21.2% variance
21:29:54 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:55 Optimization finished
[1] "3 0.07"21:29:55 UMAP embedding parameters a = 1.68 b = 0.8631
21:29:55 Read 1203 rows and found 38 numeric columns
21:29:55 Using Annoy for neighbor search, n_neighbors = 3
21:29:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715d47c5d
21:29:55 Searching Annoy index using 1 thread, search_k = 300
21:29:55 Annoy recall = 100%
21:29:56 Commencing smooth kNN distance calibration using 1 thread
21:29:56 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:56 Initializing from PCA
21:29:56 PCA: 2 components explained 21.2% variance
21:29:56 Commencing optimization for 500 epochs, with 3664 positive edges
21:29:57 Optimization finished
[1] "3 0.08"21:29:58 UMAP embedding parameters a = 1.645 b = 0.8737
21:29:58 Read 1203 rows and found 38 numeric columns
21:29:58 Using Annoy for neighbor search, n_neighbors = 3
21:29:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87536bbfeb
21:29:58 Searching Annoy index using 1 thread, search_k = 300
21:29:58 Annoy recall = 100%
21:29:58 Commencing smooth kNN distance calibration using 1 thread
21:29:59 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:29:59 Initializing from PCA
21:29:59 PCA: 2 components explained 21.2% variance
21:29:59 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:00 Optimization finished
[1] "3 0.09"21:30:00 UMAP embedding parameters a = 1.611 b = 0.8844
21:30:00 Read 1203 rows and found 38 numeric columns
21:30:00 Using Annoy for neighbor search, n_neighbors = 3
21:30:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f4044f5
21:30:01 Searching Annoy index using 1 thread, search_k = 300
21:30:01 Annoy recall = 100%
21:30:01 Commencing smooth kNN distance calibration using 1 thread
21:30:02 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:02 Initializing from PCA
21:30:02 PCA: 2 components explained 21.2% variance
21:30:02 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:03 Optimization finished
[1] "3 0.1"21:30:03 UMAP embedding parameters a = 1.577 b = 0.8951
21:30:03 Read 1203 rows and found 38 numeric columns
21:30:03 Using Annoy for neighbor search, n_neighbors = 3
21:30:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f055d59
21:30:03 Searching Annoy index using 1 thread, search_k = 300
21:30:03 Annoy recall = 100%
21:30:04 Commencing smooth kNN distance calibration using 1 thread
21:30:05 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:05 Initializing from PCA
21:30:05 PCA: 2 components explained 21.2% variance
21:30:05 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:06 Optimization finished
[1] "3 0.11"21:30:06 UMAP embedding parameters a = 1.544 b = 0.9058
21:30:06 Read 1203 rows and found 38 numeric columns
21:30:06 Using Annoy for neighbor search, n_neighbors = 3
21:30:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e1c1e08
21:30:06 Searching Annoy index using 1 thread, search_k = 300
21:30:06 Annoy recall = 100%
21:30:07 Commencing smooth kNN distance calibration using 1 thread
21:30:07 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:07 Initializing from PCA
21:30:07 PCA: 2 components explained 21.2% variance
21:30:07 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:09 Optimization finished
[1] "3 0.12"21:30:09 UMAP embedding parameters a = 1.51 b = 0.9165
21:30:09 Read 1203 rows and found 38 numeric columns
21:30:09 Using Annoy for neighbor search, n_neighbors = 3
21:30:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ef50eb0
21:30:09 Searching Annoy index using 1 thread, search_k = 300
21:30:09 Annoy recall = 100%
21:30:09 Commencing smooth kNN distance calibration using 1 thread
21:30:10 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:10 Initializing from PCA
21:30:10 PCA: 2 components explained 21.2% variance
21:30:10 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:11 Optimization finished
[1] "3 0.13"21:30:11 UMAP embedding parameters a = 1.478 b = 0.9272
21:30:12 Read 1203 rows and found 38 numeric columns
21:30:12 Using Annoy for neighbor search, n_neighbors = 3
21:30:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f3db031
21:30:12 Searching Annoy index using 1 thread, search_k = 300
21:30:12 Annoy recall = 100%
21:30:12 Commencing smooth kNN distance calibration using 1 thread
21:30:13 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:13 Initializing from PCA
21:30:13 PCA: 2 components explained 21.2% variance
21:30:13 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:14 Optimization finished
[1] "3 0.14"21:30:14 UMAP embedding parameters a = 1.446 b = 0.938
21:30:14 Read 1203 rows and found 38 numeric columns
21:30:14 Using Annoy for neighbor search, n_neighbors = 3
21:30:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778380978
21:30:15 Searching Annoy index using 1 thread, search_k = 300
21:30:15 Annoy recall = 100%
21:30:15 Commencing smooth kNN distance calibration using 1 thread
21:30:16 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:16 Initializing from PCA
21:30:16 PCA: 2 components explained 21.2% variance
21:30:16 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:17 Optimization finished
[1] "3 0.15"21:30:17 UMAP embedding parameters a = 1.414 b = 0.9488
21:30:17 Read 1203 rows and found 38 numeric columns
21:30:17 Using Annoy for neighbor search, n_neighbors = 3
21:30:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712a6b50c
21:30:17 Searching Annoy index using 1 thread, search_k = 300
21:30:17 Annoy recall = 100%
21:30:18 Commencing smooth kNN distance calibration using 1 thread
21:30:19 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:19 Initializing from PCA
21:30:19 PCA: 2 components explained 21.2% variance
21:30:19 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:20 Optimization finished
[1] "3 0.16"21:30:20 UMAP embedding parameters a = 1.383 b = 0.9596
21:30:20 Read 1203 rows and found 38 numeric columns
21:30:20 Using Annoy for neighbor search, n_neighbors = 3
21:30:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755d117e5
21:30:20 Searching Annoy index using 1 thread, search_k = 300
21:30:20 Annoy recall = 100%
21:30:21 Commencing smooth kNN distance calibration using 1 thread
21:30:21 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:21 Initializing from PCA
21:30:22 PCA: 2 components explained 21.2% variance
21:30:22 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:23 Optimization finished
[1] "3 0.17"21:30:23 UMAP embedding parameters a = 1.352 b = 0.9704
21:30:23 Read 1203 rows and found 38 numeric columns
21:30:23 Using Annoy for neighbor search, n_neighbors = 3
21:30:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a94d555
21:30:23 Searching Annoy index using 1 thread, search_k = 300
21:30:23 Annoy recall = 100%
21:30:24 Commencing smooth kNN distance calibration using 1 thread
21:30:24 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:24 Initializing from PCA
21:30:24 PCA: 2 components explained 21.2% variance
21:30:24 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:26 Optimization finished
[1] "3 0.18"21:30:26 UMAP embedding parameters a = 1.321 b = 0.9813
21:30:26 Read 1203 rows and found 38 numeric columns
21:30:26 Using Annoy for neighbor search, n_neighbors = 3
21:30:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87586d2786
21:30:26 Searching Annoy index using 1 thread, search_k = 300
21:30:26 Annoy recall = 100%
21:30:26 Commencing smooth kNN distance calibration using 1 thread
21:30:27 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:27 Initializing from PCA
21:30:27 PCA: 2 components explained 21.2% variance
21:30:27 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:29 Optimization finished
[1] "3 0.19"21:30:29 UMAP embedding parameters a = 1.292 b = 0.9921
21:30:29 Read 1203 rows and found 38 numeric columns
21:30:29 Using Annoy for neighbor search, n_neighbors = 3
21:30:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e3e567
21:30:29 Searching Annoy index using 1 thread, search_k = 300
21:30:29 Annoy recall = 100%
21:30:30 Commencing smooth kNN distance calibration using 1 thread
21:30:30 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:30 Initializing from PCA
21:30:30 PCA: 2 components explained 21.2% variance
21:30:30 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:32 Optimization finished
[1] "3 0.2"21:30:32 UMAP embedding parameters a = 1.262 b = 1.003
21:30:32 Read 1203 rows and found 38 numeric columns
21:30:32 Using Annoy for neighbor search, n_neighbors = 3
21:30:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87db7db56
21:30:32 Searching Annoy index using 1 thread, search_k = 300
21:30:32 Annoy recall = 100%
21:30:32 Commencing smooth kNN distance calibration using 1 thread
21:30:33 Found 2 connected components, falling back to 'spca' initialization with init_sdev = 1
21:30:33 Initializing from PCA
21:30:33 PCA: 2 components explained 21.2% variance
21:30:33 Commencing optimization for 500 epochs, with 3664 positive edges
21:30:34 Optimization finished
[1] "4 0"21:30:35 UMAP embedding parameters a = 1.933 b = 0.7905
21:30:35 Read 1203 rows and found 38 numeric columns
21:30:35 Using Annoy for neighbor search, n_neighbors = 4
21:30:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769be05fa
21:30:35 Searching Annoy index using 1 thread, search_k = 400
21:30:35 Annoy recall = 100%
21:30:35 Commencing smooth kNN distance calibration using 1 thread
21:30:36 Initializing from normalized Laplacian + noise
21:30:36 Commencing optimization for 500 epochs, with 5364 positive edges
21:30:38 Optimization finished
[1] "4 0.01"21:30:38 UMAP embedding parameters a = 1.896 b = 0.8006
21:30:38 Read 1203 rows and found 38 numeric columns
21:30:38 Using Annoy for neighbor search, n_neighbors = 4
21:30:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87652918ae
21:30:38 Searching Annoy index using 1 thread, search_k = 400
21:30:38 Annoy recall = 100%
21:30:38 Commencing smooth kNN distance calibration using 1 thread
21:30:39 Initializing from normalized Laplacian + noise
21:30:39 Commencing optimization for 500 epochs, with 5364 positive edges
21:30:41 Optimization finished
[1] "4 0.02"21:30:41 UMAP embedding parameters a = 1.859 b = 0.8109
21:30:41 Read 1203 rows and found 38 numeric columns
21:30:41 Using Annoy for neighbor search, n_neighbors = 4
21:30:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724ac807a
21:30:41 Searching Annoy index using 1 thread, search_k = 400
21:30:41 Annoy recall = 100%
21:30:42 Commencing smooth kNN distance calibration using 1 thread
21:30:42 Initializing from normalized Laplacian + noise
21:30:42 Commencing optimization for 500 epochs, with 5364 positive edges
21:30:44 Optimization finished
[1] "4 0.03"21:30:44 UMAP embedding parameters a = 1.822 b = 0.8212
21:30:44 Read 1203 rows and found 38 numeric columns
21:30:44 Using Annoy for neighbor search, n_neighbors = 4
21:30:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747e7a7f1
21:30:44 Searching Annoy index using 1 thread, search_k = 400
21:30:44 Annoy recall = 100%
21:30:45 Commencing smooth kNN distance calibration using 1 thread
21:30:46 Initializing from normalized Laplacian + noise
21:30:46 Commencing optimization for 500 epochs, with 5364 positive edges
21:30:47 Optimization finished
[1] "4 0.04"21:30:47 UMAP embedding parameters a = 1.786 b = 0.8316
21:30:47 Read 1203 rows and found 38 numeric columns
21:30:47 Using Annoy for neighbor search, n_neighbors = 4
21:30:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87483fac24
21:30:47 Searching Annoy index using 1 thread, search_k = 400
21:30:47 Annoy recall = 100%
21:30:48 Commencing smooth kNN distance calibration using 1 thread
21:30:49 Initializing from normalized Laplacian + noise
21:30:49 Commencing optimization for 500 epochs, with 5364 positive edges
21:30:50 Optimization finished
[1] "4 0.05"21:30:50 UMAP embedding parameters a = 1.75 b = 0.8421
21:30:50 Read 1203 rows and found 38 numeric columns
21:30:50 Using Annoy for neighbor search, n_neighbors = 4
21:30:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fd8a30b
21:30:51 Searching Annoy index using 1 thread, search_k = 400
21:30:51 Annoy recall = 100%
21:30:51 Commencing smooth kNN distance calibration using 1 thread
21:30:52 Initializing from normalized Laplacian + noise
21:30:52 Commencing optimization for 500 epochs, with 5364 positive edges
21:30:53 Optimization finished
[1] "4 0.06"21:30:54 UMAP embedding parameters a = 1.715 b = 0.8526
21:30:54 Read 1203 rows and found 38 numeric columns
21:30:54 Using Annoy for neighbor search, n_neighbors = 4
21:30:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cc8358
21:30:54 Searching Annoy index using 1 thread, search_k = 400
21:30:54 Annoy recall = 100%
21:30:54 Commencing smooth kNN distance calibration using 1 thread
21:30:55 Initializing from normalized Laplacian + noise
21:30:55 Commencing optimization for 500 epochs, with 5364 positive edges
21:30:57 Optimization finished
[1] "4 0.07"21:30:57 UMAP embedding parameters a = 1.68 b = 0.8631
21:30:57 Read 1203 rows and found 38 numeric columns
21:30:57 Using Annoy for neighbor search, n_neighbors = 4
21:30:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871bccefe7
21:30:57 Searching Annoy index using 1 thread, search_k = 400
21:30:57 Annoy recall = 100%
21:30:58 Commencing smooth kNN distance calibration using 1 thread
21:30:58 Initializing from normalized Laplacian + noise
21:30:58 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:00 Optimization finished
[1] "4 0.08"21:31:00 UMAP embedding parameters a = 1.645 b = 0.8737
21:31:00 Read 1203 rows and found 38 numeric columns
21:31:00 Using Annoy for neighbor search, n_neighbors = 4
21:31:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872987b9bf
21:31:00 Searching Annoy index using 1 thread, search_k = 400
21:31:00 Annoy recall = 100%
21:31:01 Commencing smooth kNN distance calibration using 1 thread
21:31:02 Initializing from normalized Laplacian + noise
21:31:02 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:03 Optimization finished
[1] "4 0.09"21:31:03 UMAP embedding parameters a = 1.611 b = 0.8844
21:31:03 Read 1203 rows and found 38 numeric columns
21:31:03 Using Annoy for neighbor search, n_neighbors = 4
21:31:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87752cd57d
21:31:04 Searching Annoy index using 1 thread, search_k = 400
21:31:04 Annoy recall = 100%
21:31:04 Commencing smooth kNN distance calibration using 1 thread
21:31:05 Initializing from normalized Laplacian + noise
21:31:05 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:06 Optimization finished
[1] "4 0.1"21:31:07 UMAP embedding parameters a = 1.577 b = 0.8951
21:31:07 Read 1203 rows and found 38 numeric columns
21:31:07 Using Annoy for neighbor search, n_neighbors = 4
21:31:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745d250cb
21:31:07 Searching Annoy index using 1 thread, search_k = 400
21:31:07 Annoy recall = 100%
21:31:07 Commencing smooth kNN distance calibration using 1 thread
21:31:08 Initializing from normalized Laplacian + noise
21:31:08 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:10 Optimization finished
[1] "4 0.11"21:31:10 UMAP embedding parameters a = 1.544 b = 0.9058
21:31:10 Read 1203 rows and found 38 numeric columns
21:31:10 Using Annoy for neighbor search, n_neighbors = 4
21:31:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87602cee9d
21:31:10 Searching Annoy index using 1 thread, search_k = 400
21:31:10 Annoy recall = 100%
21:31:11 Commencing smooth kNN distance calibration using 1 thread
21:31:12 Initializing from normalized Laplacian + noise
21:31:12 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:13 Optimization finished
[1] "4 0.12"21:31:13 UMAP embedding parameters a = 1.51 b = 0.9165
21:31:13 Read 1203 rows and found 38 numeric columns
21:31:13 Using Annoy for neighbor search, n_neighbors = 4
21:31:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875449aed8
21:31:13 Searching Annoy index using 1 thread, search_k = 400
21:31:13 Annoy recall = 100%
21:31:14 Commencing smooth kNN distance calibration using 1 thread
21:31:15 Initializing from normalized Laplacian + noise
21:31:15 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:16 Optimization finished
[1] "4 0.13"21:31:16 UMAP embedding parameters a = 1.478 b = 0.9272
21:31:16 Read 1203 rows and found 38 numeric columns
21:31:16 Using Annoy for neighbor search, n_neighbors = 4
21:31:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87485d5264
21:31:17 Searching Annoy index using 1 thread, search_k = 400
21:31:17 Annoy recall = 100%
21:31:17 Commencing smooth kNN distance calibration using 1 thread
21:31:18 Initializing from normalized Laplacian + noise
21:31:18 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:20 Optimization finished
[1] "4 0.14"21:31:20 UMAP embedding parameters a = 1.446 b = 0.938
21:31:20 Read 1203 rows and found 38 numeric columns
21:31:20 Using Annoy for neighbor search, n_neighbors = 4
21:31:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729ced9b2
21:31:20 Searching Annoy index using 1 thread, search_k = 400
21:31:20 Annoy recall = 100%
21:31:21 Commencing smooth kNN distance calibration using 1 thread
21:31:21 Initializing from normalized Laplacian + noise
21:31:22 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:23 Optimization finished
[1] "4 0.15"21:31:23 UMAP embedding parameters a = 1.414 b = 0.9488
21:31:23 Read 1203 rows and found 38 numeric columns
21:31:23 Using Annoy for neighbor search, n_neighbors = 4
21:31:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87365a9ca8
21:31:23 Searching Annoy index using 1 thread, search_k = 400
21:31:23 Annoy recall = 100%
21:31:24 Commencing smooth kNN distance calibration using 1 thread
21:31:25 Initializing from normalized Laplacian + noise
21:31:25 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:26 Optimization finished
[1] "4 0.16"21:31:26 UMAP embedding parameters a = 1.383 b = 0.9596
21:31:26 Read 1203 rows and found 38 numeric columns
21:31:26 Using Annoy for neighbor search, n_neighbors = 4
21:31:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c1dc4f5
21:31:27 Searching Annoy index using 1 thread, search_k = 400
21:31:27 Annoy recall = 100%
21:31:27 Commencing smooth kNN distance calibration using 1 thread
21:31:28 Initializing from normalized Laplacian + noise
21:31:28 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:30 Optimization finished
[1] "4 0.17"21:31:30 UMAP embedding parameters a = 1.352 b = 0.9704
21:31:30 Read 1203 rows and found 38 numeric columns
21:31:30 Using Annoy for neighbor search, n_neighbors = 4
21:31:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fa3560f
21:31:30 Searching Annoy index using 1 thread, search_k = 400
21:31:30 Annoy recall = 100%
21:31:31 Commencing smooth kNN distance calibration using 1 thread
21:31:32 Initializing from normalized Laplacian + noise
21:31:32 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:33 Optimization finished
[1] "4 0.18"21:31:33 UMAP embedding parameters a = 1.321 b = 0.9813
21:31:33 Read 1203 rows and found 38 numeric columns
21:31:33 Using Annoy for neighbor search, n_neighbors = 4
21:31:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879c65c93
21:31:33 Searching Annoy index using 1 thread, search_k = 400
21:31:33 Annoy recall = 100%
21:31:34 Commencing smooth kNN distance calibration using 1 thread
21:31:35 Initializing from normalized Laplacian + noise
21:31:35 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:36 Optimization finished
[1] "4 0.19"21:31:37 UMAP embedding parameters a = 1.292 b = 0.9921
21:31:37 Read 1203 rows and found 38 numeric columns
21:31:37 Using Annoy for neighbor search, n_neighbors = 4
21:31:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b5e09ea
21:31:37 Searching Annoy index using 1 thread, search_k = 400
21:31:37 Annoy recall = 100%
21:31:37 Commencing smooth kNN distance calibration using 1 thread
21:31:38 Initializing from normalized Laplacian + noise
21:31:38 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:40 Optimization finished
[1] "4 0.2"21:31:40 UMAP embedding parameters a = 1.262 b = 1.003
21:31:40 Read 1203 rows and found 38 numeric columns
21:31:40 Using Annoy for neighbor search, n_neighbors = 4
21:31:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ea8b369
21:31:40 Searching Annoy index using 1 thread, search_k = 400
21:31:40 Annoy recall = 100%
21:31:41 Commencing smooth kNN distance calibration using 1 thread
21:31:42 Initializing from normalized Laplacian + noise
21:31:42 Commencing optimization for 500 epochs, with 5364 positive edges
21:31:43 Optimization finished
[1] "5 0"21:31:43 UMAP embedding parameters a = 1.933 b = 0.7905
21:31:43 Read 1203 rows and found 38 numeric columns
21:31:43 Using Annoy for neighbor search, n_neighbors = 5
21:31:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767e27a9b
21:31:44 Searching Annoy index using 1 thread, search_k = 500
21:31:44 Annoy recall = 100%
21:31:44 Commencing smooth kNN distance calibration using 1 thread
21:31:45 Initializing from normalized Laplacian + noise
21:31:45 Commencing optimization for 500 epochs, with 6968 positive edges
21:31:47 Optimization finished
[1] "5 0.01"21:31:47 UMAP embedding parameters a = 1.896 b = 0.8006
21:31:47 Read 1203 rows and found 38 numeric columns
21:31:47 Using Annoy for neighbor search, n_neighbors = 5
21:31:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a53189a
21:31:47 Searching Annoy index using 1 thread, search_k = 500
21:31:47 Annoy recall = 100%
21:31:48 Commencing smooth kNN distance calibration using 1 thread
21:31:49 Initializing from normalized Laplacian + noise
21:31:49 Commencing optimization for 500 epochs, with 6968 positive edges
21:31:50 Optimization finished
[1] "5 0.02"21:31:51 UMAP embedding parameters a = 1.859 b = 0.8109
21:31:51 Read 1203 rows and found 38 numeric columns
21:31:51 Using Annoy for neighbor search, n_neighbors = 5
21:31:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87de6639a
21:31:51 Searching Annoy index using 1 thread, search_k = 500
21:31:51 Annoy recall = 100%
21:31:51 Commencing smooth kNN distance calibration using 1 thread
21:31:52 Initializing from normalized Laplacian + noise
21:31:52 Commencing optimization for 500 epochs, with 6968 positive edges
21:31:54 Optimization finished
[1] "5 0.03"21:31:54 UMAP embedding parameters a = 1.822 b = 0.8212
21:31:54 Read 1203 rows and found 38 numeric columns
21:31:54 Using Annoy for neighbor search, n_neighbors = 5
21:31:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87601a8413
21:31:54 Searching Annoy index using 1 thread, search_k = 500
21:31:55 Annoy recall = 100%
21:31:55 Commencing smooth kNN distance calibration using 1 thread
21:31:56 Initializing from normalized Laplacian + noise
21:31:56 Commencing optimization for 500 epochs, with 6968 positive edges
21:31:58 Optimization finished
[1] "5 0.04"21:31:58 UMAP embedding parameters a = 1.786 b = 0.8316
21:31:58 Read 1203 rows and found 38 numeric columns
21:31:58 Using Annoy for neighbor search, n_neighbors = 5
21:31:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877cf9cda6
21:31:58 Searching Annoy index using 1 thread, search_k = 500
21:31:58 Annoy recall = 100%
21:31:59 Commencing smooth kNN distance calibration using 1 thread
21:32:00 Initializing from normalized Laplacian + noise
21:32:00 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:01 Optimization finished
[1] "5 0.05"21:32:02 UMAP embedding parameters a = 1.75 b = 0.8421
21:32:02 Read 1203 rows and found 38 numeric columns
21:32:02 Using Annoy for neighbor search, n_neighbors = 5
21:32:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763b77b7f
21:32:02 Searching Annoy index using 1 thread, search_k = 500
21:32:02 Annoy recall = 100%
21:32:02 Commencing smooth kNN distance calibration using 1 thread
21:32:03 Initializing from normalized Laplacian + noise
21:32:03 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:05 Optimization finished
[1] "5 0.06"21:32:05 UMAP embedding parameters a = 1.715 b = 0.8526
21:32:05 Read 1203 rows and found 38 numeric columns
21:32:05 Using Annoy for neighbor search, n_neighbors = 5
21:32:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877aaf5968
21:32:05 Searching Annoy index using 1 thread, search_k = 500
21:32:05 Annoy recall = 100%
21:32:06 Commencing smooth kNN distance calibration using 1 thread
21:32:07 Initializing from normalized Laplacian + noise
21:32:07 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:09 Optimization finished
[1] "5 0.07"21:32:09 UMAP embedding parameters a = 1.68 b = 0.8631
21:32:09 Read 1203 rows and found 38 numeric columns
21:32:09 Using Annoy for neighbor search, n_neighbors = 5
21:32:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875566f52d
21:32:09 Searching Annoy index using 1 thread, search_k = 500
21:32:09 Annoy recall = 100%
21:32:10 Commencing smooth kNN distance calibration using 1 thread
21:32:11 Initializing from normalized Laplacian + noise
21:32:11 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:12 Optimization finished
[1] "5 0.08"21:32:13 UMAP embedding parameters a = 1.645 b = 0.8737
21:32:13 Read 1203 rows and found 38 numeric columns
21:32:13 Using Annoy for neighbor search, n_neighbors = 5
21:32:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87649b60e6
21:32:13 Searching Annoy index using 1 thread, search_k = 500
21:32:13 Annoy recall = 100%
21:32:13 Commencing smooth kNN distance calibration using 1 thread
21:32:14 Initializing from normalized Laplacian + noise
21:32:14 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:16 Optimization finished
[1] "5 0.09"21:32:16 UMAP embedding parameters a = 1.611 b = 0.8844
21:32:16 Read 1203 rows and found 38 numeric columns
21:32:16 Using Annoy for neighbor search, n_neighbors = 5
21:32:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8786734bf
21:32:16 Searching Annoy index using 1 thread, search_k = 500
21:32:17 Annoy recall = 100%
21:32:17 Commencing smooth kNN distance calibration using 1 thread
21:32:18 Initializing from normalized Laplacian + noise
21:32:18 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:20 Optimization finished
[1] "5 0.1"21:32:20 UMAP embedding parameters a = 1.577 b = 0.8951
21:32:20 Read 1203 rows and found 38 numeric columns
21:32:20 Using Annoy for neighbor search, n_neighbors = 5
21:32:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f24fb27
21:32:20 Searching Annoy index using 1 thread, search_k = 500
21:32:20 Annoy recall = 100%
21:32:21 Commencing smooth kNN distance calibration using 1 thread
21:32:22 Initializing from normalized Laplacian + noise
21:32:22 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:23 Optimization finished
[1] "5 0.11"21:32:24 UMAP embedding parameters a = 1.544 b = 0.9058
21:32:24 Read 1203 rows and found 38 numeric columns
21:32:24 Using Annoy for neighbor search, n_neighbors = 5
21:32:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749c47994
21:32:24 Searching Annoy index using 1 thread, search_k = 500
21:32:24 Annoy recall = 100%
21:32:25 Commencing smooth kNN distance calibration using 1 thread
21:32:26 Initializing from normalized Laplacian + noise
21:32:26 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:27 Optimization finished
[1] "5 0.12"21:32:27 UMAP embedding parameters a = 1.51 b = 0.9165
21:32:27 Read 1203 rows and found 38 numeric columns
21:32:27 Using Annoy for neighbor search, n_neighbors = 5
21:32:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d13b539
21:32:28 Searching Annoy index using 1 thread, search_k = 500
21:32:28 Annoy recall = 100%
21:32:28 Commencing smooth kNN distance calibration using 1 thread
21:32:29 Initializing from normalized Laplacian + noise
21:32:29 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:31 Optimization finished
[1] "5 0.13"21:32:31 UMAP embedding parameters a = 1.478 b = 0.9272
21:32:31 Read 1203 rows and found 38 numeric columns
21:32:31 Using Annoy for neighbor search, n_neighbors = 5
21:32:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770ca318
21:32:31 Searching Annoy index using 1 thread, search_k = 500
21:32:32 Annoy recall = 100%
21:32:32 Commencing smooth kNN distance calibration using 1 thread
21:32:33 Initializing from normalized Laplacian + noise
21:32:33 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:35 Optimization finished
[1] "5 0.14"21:32:35 UMAP embedding parameters a = 1.446 b = 0.938
21:32:35 Read 1203 rows and found 38 numeric columns
21:32:35 Using Annoy for neighbor search, n_neighbors = 5
21:32:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87120425b9
21:32:35 Searching Annoy index using 1 thread, search_k = 500
21:32:35 Annoy recall = 100%
21:32:36 Commencing smooth kNN distance calibration using 1 thread
21:32:37 Initializing from normalized Laplacian + noise
21:32:37 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:39 Optimization finished
[1] "5 0.15"21:32:39 UMAP embedding parameters a = 1.414 b = 0.9488
21:32:39 Read 1203 rows and found 38 numeric columns
21:32:39 Using Annoy for neighbor search, n_neighbors = 5
21:32:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cec5844
21:32:39 Searching Annoy index using 1 thread, search_k = 500
21:32:39 Annoy recall = 100%
21:32:40 Commencing smooth kNN distance calibration using 1 thread
21:32:41 Initializing from normalized Laplacian + noise
21:32:41 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:42 Optimization finished
[1] "5 0.16"21:32:43 UMAP embedding parameters a = 1.383 b = 0.9596
21:32:43 Read 1203 rows and found 38 numeric columns
21:32:43 Using Annoy for neighbor search, n_neighbors = 5
21:32:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dd92670
21:32:43 Searching Annoy index using 1 thread, search_k = 500
21:32:43 Annoy recall = 100%
21:32:43 Commencing smooth kNN distance calibration using 1 thread
21:32:45 Initializing from normalized Laplacian + noise
21:32:45 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:46 Optimization finished
[1] "5 0.17"21:32:46 UMAP embedding parameters a = 1.352 b = 0.9704
21:32:46 Read 1203 rows and found 38 numeric columns
21:32:46 Using Annoy for neighbor search, n_neighbors = 5
21:32:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dd115a0
21:32:47 Searching Annoy index using 1 thread, search_k = 500
21:32:47 Annoy recall = 100%
21:32:47 Commencing smooth kNN distance calibration using 1 thread
21:32:48 Initializing from normalized Laplacian + noise
21:32:48 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:50 Optimization finished
[1] "5 0.18"21:32:50 UMAP embedding parameters a = 1.321 b = 0.9813
21:32:50 Read 1203 rows and found 38 numeric columns
21:32:50 Using Annoy for neighbor search, n_neighbors = 5
21:32:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716741204
21:32:50 Searching Annoy index using 1 thread, search_k = 500
21:32:51 Annoy recall = 100%
21:32:51 Commencing smooth kNN distance calibration using 1 thread
21:32:52 Initializing from normalized Laplacian + noise
21:32:52 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:54 Optimization finished
[1] "5 0.19"21:32:54 UMAP embedding parameters a = 1.292 b = 0.9921
21:32:54 Read 1203 rows and found 38 numeric columns
21:32:54 Using Annoy for neighbor search, n_neighbors = 5
21:32:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87305fbee
21:32:54 Searching Annoy index using 1 thread, search_k = 500
21:32:54 Annoy recall = 100%
21:32:55 Commencing smooth kNN distance calibration using 1 thread
21:32:56 Initializing from normalized Laplacian + noise
21:32:56 Commencing optimization for 500 epochs, with 6968 positive edges
21:32:58 Optimization finished
[1] "5 0.2"21:32:58 UMAP embedding parameters a = 1.262 b = 1.003
21:32:58 Read 1203 rows and found 38 numeric columns
21:32:58 Using Annoy for neighbor search, n_neighbors = 5
21:32:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773a3666b
21:32:58 Searching Annoy index using 1 thread, search_k = 500
21:32:58 Annoy recall = 100%
21:32:59 Commencing smooth kNN distance calibration using 1 thread
21:33:00 Initializing from normalized Laplacian + noise
21:33:00 Commencing optimization for 500 epochs, with 6968 positive edges
21:33:02 Optimization finished
[1] "6 0"21:33:02 UMAP embedding parameters a = 1.933 b = 0.7905
21:33:02 Read 1203 rows and found 38 numeric columns
21:33:02 Using Annoy for neighbor search, n_neighbors = 6
21:33:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776a100a1
21:33:02 Searching Annoy index using 1 thread, search_k = 600
21:33:02 Annoy recall = 100%
21:33:03 Commencing smooth kNN distance calibration using 1 thread
21:33:04 Initializing from normalized Laplacian + noise
21:33:04 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:06 Optimization finished
[1] "6 0.01"21:33:06 UMAP embedding parameters a = 1.896 b = 0.8006
21:33:06 Read 1203 rows and found 38 numeric columns
21:33:06 Using Annoy for neighbor search, n_neighbors = 6
21:33:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87574faac6
21:33:06 Searching Annoy index using 1 thread, search_k = 600
21:33:06 Annoy recall = 100%
21:33:07 Commencing smooth kNN distance calibration using 1 thread
21:33:08 Initializing from normalized Laplacian + noise
21:33:08 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:10 Optimization finished
[1] "6 0.02"21:33:10 UMAP embedding parameters a = 1.859 b = 0.8109
21:33:10 Read 1203 rows and found 38 numeric columns
21:33:10 Using Annoy for neighbor search, n_neighbors = 6
21:33:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c00b8cf
21:33:10 Searching Annoy index using 1 thread, search_k = 600
21:33:10 Annoy recall = 100%
21:33:11 Commencing smooth kNN distance calibration using 1 thread
21:33:12 Initializing from normalized Laplacian + noise
21:33:12 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:14 Optimization finished
[1] "6 0.03"21:33:14 UMAP embedding parameters a = 1.822 b = 0.8212
21:33:14 Read 1203 rows and found 38 numeric columns
21:33:14 Using Annoy for neighbor search, n_neighbors = 6
21:33:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87206fda53
21:33:14 Searching Annoy index using 1 thread, search_k = 600
21:33:14 Annoy recall = 100%
21:33:15 Commencing smooth kNN distance calibration using 1 thread
21:33:16 Initializing from normalized Laplacian + noise
21:33:16 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:18 Optimization finished
[1] "6 0.04"21:33:18 UMAP embedding parameters a = 1.786 b = 0.8316
21:33:18 Read 1203 rows and found 38 numeric columns
21:33:18 Using Annoy for neighbor search, n_neighbors = 6
21:33:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87daa476f
21:33:18 Searching Annoy index using 1 thread, search_k = 600
21:33:18 Annoy recall = 100%
21:33:19 Commencing smooth kNN distance calibration using 1 thread
21:33:20 Initializing from normalized Laplacian + noise
21:33:20 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:22 Optimization finished
[1] "6 0.05"21:33:22 UMAP embedding parameters a = 1.75 b = 0.8421
21:33:22 Read 1203 rows and found 38 numeric columns
21:33:22 Using Annoy for neighbor search, n_neighbors = 6
21:33:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87281e7dc4
21:33:22 Searching Annoy index using 1 thread, search_k = 600
21:33:22 Annoy recall = 100%
21:33:23 Commencing smooth kNN distance calibration using 1 thread
21:33:24 Initializing from normalized Laplacian + noise
21:33:24 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:26 Optimization finished
[1] "6 0.06"21:33:26 UMAP embedding parameters a = 1.715 b = 0.8526
21:33:26 Read 1203 rows and found 38 numeric columns
21:33:26 Using Annoy for neighbor search, n_neighbors = 6
21:33:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760133062
21:33:26 Searching Annoy index using 1 thread, search_k = 600
21:33:26 Annoy recall = 100%
21:33:27 Commencing smooth kNN distance calibration using 1 thread
21:33:28 Initializing from normalized Laplacian + noise
21:33:28 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:30 Optimization finished
[1] "6 0.07"21:33:30 UMAP embedding parameters a = 1.68 b = 0.8631
21:33:30 Read 1203 rows and found 38 numeric columns
21:33:30 Using Annoy for neighbor search, n_neighbors = 6
21:33:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871770a402
21:33:30 Searching Annoy index using 1 thread, search_k = 600
21:33:31 Annoy recall = 100%
21:33:31 Commencing smooth kNN distance calibration using 1 thread
21:33:32 Initializing from normalized Laplacian + noise
21:33:32 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:34 Optimization finished
[1] "6 0.08"21:33:34 UMAP embedding parameters a = 1.645 b = 0.8737
21:33:34 Read 1203 rows and found 38 numeric columns
21:33:34 Using Annoy for neighbor search, n_neighbors = 6
21:33:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87237c87ae
21:33:35 Searching Annoy index using 1 thread, search_k = 600
21:33:35 Annoy recall = 100%
21:33:35 Commencing smooth kNN distance calibration using 1 thread
21:33:36 Initializing from normalized Laplacian + noise
21:33:36 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:38 Optimization finished
[1] "6 0.09"21:33:38 UMAP embedding parameters a = 1.611 b = 0.8844
21:33:38 Read 1203 rows and found 38 numeric columns
21:33:38 Using Annoy for neighbor search, n_neighbors = 6
21:33:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ebbe3cb
21:33:39 Searching Annoy index using 1 thread, search_k = 600
21:33:39 Annoy recall = 100%
21:33:39 Commencing smooth kNN distance calibration using 1 thread
21:33:41 Initializing from normalized Laplacian + noise
21:33:41 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:42 Optimization finished
[1] "6 0.1"21:33:43 UMAP embedding parameters a = 1.577 b = 0.8951
21:33:43 Read 1203 rows and found 38 numeric columns
21:33:43 Using Annoy for neighbor search, n_neighbors = 6
21:33:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f531e9e
21:33:43 Searching Annoy index using 1 thread, search_k = 600
21:33:43 Annoy recall = 100%
21:33:44 Commencing smooth kNN distance calibration using 1 thread
21:33:45 Initializing from normalized Laplacian + noise
21:33:45 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:47 Optimization finished
[1] "6 0.11"21:33:47 UMAP embedding parameters a = 1.544 b = 0.9058
21:33:47 Read 1203 rows and found 38 numeric columns
21:33:47 Using Annoy for neighbor search, n_neighbors = 6
21:33:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dcfa048
21:33:47 Searching Annoy index using 1 thread, search_k = 600
21:33:47 Annoy recall = 100%
21:33:48 Commencing smooth kNN distance calibration using 1 thread
21:33:49 Initializing from normalized Laplacian + noise
21:33:49 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:51 Optimization finished
[1] "6 0.12"21:33:51 UMAP embedding parameters a = 1.51 b = 0.9165
21:33:51 Read 1203 rows and found 38 numeric columns
21:33:51 Using Annoy for neighbor search, n_neighbors = 6
21:33:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ca24765
21:33:51 Searching Annoy index using 1 thread, search_k = 600
21:33:51 Annoy recall = 100%
21:33:52 Commencing smooth kNN distance calibration using 1 thread
21:33:53 Initializing from normalized Laplacian + noise
21:33:53 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:55 Optimization finished
[1] "6 0.13"21:33:55 UMAP embedding parameters a = 1.478 b = 0.9272
21:33:55 Read 1203 rows and found 38 numeric columns
21:33:55 Using Annoy for neighbor search, n_neighbors = 6
21:33:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f6da2b1
21:33:55 Searching Annoy index using 1 thread, search_k = 600
21:33:55 Annoy recall = 100%
21:33:56 Commencing smooth kNN distance calibration using 1 thread
21:33:57 Initializing from normalized Laplacian + noise
21:33:57 Commencing optimization for 500 epochs, with 8570 positive edges
21:33:59 Optimization finished
[1] "6 0.14"21:33:59 UMAP embedding parameters a = 1.446 b = 0.938
21:33:59 Read 1203 rows and found 38 numeric columns
21:33:59 Using Annoy for neighbor search, n_neighbors = 6
21:33:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ac96def
21:34:00 Searching Annoy index using 1 thread, search_k = 600
21:34:00 Annoy recall = 100%
21:34:00 Commencing smooth kNN distance calibration using 1 thread
21:34:01 Initializing from normalized Laplacian + noise
21:34:01 Commencing optimization for 500 epochs, with 8570 positive edges
21:34:03 Optimization finished
[1] "6 0.15"21:34:04 UMAP embedding parameters a = 1.414 b = 0.9488
21:34:04 Read 1203 rows and found 38 numeric columns
21:34:04 Using Annoy for neighbor search, n_neighbors = 6
21:34:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873059c2e4
21:34:04 Searching Annoy index using 1 thread, search_k = 600
21:34:04 Annoy recall = 100%
21:34:04 Commencing smooth kNN distance calibration using 1 thread
21:34:06 Initializing from normalized Laplacian + noise
21:34:06 Commencing optimization for 500 epochs, with 8570 positive edges
21:34:08 Optimization finished
[1] "6 0.16"21:34:08 UMAP embedding parameters a = 1.383 b = 0.9596
21:34:08 Read 1203 rows and found 38 numeric columns
21:34:08 Using Annoy for neighbor search, n_neighbors = 6
21:34:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a1cfc1a
21:34:08 Searching Annoy index using 1 thread, search_k = 600
21:34:08 Annoy recall = 100%
21:34:09 Commencing smooth kNN distance calibration using 1 thread
21:34:10 Initializing from normalized Laplacian + noise
21:34:10 Commencing optimization for 500 epochs, with 8570 positive edges
21:34:12 Optimization finished
[1] "6 0.17"21:34:12 UMAP embedding parameters a = 1.352 b = 0.9704
21:34:12 Read 1203 rows and found 38 numeric columns
21:34:12 Using Annoy for neighbor search, n_neighbors = 6
21:34:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876030631c
21:34:12 Searching Annoy index using 1 thread, search_k = 600
21:34:12 Annoy recall = 100%
21:34:13 Commencing smooth kNN distance calibration using 1 thread
21:34:14 Initializing from normalized Laplacian + noise
21:34:14 Commencing optimization for 500 epochs, with 8570 positive edges
21:34:16 Optimization finished
[1] "6 0.18"21:34:16 UMAP embedding parameters a = 1.321 b = 0.9813
21:34:16 Read 1203 rows and found 38 numeric columns
21:34:16 Using Annoy for neighbor search, n_neighbors = 6
21:34:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714f523ca
21:34:16 Searching Annoy index using 1 thread, search_k = 600
21:34:17 Annoy recall = 100%
21:34:17 Commencing smooth kNN distance calibration using 1 thread
21:34:18 Initializing from normalized Laplacian + noise
21:34:18 Commencing optimization for 500 epochs, with 8570 positive edges
21:34:20 Optimization finished
[1] "6 0.19"21:34:21 UMAP embedding parameters a = 1.292 b = 0.9921
21:34:21 Read 1203 rows and found 38 numeric columns
21:34:21 Using Annoy for neighbor search, n_neighbors = 6
21:34:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87628430d9
21:34:21 Searching Annoy index using 1 thread, search_k = 600
21:34:21 Annoy recall = 100%
21:34:21 Commencing smooth kNN distance calibration using 1 thread
21:34:23 Initializing from normalized Laplacian + noise
21:34:23 Commencing optimization for 500 epochs, with 8570 positive edges
21:34:25 Optimization finished
[1] "6 0.2"21:34:25 UMAP embedding parameters a = 1.262 b = 1.003
21:34:25 Read 1203 rows and found 38 numeric columns
21:34:25 Using Annoy for neighbor search, n_neighbors = 6
21:34:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f555e43
21:34:25 Searching Annoy index using 1 thread, search_k = 600
21:34:25 Annoy recall = 100%
21:34:26 Commencing smooth kNN distance calibration using 1 thread
21:34:27 Initializing from normalized Laplacian + noise
21:34:27 Commencing optimization for 500 epochs, with 8570 positive edges
21:34:29 Optimization finished
[1] "7 0"21:34:29 UMAP embedding parameters a = 1.933 b = 0.7905
21:34:29 Read 1203 rows and found 38 numeric columns
21:34:29 Using Annoy for neighbor search, n_neighbors = 7
21:34:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875eb99d5f
21:34:29 Searching Annoy index using 1 thread, search_k = 700
21:34:29 Annoy recall = 100%
21:34:30 Commencing smooth kNN distance calibration using 1 thread
21:34:31 Initializing from normalized Laplacian + noise
21:34:31 Commencing optimization for 500 epochs, with 10170 positive edges
21:34:33 Optimization finished
[1] "7 0.01"21:34:34 UMAP embedding parameters a = 1.896 b = 0.8006
21:34:34 Read 1203 rows and found 38 numeric columns
21:34:34 Using Annoy for neighbor search, n_neighbors = 7
21:34:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f97e612
21:34:34 Searching Annoy index using 1 thread, search_k = 700
21:34:34 Annoy recall = 100%
21:34:35 Commencing smooth kNN distance calibration using 1 thread
21:34:36 Initializing from normalized Laplacian + noise
21:34:36 Commencing optimization for 500 epochs, with 10170 positive edges
21:34:38 Optimization finished
[1] "7 0.02"21:34:38 UMAP embedding parameters a = 1.859 b = 0.8109
21:34:38 Read 1203 rows and found 38 numeric columns
21:34:38 Using Annoy for neighbor search, n_neighbors = 7
21:34:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872662015c
21:34:38 Searching Annoy index using 1 thread, search_k = 700
21:34:38 Annoy recall = 100%
21:34:39 Commencing smooth kNN distance calibration using 1 thread
21:34:40 Initializing from normalized Laplacian + noise
21:34:40 Commencing optimization for 500 epochs, with 10170 positive edges
21:34:42 Optimization finished
[1] "7 0.03"21:34:43 UMAP embedding parameters a = 1.822 b = 0.8212
21:34:43 Read 1203 rows and found 38 numeric columns
21:34:43 Using Annoy for neighbor search, n_neighbors = 7
21:34:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770bdc318
21:34:43 Searching Annoy index using 1 thread, search_k = 700
21:34:43 Annoy recall = 100%
21:34:43 Commencing smooth kNN distance calibration using 1 thread
21:34:45 Initializing from normalized Laplacian + noise
21:34:45 Commencing optimization for 500 epochs, with 10170 positive edges
21:34:47 Optimization finished
[1] "7 0.04"21:34:47 UMAP embedding parameters a = 1.786 b = 0.8316
21:34:47 Read 1203 rows and found 38 numeric columns
21:34:47 Using Annoy for neighbor search, n_neighbors = 7
21:34:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c843e56
21:34:47 Searching Annoy index using 1 thread, search_k = 700
21:34:47 Annoy recall = 100%
21:34:48 Commencing smooth kNN distance calibration using 1 thread
21:34:49 Initializing from normalized Laplacian + noise
21:34:49 Commencing optimization for 500 epochs, with 10170 positive edges
21:34:51 Optimization finished
[1] "7 0.05"21:34:51 UMAP embedding parameters a = 1.75 b = 0.8421
21:34:51 Read 1203 rows and found 38 numeric columns
21:34:52 Using Annoy for neighbor search, n_neighbors = 7
21:34:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87343b27cc
21:34:52 Searching Annoy index using 1 thread, search_k = 700
21:34:52 Annoy recall = 100%
21:34:52 Commencing smooth kNN distance calibration using 1 thread
21:34:54 Initializing from normalized Laplacian + noise
21:34:54 Commencing optimization for 500 epochs, with 10170 positive edges
21:34:56 Optimization finished
[1] "7 0.06"21:34:56 UMAP embedding parameters a = 1.715 b = 0.8526
21:34:56 Read 1203 rows and found 38 numeric columns
21:34:56 Using Annoy for neighbor search, n_neighbors = 7
21:34:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e8ed8b8
21:34:56 Searching Annoy index using 1 thread, search_k = 700
21:34:56 Annoy recall = 100%
21:34:57 Commencing smooth kNN distance calibration using 1 thread
21:34:58 Initializing from normalized Laplacian + noise
21:34:58 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:00 Optimization finished
[1] "7 0.07"21:35:01 UMAP embedding parameters a = 1.68 b = 0.8631
21:35:01 Read 1203 rows and found 38 numeric columns
21:35:01 Using Annoy for neighbor search, n_neighbors = 7
21:35:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712f8505a
21:35:01 Searching Annoy index using 1 thread, search_k = 700
21:35:01 Annoy recall = 100%
21:35:01 Commencing smooth kNN distance calibration using 1 thread
21:35:03 Initializing from normalized Laplacian + noise
21:35:03 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:05 Optimization finished
[1] "7 0.08"21:35:05 UMAP embedding parameters a = 1.645 b = 0.8737
21:35:05 Read 1203 rows and found 38 numeric columns
21:35:05 Using Annoy for neighbor search, n_neighbors = 7
21:35:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87374123ba
21:35:05 Searching Annoy index using 1 thread, search_k = 700
21:35:05 Annoy recall = 100%
21:35:06 Commencing smooth kNN distance calibration using 1 thread
21:35:07 Initializing from normalized Laplacian + noise
21:35:07 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:09 Optimization finished
[1] "7 0.09"21:35:10 UMAP embedding parameters a = 1.611 b = 0.8844
21:35:10 Read 1203 rows and found 38 numeric columns
21:35:10 Using Annoy for neighbor search, n_neighbors = 7
21:35:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712323f23
21:35:10 Searching Annoy index using 1 thread, search_k = 700
21:35:10 Annoy recall = 100%
21:35:11 Commencing smooth kNN distance calibration using 1 thread
21:35:12 Initializing from normalized Laplacian + noise
21:35:12 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:14 Optimization finished
[1] "7 0.1"21:35:14 UMAP embedding parameters a = 1.577 b = 0.8951
21:35:14 Read 1203 rows and found 38 numeric columns
21:35:14 Using Annoy for neighbor search, n_neighbors = 7
21:35:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8799950fb
21:35:14 Searching Annoy index using 1 thread, search_k = 700
21:35:14 Annoy recall = 100%
21:35:15 Commencing smooth kNN distance calibration using 1 thread
21:35:16 Initializing from normalized Laplacian + noise
21:35:16 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:18 Optimization finished
[1] "7 0.11"21:35:19 UMAP embedding parameters a = 1.544 b = 0.9058
21:35:19 Read 1203 rows and found 38 numeric columns
21:35:19 Using Annoy for neighbor search, n_neighbors = 7
21:35:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e90ce81
21:35:19 Searching Annoy index using 1 thread, search_k = 700
21:35:19 Annoy recall = 100%
21:35:20 Commencing smooth kNN distance calibration using 1 thread
21:35:21 Initializing from normalized Laplacian + noise
21:35:21 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:23 Optimization finished
[1] "7 0.12"21:35:23 UMAP embedding parameters a = 1.51 b = 0.9165
21:35:23 Read 1203 rows and found 38 numeric columns
21:35:23 Using Annoy for neighbor search, n_neighbors = 7
21:35:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e32f7f2
21:35:24 Searching Annoy index using 1 thread, search_k = 700
21:35:24 Annoy recall = 100%
21:35:24 Commencing smooth kNN distance calibration using 1 thread
21:35:26 Initializing from normalized Laplacian + noise
21:35:26 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:28 Optimization finished
[1] "7 0.13"21:35:28 UMAP embedding parameters a = 1.478 b = 0.9272
21:35:28 Read 1203 rows and found 38 numeric columns
21:35:28 Using Annoy for neighbor search, n_neighbors = 7
21:35:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a092b4e
21:35:28 Searching Annoy index using 1 thread, search_k = 700
21:35:28 Annoy recall = 100%
21:35:29 Commencing smooth kNN distance calibration using 1 thread
21:35:30 Initializing from normalized Laplacian + noise
21:35:30 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:32 Optimization finished
[1] "7 0.14"21:35:33 UMAP embedding parameters a = 1.446 b = 0.938
21:35:33 Read 1203 rows and found 38 numeric columns
21:35:33 Using Annoy for neighbor search, n_neighbors = 7
21:35:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c3b15f0
21:35:33 Searching Annoy index using 1 thread, search_k = 700
21:35:33 Annoy recall = 100%
21:35:34 Commencing smooth kNN distance calibration using 1 thread
21:35:35 Initializing from normalized Laplacian + noise
21:35:35 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:37 Optimization finished
[1] "7 0.15"21:35:37 UMAP embedding parameters a = 1.414 b = 0.9488
21:35:37 Read 1203 rows and found 38 numeric columns
21:35:37 Using Annoy for neighbor search, n_neighbors = 7
21:35:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87765175b6
21:35:37 Searching Annoy index using 1 thread, search_k = 700
21:35:38 Annoy recall = 100%
21:35:38 Commencing smooth kNN distance calibration using 1 thread
21:35:40 Initializing from normalized Laplacian + noise
21:35:40 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:42 Optimization finished
[1] "7 0.16"21:35:42 UMAP embedding parameters a = 1.383 b = 0.9596
21:35:42 Read 1203 rows and found 38 numeric columns
21:35:42 Using Annoy for neighbor search, n_neighbors = 7
21:35:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a1c5bb1
21:35:42 Searching Annoy index using 1 thread, search_k = 700
21:35:42 Annoy recall = 100%
21:35:43 Commencing smooth kNN distance calibration using 1 thread
21:35:44 Initializing from normalized Laplacian + noise
21:35:44 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:46 Optimization finished
[1] "7 0.17"21:35:46 UMAP embedding parameters a = 1.352 b = 0.9704
21:35:46 Read 1203 rows and found 38 numeric columns
21:35:46 Using Annoy for neighbor search, n_neighbors = 7
21:35:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733abb9f2
21:35:47 Searching Annoy index using 1 thread, search_k = 700
21:35:47 Annoy recall = 100%
21:35:47 Commencing smooth kNN distance calibration using 1 thread
21:35:49 Initializing from normalized Laplacian + noise
21:35:49 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:51 Optimization finished
[1] "7 0.18"21:35:51 UMAP embedding parameters a = 1.321 b = 0.9813
21:35:51 Read 1203 rows and found 38 numeric columns
21:35:51 Using Annoy for neighbor search, n_neighbors = 7
21:35:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719cdfd64
21:35:51 Searching Annoy index using 1 thread, search_k = 700
21:35:51 Annoy recall = 100%
21:35:52 Commencing smooth kNN distance calibration using 1 thread
21:35:53 Initializing from normalized Laplacian + noise
21:35:54 Commencing optimization for 500 epochs, with 10170 positive edges
21:35:56 Optimization finished
[1] "7 0.19"21:35:56 UMAP embedding parameters a = 1.292 b = 0.9921
21:35:56 Read 1203 rows and found 38 numeric columns
21:35:56 Using Annoy for neighbor search, n_neighbors = 7
21:35:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748d83f7c
21:35:56 Searching Annoy index using 1 thread, search_k = 700
21:35:56 Annoy recall = 100%
21:35:57 Commencing smooth kNN distance calibration using 1 thread
21:35:58 Initializing from normalized Laplacian + noise
21:35:58 Commencing optimization for 500 epochs, with 10170 positive edges
21:36:00 Optimization finished
[1] "7 0.2"21:36:00 UMAP embedding parameters a = 1.262 b = 1.003
21:36:00 Read 1203 rows and found 38 numeric columns
21:36:00 Using Annoy for neighbor search, n_neighbors = 7
21:36:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732fed890
21:36:01 Searching Annoy index using 1 thread, search_k = 700
21:36:01 Annoy recall = 100%
21:36:01 Commencing smooth kNN distance calibration using 1 thread
21:36:03 Initializing from normalized Laplacian + noise
21:36:03 Commencing optimization for 500 epochs, with 10170 positive edges
21:36:05 Optimization finished
[1] "8 0"21:36:05 UMAP embedding parameters a = 1.933 b = 0.7905
21:36:05 Read 1203 rows and found 38 numeric columns
21:36:05 Using Annoy for neighbor search, n_neighbors = 8
21:36:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87279d9dac
21:36:05 Searching Annoy index using 1 thread, search_k = 800
21:36:05 Annoy recall = 100%
21:36:06 Commencing smooth kNN distance calibration using 1 thread
21:36:08 Initializing from normalized Laplacian + noise
21:36:08 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:10 Optimization finished
[1] "8 0.01"21:36:10 UMAP embedding parameters a = 1.896 b = 0.8006
21:36:10 Read 1203 rows and found 38 numeric columns
21:36:10 Using Annoy for neighbor search, n_neighbors = 8
21:36:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87157a86e2
21:36:10 Searching Annoy index using 1 thread, search_k = 800
21:36:10 Annoy recall = 100%
21:36:11 Commencing smooth kNN distance calibration using 1 thread
21:36:12 Initializing from normalized Laplacian + noise
21:36:12 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:15 Optimization finished
[1] "8 0.02"21:36:15 UMAP embedding parameters a = 1.859 b = 0.8109
21:36:15 Read 1203 rows and found 38 numeric columns
21:36:15 Using Annoy for neighbor search, n_neighbors = 8
21:36:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87126c7b42
21:36:15 Searching Annoy index using 1 thread, search_k = 800
21:36:15 Annoy recall = 100%
21:36:16 Commencing smooth kNN distance calibration using 1 thread
21:36:17 Initializing from normalized Laplacian + noise
21:36:17 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:20 Optimization finished
[1] "8 0.03"21:36:20 UMAP embedding parameters a = 1.822 b = 0.8212
21:36:20 Read 1203 rows and found 38 numeric columns
21:36:20 Using Annoy for neighbor search, n_neighbors = 8
21:36:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732670b9b
21:36:20 Searching Annoy index using 1 thread, search_k = 800
21:36:20 Annoy recall = 100%
21:36:21 Commencing smooth kNN distance calibration using 1 thread
21:36:22 Initializing from normalized Laplacian + noise
21:36:22 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:24 Optimization finished
[1] "8 0.04"21:36:25 UMAP embedding parameters a = 1.786 b = 0.8316
21:36:25 Read 1203 rows and found 38 numeric columns
21:36:25 Using Annoy for neighbor search, n_neighbors = 8
21:36:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745d449c6
21:36:25 Searching Annoy index using 1 thread, search_k = 800
21:36:25 Annoy recall = 100%
21:36:26 Commencing smooth kNN distance calibration using 1 thread
21:36:27 Initializing from normalized Laplacian + noise
21:36:27 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:29 Optimization finished
[1] "8 0.05"21:36:30 UMAP embedding parameters a = 1.75 b = 0.8421
21:36:30 Read 1203 rows and found 38 numeric columns
21:36:30 Using Annoy for neighbor search, n_neighbors = 8
21:36:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c89775c
21:36:30 Searching Annoy index using 1 thread, search_k = 800
21:36:30 Annoy recall = 100%
21:36:31 Commencing smooth kNN distance calibration using 1 thread
21:36:32 Initializing from normalized Laplacian + noise
21:36:32 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:34 Optimization finished
[1] "8 0.06"21:36:34 UMAP embedding parameters a = 1.715 b = 0.8526
21:36:34 Read 1203 rows and found 38 numeric columns
21:36:34 Using Annoy for neighbor search, n_neighbors = 8
21:36:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712976eb7
21:36:35 Searching Annoy index using 1 thread, search_k = 800
21:36:35 Annoy recall = 100%
21:36:35 Commencing smooth kNN distance calibration using 1 thread
21:36:37 Initializing from normalized Laplacian + noise
21:36:37 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:39 Optimization finished
[1] "8 0.07"21:36:39 UMAP embedding parameters a = 1.68 b = 0.8631
21:36:39 Read 1203 rows and found 38 numeric columns
21:36:39 Using Annoy for neighbor search, n_neighbors = 8
21:36:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ac96d91
21:36:40 Searching Annoy index using 1 thread, search_k = 800
21:36:40 Annoy recall = 100%
21:36:40 Commencing smooth kNN distance calibration using 1 thread
21:36:42 Initializing from normalized Laplacian + noise
21:36:42 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:44 Optimization finished
[1] "8 0.08"21:36:44 UMAP embedding parameters a = 1.645 b = 0.8737
21:36:44 Read 1203 rows and found 38 numeric columns
21:36:44 Using Annoy for neighbor search, n_neighbors = 8
21:36:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f0da835
21:36:44 Searching Annoy index using 1 thread, search_k = 800
21:36:45 Annoy recall = 100%
21:36:45 Commencing smooth kNN distance calibration using 1 thread
21:36:47 Initializing from normalized Laplacian + noise
21:36:47 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:49 Optimization finished
[1] "8 0.09"21:36:49 UMAP embedding parameters a = 1.611 b = 0.8844
21:36:49 Read 1203 rows and found 38 numeric columns
21:36:49 Using Annoy for neighbor search, n_neighbors = 8
21:36:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731ecccfb
21:36:49 Searching Annoy index using 1 thread, search_k = 800
21:36:49 Annoy recall = 100%
21:36:50 Commencing smooth kNN distance calibration using 1 thread
21:36:52 Initializing from normalized Laplacian + noise
21:36:52 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:54 Optimization finished
[1] "8 0.1"21:36:54 UMAP embedding parameters a = 1.577 b = 0.8951
21:36:54 Read 1203 rows and found 38 numeric columns
21:36:54 Using Annoy for neighbor search, n_neighbors = 8
21:36:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739830af0
21:36:54 Searching Annoy index using 1 thread, search_k = 800
21:36:54 Annoy recall = 100%
21:36:55 Commencing smooth kNN distance calibration using 1 thread
21:36:57 Initializing from normalized Laplacian + noise
21:36:57 Commencing optimization for 500 epochs, with 11730 positive edges
21:36:59 Optimization finished
[1] "8 0.11"21:36:59 UMAP embedding parameters a = 1.544 b = 0.9058
21:36:59 Read 1203 rows and found 38 numeric columns
21:36:59 Using Annoy for neighbor search, n_neighbors = 8
21:36:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ea58e47
21:36:59 Searching Annoy index using 1 thread, search_k = 800
21:37:00 Annoy recall = 100%
21:37:00 Commencing smooth kNN distance calibration using 1 thread
21:37:02 Initializing from normalized Laplacian + noise
21:37:02 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:04 Optimization finished
[1] "8 0.12"21:37:04 UMAP embedding parameters a = 1.51 b = 0.9165
21:37:04 Read 1203 rows and found 38 numeric columns
21:37:04 Using Annoy for neighbor search, n_neighbors = 8
21:37:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87584ece57
21:37:05 Searching Annoy index using 1 thread, search_k = 800
21:37:05 Annoy recall = 100%
21:37:05 Commencing smooth kNN distance calibration using 1 thread
21:37:07 Initializing from normalized Laplacian + noise
21:37:07 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:09 Optimization finished
[1] "8 0.13"21:37:09 UMAP embedding parameters a = 1.478 b = 0.9272
21:37:09 Read 1203 rows and found 38 numeric columns
21:37:09 Using Annoy for neighbor search, n_neighbors = 8
21:37:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a40ce08
21:37:10 Searching Annoy index using 1 thread, search_k = 800
21:37:10 Annoy recall = 100%
21:37:11 Commencing smooth kNN distance calibration using 1 thread
21:37:12 Initializing from normalized Laplacian + noise
21:37:12 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:14 Optimization finished
[1] "8 0.14"21:37:15 UMAP embedding parameters a = 1.446 b = 0.938
21:37:15 Read 1203 rows and found 38 numeric columns
21:37:15 Using Annoy for neighbor search, n_neighbors = 8
21:37:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b29cc9d
21:37:15 Searching Annoy index using 1 thread, search_k = 800
21:37:15 Annoy recall = 100%
21:37:16 Commencing smooth kNN distance calibration using 1 thread
21:37:17 Initializing from normalized Laplacian + noise
21:37:17 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:19 Optimization finished
[1] "8 0.15"21:37:20 UMAP embedding parameters a = 1.414 b = 0.9488
21:37:20 Read 1203 rows and found 38 numeric columns
21:37:20 Using Annoy for neighbor search, n_neighbors = 8
21:37:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c89f623
21:37:20 Searching Annoy index using 1 thread, search_k = 800
21:37:20 Annoy recall = 100%
21:37:21 Commencing smooth kNN distance calibration using 1 thread
21:37:22 Initializing from normalized Laplacian + noise
21:37:22 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:25 Optimization finished
[1] "8 0.16"21:37:25 UMAP embedding parameters a = 1.383 b = 0.9596
21:37:25 Read 1203 rows and found 38 numeric columns
21:37:25 Using Annoy for neighbor search, n_neighbors = 8
21:37:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748cfa6c0
21:37:25 Searching Annoy index using 1 thread, search_k = 800
21:37:25 Annoy recall = 100%
21:37:26 Commencing smooth kNN distance calibration using 1 thread
21:37:28 Initializing from normalized Laplacian + noise
21:37:28 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:30 Optimization finished
[1] "8 0.17"21:37:30 UMAP embedding parameters a = 1.352 b = 0.9704
21:37:30 Read 1203 rows and found 38 numeric columns
21:37:30 Using Annoy for neighbor search, n_neighbors = 8
21:37:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e221cf8
21:37:30 Searching Annoy index using 1 thread, search_k = 800
21:37:30 Annoy recall = 100%
21:37:31 Commencing smooth kNN distance calibration using 1 thread
21:37:33 Initializing from normalized Laplacian + noise
21:37:33 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:35 Optimization finished
[1] "8 0.18"21:37:35 UMAP embedding parameters a = 1.321 b = 0.9813
21:37:35 Read 1203 rows and found 38 numeric columns
21:37:35 Using Annoy for neighbor search, n_neighbors = 8
21:37:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743cb19de
21:37:35 Searching Annoy index using 1 thread, search_k = 800
21:37:36 Annoy recall = 100%
21:37:36 Commencing smooth kNN distance calibration using 1 thread
21:37:38 Initializing from normalized Laplacian + noise
21:37:38 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:40 Optimization finished
[1] "8 0.19"21:37:40 UMAP embedding parameters a = 1.292 b = 0.9921
21:37:40 Read 1203 rows and found 38 numeric columns
21:37:40 Using Annoy for neighbor search, n_neighbors = 8
21:37:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b01e5e3
21:37:41 Searching Annoy index using 1 thread, search_k = 800
21:37:41 Annoy recall = 100%
21:37:41 Commencing smooth kNN distance calibration using 1 thread
21:37:43 Initializing from normalized Laplacian + noise
21:37:43 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:45 Optimization finished
[1] "8 0.2"21:37:46 UMAP embedding parameters a = 1.262 b = 1.003
21:37:46 Read 1203 rows and found 38 numeric columns
21:37:46 Using Annoy for neighbor search, n_neighbors = 8
21:37:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777bb6df3
21:37:46 Searching Annoy index using 1 thread, search_k = 800
21:37:46 Annoy recall = 100%
21:37:47 Commencing smooth kNN distance calibration using 1 thread
21:37:48 Initializing from normalized Laplacian + noise
21:37:48 Commencing optimization for 500 epochs, with 11730 positive edges
21:37:51 Optimization finished
[1] "9 0"21:37:51 UMAP embedding parameters a = 1.933 b = 0.7905
21:37:51 Read 1203 rows and found 38 numeric columns
21:37:51 Using Annoy for neighbor search, n_neighbors = 9
21:37:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87525be85f
21:37:51 Searching Annoy index using 1 thread, search_k = 900
21:37:51 Annoy recall = 100%
21:37:52 Commencing smooth kNN distance calibration using 1 thread
21:37:53 Initializing from normalized Laplacian + noise
21:37:53 Commencing optimization for 500 epochs, with 13316 positive edges
21:37:56 Optimization finished
[1] "9 0.01"21:37:56 UMAP embedding parameters a = 1.896 b = 0.8006
21:37:56 Read 1203 rows and found 38 numeric columns
21:37:56 Using Annoy for neighbor search, n_neighbors = 9
21:37:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872934ddd5
21:37:56 Searching Annoy index using 1 thread, search_k = 900
21:37:56 Annoy recall = 100%
21:37:57 Commencing smooth kNN distance calibration using 1 thread
21:37:59 Initializing from normalized Laplacian + noise
21:37:59 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:01 Optimization finished
[1] "9 0.02"21:38:01 UMAP embedding parameters a = 1.859 b = 0.8109
21:38:01 Read 1203 rows and found 38 numeric columns
21:38:01 Using Annoy for neighbor search, n_neighbors = 9
21:38:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721c49942
21:38:02 Searching Annoy index using 1 thread, search_k = 900
21:38:02 Annoy recall = 100%
21:38:03 Commencing smooth kNN distance calibration using 1 thread
21:38:04 Initializing from normalized Laplacian + noise
21:38:04 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:07 Optimization finished
[1] "9 0.03"21:38:07 UMAP embedding parameters a = 1.822 b = 0.8212
21:38:07 Read 1203 rows and found 38 numeric columns
21:38:07 Using Annoy for neighbor search, n_neighbors = 9
21:38:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e96fe4f
21:38:07 Searching Annoy index using 1 thread, search_k = 900
21:38:07 Annoy recall = 100%
21:38:08 Commencing smooth kNN distance calibration using 1 thread
21:38:10 Initializing from normalized Laplacian + noise
21:38:10 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:12 Optimization finished
[1] "9 0.04"21:38:12 UMAP embedding parameters a = 1.786 b = 0.8316
21:38:12 Read 1203 rows and found 38 numeric columns
21:38:12 Using Annoy for neighbor search, n_neighbors = 9
21:38:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f86538b
21:38:12 Searching Annoy index using 1 thread, search_k = 900
21:38:12 Annoy recall = 100%
21:38:13 Commencing smooth kNN distance calibration using 1 thread
21:38:15 Initializing from normalized Laplacian + noise
21:38:15 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:17 Optimization finished
[1] "9 0.05"21:38:17 UMAP embedding parameters a = 1.75 b = 0.8421
21:38:17 Read 1203 rows and found 38 numeric columns
21:38:17 Using Annoy for neighbor search, n_neighbors = 9
21:38:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872be0f4f3
21:38:18 Searching Annoy index using 1 thread, search_k = 900
21:38:18 Annoy recall = 100%
21:38:19 Commencing smooth kNN distance calibration using 1 thread
21:38:20 Initializing from normalized Laplacian + noise
21:38:20 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:23 Optimization finished
[1] "9 0.06"21:38:23 UMAP embedding parameters a = 1.715 b = 0.8526
21:38:23 Read 1203 rows and found 38 numeric columns
21:38:23 Using Annoy for neighbor search, n_neighbors = 9
21:38:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872242b841
21:38:23 Searching Annoy index using 1 thread, search_k = 900
21:38:23 Annoy recall = 100%
21:38:24 Commencing smooth kNN distance calibration using 1 thread
21:38:25 Initializing from normalized Laplacian + noise
21:38:25 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:28 Optimization finished
[1] "9 0.07"21:38:28 UMAP embedding parameters a = 1.68 b = 0.8631
21:38:28 Read 1203 rows and found 38 numeric columns
21:38:28 Using Annoy for neighbor search, n_neighbors = 9
21:38:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87395450ef
21:38:28 Searching Annoy index using 1 thread, search_k = 900
21:38:28 Annoy recall = 100%
21:38:29 Commencing smooth kNN distance calibration using 1 thread
21:38:31 Initializing from normalized Laplacian + noise
21:38:31 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:33 Optimization finished
[1] "9 0.08"21:38:33 UMAP embedding parameters a = 1.645 b = 0.8737
21:38:33 Read 1203 rows and found 38 numeric columns
21:38:33 Using Annoy for neighbor search, n_neighbors = 9
21:38:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774b9346f
21:38:33 Searching Annoy index using 1 thread, search_k = 900
21:38:34 Annoy recall = 100%
21:38:34 Commencing smooth kNN distance calibration using 1 thread
21:38:36 Initializing from normalized Laplacian + noise
21:38:36 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:38 Optimization finished
[1] "9 0.09"21:38:39 UMAP embedding parameters a = 1.611 b = 0.8844
21:38:39 Read 1203 rows and found 38 numeric columns
21:38:39 Using Annoy for neighbor search, n_neighbors = 9
21:38:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87554190d2
21:38:39 Searching Annoy index using 1 thread, search_k = 900
21:38:39 Annoy recall = 100%
21:38:40 Commencing smooth kNN distance calibration using 1 thread
21:38:41 Initializing from normalized Laplacian + noise
21:38:41 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:44 Optimization finished
[1] "9 0.1"21:38:44 UMAP embedding parameters a = 1.577 b = 0.8951
21:38:44 Read 1203 rows and found 38 numeric columns
21:38:44 Using Annoy for neighbor search, n_neighbors = 9
21:38:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760f1ee9b
21:38:44 Searching Annoy index using 1 thread, search_k = 900
21:38:44 Annoy recall = 100%
21:38:45 Commencing smooth kNN distance calibration using 1 thread
21:38:47 Initializing from normalized Laplacian + noise
21:38:47 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:49 Optimization finished
[1] "9 0.11"21:38:49 UMAP embedding parameters a = 1.544 b = 0.9058
21:38:49 Read 1203 rows and found 38 numeric columns
21:38:49 Using Annoy for neighbor search, n_neighbors = 9
21:38:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a33bb51
21:38:49 Searching Annoy index using 1 thread, search_k = 900
21:38:49 Annoy recall = 100%
21:38:50 Commencing smooth kNN distance calibration using 1 thread
21:38:52 Initializing from normalized Laplacian + noise
21:38:52 Commencing optimization for 500 epochs, with 13316 positive edges
21:38:54 Optimization finished
[1] "9 0.12"21:38:54 UMAP embedding parameters a = 1.51 b = 0.9165
21:38:54 Read 1203 rows and found 38 numeric columns
21:38:54 Using Annoy for neighbor search, n_neighbors = 9
21:38:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767ae0c14
21:38:55 Searching Annoy index using 1 thread, search_k = 900
21:38:55 Annoy recall = 100%
21:38:56 Commencing smooth kNN distance calibration using 1 thread
21:38:57 Initializing from normalized Laplacian + noise
21:38:57 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:00 Optimization finished
[1] "9 0.13"21:39:00 UMAP embedding parameters a = 1.478 b = 0.9272
21:39:00 Read 1203 rows and found 38 numeric columns
21:39:00 Using Annoy for neighbor search, n_neighbors = 9
21:39:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871358fa37
21:39:00 Searching Annoy index using 1 thread, search_k = 900
21:39:00 Annoy recall = 100%
21:39:01 Commencing smooth kNN distance calibration using 1 thread
21:39:03 Initializing from normalized Laplacian + noise
21:39:03 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:05 Optimization finished
[1] "9 0.14"21:39:05 UMAP embedding parameters a = 1.446 b = 0.938
21:39:05 Read 1203 rows and found 38 numeric columns
21:39:05 Using Annoy for neighbor search, n_neighbors = 9
21:39:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750080518
21:39:05 Searching Annoy index using 1 thread, search_k = 900
21:39:05 Annoy recall = 100%
21:39:06 Commencing smooth kNN distance calibration using 1 thread
21:39:08 Initializing from normalized Laplacian + noise
21:39:08 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:10 Optimization finished
[1] "9 0.15"21:39:11 UMAP embedding parameters a = 1.414 b = 0.9488
21:39:11 Read 1203 rows and found 38 numeric columns
21:39:11 Using Annoy for neighbor search, n_neighbors = 9
21:39:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754378370
21:39:11 Searching Annoy index using 1 thread, search_k = 900
21:39:11 Annoy recall = 100%
21:39:12 Commencing smooth kNN distance calibration using 1 thread
21:39:13 Initializing from normalized Laplacian + noise
21:39:13 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:16 Optimization finished
[1] "9 0.16"21:39:16 UMAP embedding parameters a = 1.383 b = 0.9596
21:39:16 Read 1203 rows and found 38 numeric columns
21:39:16 Using Annoy for neighbor search, n_neighbors = 9
21:39:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725f068ee
21:39:16 Searching Annoy index using 1 thread, search_k = 900
21:39:16 Annoy recall = 100%
21:39:17 Commencing smooth kNN distance calibration using 1 thread
21:39:19 Initializing from normalized Laplacian + noise
21:39:19 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:21 Optimization finished
[1] "9 0.17"21:39:21 UMAP embedding parameters a = 1.352 b = 0.9704
21:39:21 Read 1203 rows and found 38 numeric columns
21:39:21 Using Annoy for neighbor search, n_neighbors = 9
21:39:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ad172a9
21:39:22 Searching Annoy index using 1 thread, search_k = 900
21:39:22 Annoy recall = 100%
21:39:23 Commencing smooth kNN distance calibration using 1 thread
21:39:24 Initializing from normalized Laplacian + noise
21:39:24 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:27 Optimization finished
[1] "9 0.18"21:39:27 UMAP embedding parameters a = 1.321 b = 0.9813
21:39:27 Read 1203 rows and found 38 numeric columns
21:39:27 Using Annoy for neighbor search, n_neighbors = 9
21:39:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723452ba5
21:39:27 Searching Annoy index using 1 thread, search_k = 900
21:39:27 Annoy recall = 100%
21:39:28 Commencing smooth kNN distance calibration using 1 thread
21:39:30 Initializing from normalized Laplacian + noise
21:39:30 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:32 Optimization finished
[1] "9 0.19"21:39:32 UMAP embedding parameters a = 1.292 b = 0.9921
21:39:32 Read 1203 rows and found 38 numeric columns
21:39:32 Using Annoy for neighbor search, n_neighbors = 9
21:39:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757dd35e9
21:39:33 Searching Annoy index using 1 thread, search_k = 900
21:39:33 Annoy recall = 100%
21:39:33 Commencing smooth kNN distance calibration using 1 thread
21:39:35 Initializing from normalized Laplacian + noise
21:39:35 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:38 Optimization finished
[1] "9 0.2"21:39:38 UMAP embedding parameters a = 1.262 b = 1.003
21:39:38 Read 1203 rows and found 38 numeric columns
21:39:38 Using Annoy for neighbor search, n_neighbors = 9
21:39:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764547d99
21:39:38 Searching Annoy index using 1 thread, search_k = 900
21:39:38 Annoy recall = 100%
21:39:39 Commencing smooth kNN distance calibration using 1 thread
21:39:41 Initializing from normalized Laplacian + noise
21:39:41 Commencing optimization for 500 epochs, with 13316 positive edges
21:39:43 Optimization finished
[1] "10 0"21:39:43 UMAP embedding parameters a = 1.933 b = 0.7905
21:39:43 Read 1203 rows and found 38 numeric columns
21:39:43 Using Annoy for neighbor search, n_neighbors = 10
21:39:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871eab9ec
21:39:44 Searching Annoy index using 1 thread, search_k = 1000
21:39:44 Annoy recall = 100%
21:39:45 Commencing smooth kNN distance calibration using 1 thread
21:39:46 Initializing from normalized Laplacian + noise
21:39:46 Commencing optimization for 500 epochs, with 14864 positive edges
21:39:49 Optimization finished
[1] "10 0.01"21:39:49 UMAP embedding parameters a = 1.896 b = 0.8006
21:39:49 Read 1203 rows and found 38 numeric columns
21:39:49 Using Annoy for neighbor search, n_neighbors = 10
21:39:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87302c0440
21:39:49 Searching Annoy index using 1 thread, search_k = 1000
21:39:49 Annoy recall = 100%
21:39:50 Commencing smooth kNN distance calibration using 1 thread
21:39:52 Initializing from normalized Laplacian + noise
21:39:52 Commencing optimization for 500 epochs, with 14864 positive edges
21:39:55 Optimization finished
[1] "10 0.02"21:39:55 UMAP embedding parameters a = 1.859 b = 0.8109
21:39:55 Read 1203 rows and found 38 numeric columns
21:39:55 Using Annoy for neighbor search, n_neighbors = 10
21:39:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e954ba1
21:39:55 Searching Annoy index using 1 thread, search_k = 1000
21:39:55 Annoy recall = 100%
21:39:56 Commencing smooth kNN distance calibration using 1 thread
21:39:58 Initializing from normalized Laplacian + noise
21:39:58 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:00 Optimization finished
[1] "10 0.03"21:40:00 UMAP embedding parameters a = 1.822 b = 0.8212
21:40:01 Read 1203 rows and found 38 numeric columns
21:40:01 Using Annoy for neighbor search, n_neighbors = 10
21:40:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d148689
21:40:01 Searching Annoy index using 1 thread, search_k = 1000
21:40:01 Annoy recall = 100%
21:40:02 Commencing smooth kNN distance calibration using 1 thread
21:40:04 Initializing from normalized Laplacian + noise
21:40:04 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:06 Optimization finished
[1] "10 0.04"21:40:06 UMAP embedding parameters a = 1.786 b = 0.8316
21:40:06 Read 1203 rows and found 38 numeric columns
21:40:06 Using Annoy for neighbor search, n_neighbors = 10
21:40:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cb5fa64
21:40:06 Searching Annoy index using 1 thread, search_k = 1000
21:40:07 Annoy recall = 100%
21:40:08 Commencing smooth kNN distance calibration using 1 thread
21:40:09 Initializing from normalized Laplacian + noise
21:40:09 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:12 Optimization finished
[1] "10 0.05"21:40:12 UMAP embedding parameters a = 1.75 b = 0.8421
21:40:12 Read 1203 rows and found 38 numeric columns
21:40:12 Using Annoy for neighbor search, n_neighbors = 10
21:40:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875764f261
21:40:12 Searching Annoy index using 1 thread, search_k = 1000
21:40:12 Annoy recall = 100%
21:40:13 Commencing smooth kNN distance calibration using 1 thread
21:40:15 Initializing from normalized Laplacian + noise
21:40:15 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:18 Optimization finished
[1] "10 0.06"21:40:18 UMAP embedding parameters a = 1.715 b = 0.8526
21:40:18 Read 1203 rows and found 38 numeric columns
21:40:18 Using Annoy for neighbor search, n_neighbors = 10
21:40:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b36a381
21:40:18 Searching Annoy index using 1 thread, search_k = 1000
21:40:18 Annoy recall = 100%
21:40:19 Commencing smooth kNN distance calibration using 1 thread
21:40:21 Initializing from normalized Laplacian + noise
21:40:21 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:23 Optimization finished
[1] "10 0.07"21:40:24 UMAP embedding parameters a = 1.68 b = 0.8631
21:40:24 Read 1203 rows and found 38 numeric columns
21:40:24 Using Annoy for neighbor search, n_neighbors = 10
21:40:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87811442
21:40:24 Searching Annoy index using 1 thread, search_k = 1000
21:40:24 Annoy recall = 100%
21:40:25 Commencing smooth kNN distance calibration using 1 thread
21:40:27 Initializing from normalized Laplacian + noise
21:40:27 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:29 Optimization finished
[1] "10 0.08"21:40:29 UMAP embedding parameters a = 1.645 b = 0.8737
21:40:29 Read 1203 rows and found 38 numeric columns
21:40:29 Using Annoy for neighbor search, n_neighbors = 10
21:40:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873266d844
21:40:30 Searching Annoy index using 1 thread, search_k = 1000
21:40:30 Annoy recall = 100%
21:40:31 Commencing smooth kNN distance calibration using 1 thread
21:40:32 Initializing from normalized Laplacian + noise
21:40:32 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:35 Optimization finished
[1] "10 0.09"21:40:35 UMAP embedding parameters a = 1.611 b = 0.8844
21:40:35 Read 1203 rows and found 38 numeric columns
21:40:35 Using Annoy for neighbor search, n_neighbors = 10
21:40:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742f21175
21:40:35 Searching Annoy index using 1 thread, search_k = 1000
21:40:35 Annoy recall = 100%
21:40:36 Commencing smooth kNN distance calibration using 1 thread
21:40:38 Initializing from normalized Laplacian + noise
21:40:38 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:41 Optimization finished
[1] "10 0.1"21:40:41 UMAP embedding parameters a = 1.577 b = 0.8951
21:40:41 Read 1203 rows and found 38 numeric columns
21:40:41 Using Annoy for neighbor search, n_neighbors = 10
21:40:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752dcfca1
21:40:41 Searching Annoy index using 1 thread, search_k = 1000
21:40:41 Annoy recall = 100%
21:40:42 Commencing smooth kNN distance calibration using 1 thread
21:40:44 Initializing from normalized Laplacian + noise
21:40:44 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:46 Optimization finished
[1] "10 0.11"21:40:47 UMAP embedding parameters a = 1.544 b = 0.9058
21:40:47 Read 1203 rows and found 38 numeric columns
21:40:47 Using Annoy for neighbor search, n_neighbors = 10
21:40:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b9bb619
21:40:47 Searching Annoy index using 1 thread, search_k = 1000
21:40:47 Annoy recall = 100%
21:40:48 Commencing smooth kNN distance calibration using 1 thread
21:40:49 Initializing from normalized Laplacian + noise
21:40:49 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:52 Optimization finished
[1] "10 0.12"21:40:52 UMAP embedding parameters a = 1.51 b = 0.9165
21:40:52 Read 1203 rows and found 38 numeric columns
21:40:52 Using Annoy for neighbor search, n_neighbors = 10
21:40:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764b6aab7
21:40:52 Searching Annoy index using 1 thread, search_k = 1000
21:40:53 Annoy recall = 100%
21:40:53 Commencing smooth kNN distance calibration using 1 thread
21:40:55 Initializing from normalized Laplacian + noise
21:40:55 Commencing optimization for 500 epochs, with 14864 positive edges
21:40:58 Optimization finished
[1] "10 0.13"21:40:58 UMAP embedding parameters a = 1.478 b = 0.9272
21:40:58 Read 1203 rows and found 38 numeric columns
21:40:58 Using Annoy for neighbor search, n_neighbors = 10
21:40:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874173faf0
21:40:58 Searching Annoy index using 1 thread, search_k = 1000
21:40:58 Annoy recall = 100%
21:40:59 Commencing smooth kNN distance calibration using 1 thread
21:41:01 Initializing from normalized Laplacian + noise
21:41:01 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:04 Optimization finished
[1] "10 0.14"21:41:04 UMAP embedding parameters a = 1.446 b = 0.938
21:41:04 Read 1203 rows and found 38 numeric columns
21:41:04 Using Annoy for neighbor search, n_neighbors = 10
21:41:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b2209a4
21:41:04 Searching Annoy index using 1 thread, search_k = 1000
21:41:04 Annoy recall = 100%
21:41:05 Commencing smooth kNN distance calibration using 1 thread
21:41:07 Initializing from normalized Laplacian + noise
21:41:07 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:09 Optimization finished
[1] "10 0.15"21:41:10 UMAP embedding parameters a = 1.414 b = 0.9488
21:41:10 Read 1203 rows and found 38 numeric columns
21:41:10 Using Annoy for neighbor search, n_neighbors = 10
21:41:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710979faa
21:41:10 Searching Annoy index using 1 thread, search_k = 1000
21:41:10 Annoy recall = 100%
21:41:11 Commencing smooth kNN distance calibration using 1 thread
21:41:13 Initializing from normalized Laplacian + noise
21:41:13 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:15 Optimization finished
[1] "10 0.16"21:41:15 UMAP embedding parameters a = 1.383 b = 0.9596
21:41:15 Read 1203 rows and found 38 numeric columns
21:41:15 Using Annoy for neighbor search, n_neighbors = 10
21:41:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763b6b331
21:41:16 Searching Annoy index using 1 thread, search_k = 1000
21:41:16 Annoy recall = 100%
21:41:17 Commencing smooth kNN distance calibration using 1 thread
21:41:18 Initializing from normalized Laplacian + noise
21:41:18 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:21 Optimization finished
[1] "10 0.17"21:41:21 UMAP embedding parameters a = 1.352 b = 0.9704
21:41:21 Read 1203 rows and found 38 numeric columns
21:41:21 Using Annoy for neighbor search, n_neighbors = 10
21:41:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734765a93
21:41:21 Searching Annoy index using 1 thread, search_k = 1000
21:41:21 Annoy recall = 100%
21:41:22 Commencing smooth kNN distance calibration using 1 thread
21:41:24 Initializing from normalized Laplacian + noise
21:41:24 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:27 Optimization finished
[1] "10 0.18"21:41:27 UMAP embedding parameters a = 1.321 b = 0.9813
21:41:27 Read 1203 rows and found 38 numeric columns
21:41:27 Using Annoy for neighbor search, n_neighbors = 10
21:41:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87550d419
21:41:27 Searching Annoy index using 1 thread, search_k = 1000
21:41:27 Annoy recall = 100%
21:41:28 Commencing smooth kNN distance calibration using 1 thread
21:41:30 Initializing from normalized Laplacian + noise
21:41:30 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:33 Optimization finished
[1] "10 0.19"21:41:33 UMAP embedding parameters a = 1.292 b = 0.9921
21:41:33 Read 1203 rows and found 38 numeric columns
21:41:33 Using Annoy for neighbor search, n_neighbors = 10
21:41:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738f84403
21:41:33 Searching Annoy index using 1 thread, search_k = 1000
21:41:33 Annoy recall = 100%
21:41:34 Commencing smooth kNN distance calibration using 1 thread
21:41:36 Initializing from normalized Laplacian + noise
21:41:36 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:38 Optimization finished
[1] "10 0.2"21:41:39 UMAP embedding parameters a = 1.262 b = 1.003
21:41:39 Read 1203 rows and found 38 numeric columns
21:41:39 Using Annoy for neighbor search, n_neighbors = 10
21:41:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871568492e
21:41:39 Searching Annoy index using 1 thread, search_k = 1000
21:41:39 Annoy recall = 100%
21:41:40 Commencing smooth kNN distance calibration using 1 thread
21:41:42 Initializing from normalized Laplacian + noise
21:41:42 Commencing optimization for 500 epochs, with 14864 positive edges
21:41:44 Optimization finished
[1] "11 0"21:41:44 UMAP embedding parameters a = 1.933 b = 0.7905
21:41:44 Read 1203 rows and found 38 numeric columns
21:41:44 Using Annoy for neighbor search, n_neighbors = 11
21:41:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f848f6b
21:41:45 Searching Annoy index using 1 thread, search_k = 1100
21:41:45 Annoy recall = 100%
21:41:46 Commencing smooth kNN distance calibration using 1 thread
21:41:47 Initializing from normalized Laplacian + noise
21:41:47 Commencing optimization for 500 epochs, with 16432 positive edges
21:41:50 Optimization finished
[1] "11 0.01"21:41:50 UMAP embedding parameters a = 1.896 b = 0.8006
21:41:50 Read 1203 rows and found 38 numeric columns
21:41:50 Using Annoy for neighbor search, n_neighbors = 11
21:41:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720a65017
21:41:50 Searching Annoy index using 1 thread, search_k = 1100
21:41:51 Annoy recall = 100%
21:41:52 Commencing smooth kNN distance calibration using 1 thread
21:41:53 Initializing from normalized Laplacian + noise
21:41:53 Commencing optimization for 500 epochs, with 16432 positive edges
21:41:56 Optimization finished
[1] "11 0.02"21:41:56 UMAP embedding parameters a = 1.859 b = 0.8109
21:41:56 Read 1203 rows and found 38 numeric columns
21:41:56 Using Annoy for neighbor search, n_neighbors = 11
21:41:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728c14365
21:41:56 Searching Annoy index using 1 thread, search_k = 1100
21:41:57 Annoy recall = 100%
21:41:57 Commencing smooth kNN distance calibration using 1 thread
21:41:59 Initializing from normalized Laplacian + noise
21:41:59 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:02 Optimization finished
[1] "11 0.03"21:42:02 UMAP embedding parameters a = 1.822 b = 0.8212
21:42:02 Read 1203 rows and found 38 numeric columns
21:42:02 Using Annoy for neighbor search, n_neighbors = 11
21:42:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f8c9483
21:42:02 Searching Annoy index using 1 thread, search_k = 1100
21:42:03 Annoy recall = 100%
21:42:03 Commencing smooth kNN distance calibration using 1 thread
21:42:05 Initializing from normalized Laplacian + noise
21:42:05 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:08 Optimization finished
[1] "11 0.04"21:42:08 UMAP embedding parameters a = 1.786 b = 0.8316
21:42:08 Read 1203 rows and found 38 numeric columns
21:42:08 Using Annoy for neighbor search, n_neighbors = 11
21:42:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774ddd387
21:42:08 Searching Annoy index using 1 thread, search_k = 1100
21:42:08 Annoy recall = 100%
21:42:09 Commencing smooth kNN distance calibration using 1 thread
21:42:11 Initializing from normalized Laplacian + noise
21:42:11 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:14 Optimization finished
[1] "11 0.05"21:42:14 UMAP embedding parameters a = 1.75 b = 0.8421
21:42:14 Read 1203 rows and found 38 numeric columns
21:42:14 Using Annoy for neighbor search, n_neighbors = 11
21:42:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874eb1ac54
21:42:14 Searching Annoy index using 1 thread, search_k = 1100
21:42:15 Annoy recall = 100%
21:42:15 Commencing smooth kNN distance calibration using 1 thread
21:42:17 Initializing from normalized Laplacian + noise
21:42:17 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:20 Optimization finished
[1] "11 0.06"21:42:20 UMAP embedding parameters a = 1.715 b = 0.8526
21:42:20 Read 1203 rows and found 38 numeric columns
21:42:20 Using Annoy for neighbor search, n_neighbors = 11
21:42:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a5e072c
21:42:20 Searching Annoy index using 1 thread, search_k = 1100
21:42:21 Annoy recall = 100%
21:42:21 Commencing smooth kNN distance calibration using 1 thread
21:42:23 Initializing from normalized Laplacian + noise
21:42:23 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:26 Optimization finished
[1] "11 0.07"21:42:26 UMAP embedding parameters a = 1.68 b = 0.8631
21:42:26 Read 1203 rows and found 38 numeric columns
21:42:26 Using Annoy for neighbor search, n_neighbors = 11
21:42:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871822ff2c
21:42:26 Searching Annoy index using 1 thread, search_k = 1100
21:42:27 Annoy recall = 100%
21:42:27 Commencing smooth kNN distance calibration using 1 thread
21:42:29 Initializing from normalized Laplacian + noise
21:42:29 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:32 Optimization finished
[1] "11 0.08"21:42:32 UMAP embedding parameters a = 1.645 b = 0.8737
21:42:32 Read 1203 rows and found 38 numeric columns
21:42:32 Using Annoy for neighbor search, n_neighbors = 11
21:42:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87268ee23d
21:42:32 Searching Annoy index using 1 thread, search_k = 1100
21:42:33 Annoy recall = 100%
21:42:33 Commencing smooth kNN distance calibration using 1 thread
21:42:35 Initializing from normalized Laplacian + noise
21:42:35 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:38 Optimization finished
[1] "11 0.09"21:42:38 UMAP embedding parameters a = 1.611 b = 0.8844
21:42:38 Read 1203 rows and found 38 numeric columns
21:42:38 Using Annoy for neighbor search, n_neighbors = 11
21:42:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876eb284c5
21:42:38 Searching Annoy index using 1 thread, search_k = 1100
21:42:39 Annoy recall = 100%
21:42:39 Commencing smooth kNN distance calibration using 1 thread
21:42:41 Initializing from normalized Laplacian + noise
21:42:41 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:44 Optimization finished
[1] "11 0.1"21:42:44 UMAP embedding parameters a = 1.577 b = 0.8951
21:42:44 Read 1203 rows and found 38 numeric columns
21:42:44 Using Annoy for neighbor search, n_neighbors = 11
21:42:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a0db918
21:42:44 Searching Annoy index using 1 thread, search_k = 1100
21:42:45 Annoy recall = 100%
21:42:46 Commencing smooth kNN distance calibration using 1 thread
21:42:47 Initializing from normalized Laplacian + noise
21:42:47 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:50 Optimization finished
[1] "11 0.11"21:42:50 UMAP embedding parameters a = 1.544 b = 0.9058
21:42:50 Read 1203 rows and found 38 numeric columns
21:42:50 Using Annoy for neighbor search, n_neighbors = 11
21:42:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756bae67e
21:42:51 Searching Annoy index using 1 thread, search_k = 1100
21:42:51 Annoy recall = 100%
21:42:52 Commencing smooth kNN distance calibration using 1 thread
21:42:53 Initializing from normalized Laplacian + noise
21:42:53 Commencing optimization for 500 epochs, with 16432 positive edges
21:42:56 Optimization finished
[1] "11 0.12"21:42:56 UMAP embedding parameters a = 1.51 b = 0.9165
21:42:56 Read 1203 rows and found 38 numeric columns
21:42:56 Using Annoy for neighbor search, n_neighbors = 11
21:42:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d47d066
21:42:57 Searching Annoy index using 1 thread, search_k = 1100
21:42:57 Annoy recall = 100%
21:42:58 Commencing smooth kNN distance calibration using 1 thread
21:43:00 Initializing from normalized Laplacian + noise
21:43:00 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:02 Optimization finished
[1] "11 0.13"21:43:02 UMAP embedding parameters a = 1.478 b = 0.9272
21:43:02 Read 1203 rows and found 38 numeric columns
21:43:02 Using Annoy for neighbor search, n_neighbors = 11
21:43:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777223fa2
21:43:03 Searching Annoy index using 1 thread, search_k = 1100
21:43:03 Annoy recall = 100%
21:43:04 Commencing smooth kNN distance calibration using 1 thread
21:43:06 Initializing from normalized Laplacian + noise
21:43:06 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:08 Optimization finished
[1] "11 0.14"21:43:09 UMAP embedding parameters a = 1.446 b = 0.938
21:43:09 Read 1203 rows and found 38 numeric columns
21:43:09 Using Annoy for neighbor search, n_neighbors = 11
21:43:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871370e0e2
21:43:09 Searching Annoy index using 1 thread, search_k = 1100
21:43:09 Annoy recall = 100%
21:43:10 Commencing smooth kNN distance calibration using 1 thread
21:43:12 Initializing from normalized Laplacian + noise
21:43:12 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:15 Optimization finished
[1] "11 0.15"21:43:15 UMAP embedding parameters a = 1.414 b = 0.9488
21:43:15 Read 1203 rows and found 38 numeric columns
21:43:15 Using Annoy for neighbor search, n_neighbors = 11
21:43:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754acc2c7
21:43:15 Searching Annoy index using 1 thread, search_k = 1100
21:43:15 Annoy recall = 100%
21:43:16 Commencing smooth kNN distance calibration using 1 thread
21:43:18 Initializing from normalized Laplacian + noise
21:43:18 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:21 Optimization finished
[1] "11 0.16"21:43:21 UMAP embedding parameters a = 1.383 b = 0.9596
21:43:21 Read 1203 rows and found 38 numeric columns
21:43:21 Using Annoy for neighbor search, n_neighbors = 11
21:43:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874258e323
21:43:21 Searching Annoy index using 1 thread, search_k = 1100
21:43:21 Annoy recall = 100%
21:43:22 Commencing smooth kNN distance calibration using 1 thread
21:43:24 Initializing from normalized Laplacian + noise
21:43:24 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:27 Optimization finished
[1] "11 0.17"21:43:27 UMAP embedding parameters a = 1.352 b = 0.9704
21:43:27 Read 1203 rows and found 38 numeric columns
21:43:27 Using Annoy for neighbor search, n_neighbors = 11
21:43:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713f1f524
21:43:27 Searching Annoy index using 1 thread, search_k = 1100
21:43:27 Annoy recall = 100%
21:43:28 Commencing smooth kNN distance calibration using 1 thread
21:43:30 Initializing from normalized Laplacian + noise
21:43:30 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:33 Optimization finished
[1] "11 0.18"21:43:33 UMAP embedding parameters a = 1.321 b = 0.9813
21:43:33 Read 1203 rows and found 38 numeric columns
21:43:33 Using Annoy for neighbor search, n_neighbors = 11
21:43:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877139b0b
21:43:33 Searching Annoy index using 1 thread, search_k = 1100
21:43:34 Annoy recall = 100%
21:43:34 Commencing smooth kNN distance calibration using 1 thread
21:43:36 Initializing from normalized Laplacian + noise
21:43:36 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:39 Optimization finished
[1] "11 0.19"21:43:39 UMAP embedding parameters a = 1.292 b = 0.9921
21:43:39 Read 1203 rows and found 38 numeric columns
21:43:39 Using Annoy for neighbor search, n_neighbors = 11
21:43:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754af498
21:43:40 Searching Annoy index using 1 thread, search_k = 1100
21:43:40 Annoy recall = 100%
21:43:41 Commencing smooth kNN distance calibration using 1 thread
21:43:43 Initializing from normalized Laplacian + noise
21:43:43 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:45 Optimization finished
[1] "11 0.2"21:43:45 UMAP embedding parameters a = 1.262 b = 1.003
21:43:45 Read 1203 rows and found 38 numeric columns
21:43:45 Using Annoy for neighbor search, n_neighbors = 11
21:43:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766cef1c5
21:43:46 Searching Annoy index using 1 thread, search_k = 1100
21:43:46 Annoy recall = 100%
21:43:47 Commencing smooth kNN distance calibration using 1 thread
21:43:49 Initializing from normalized Laplacian + noise
21:43:49 Commencing optimization for 500 epochs, with 16432 positive edges
21:43:52 Optimization finished
[1] "12 0"21:43:52 UMAP embedding parameters a = 1.933 b = 0.7905
21:43:52 Read 1203 rows and found 38 numeric columns
21:43:52 Using Annoy for neighbor search, n_neighbors = 12
21:43:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762af5124
21:43:52 Searching Annoy index using 1 thread, search_k = 1200
21:43:52 Annoy recall = 100%
21:43:53 Commencing smooth kNN distance calibration using 1 thread
21:43:55 Initializing from normalized Laplacian + noise
21:43:55 Commencing optimization for 500 epochs, with 17994 positive edges
21:43:58 Optimization finished
[1] "12 0.01"21:43:58 UMAP embedding parameters a = 1.896 b = 0.8006
21:43:58 Read 1203 rows and found 38 numeric columns
21:43:58 Using Annoy for neighbor search, n_neighbors = 12
21:43:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a019f4f
21:43:59 Searching Annoy index using 1 thread, search_k = 1200
21:43:59 Annoy recall = 100%
21:44:00 Commencing smooth kNN distance calibration using 1 thread
21:44:02 Initializing from normalized Laplacian + noise
21:44:02 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:05 Optimization finished
[1] "12 0.02"21:44:05 UMAP embedding parameters a = 1.859 b = 0.8109
21:44:05 Read 1203 rows and found 38 numeric columns
21:44:05 Using Annoy for neighbor search, n_neighbors = 12
21:44:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872842ecb5
21:44:05 Searching Annoy index using 1 thread, search_k = 1200
21:44:05 Annoy recall = 100%
21:44:06 Commencing smooth kNN distance calibration using 1 thread
21:44:08 Initializing from normalized Laplacian + noise
21:44:08 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:11 Optimization finished
[1] "12 0.03"21:44:11 UMAP embedding parameters a = 1.822 b = 0.8212
21:44:11 Read 1203 rows and found 38 numeric columns
21:44:11 Using Annoy for neighbor search, n_neighbors = 12
21:44:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875dd15ac8
21:44:11 Searching Annoy index using 1 thread, search_k = 1200
21:44:12 Annoy recall = 100%
21:44:13 Commencing smooth kNN distance calibration using 1 thread
21:44:15 Initializing from normalized Laplacian + noise
21:44:15 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:17 Optimization finished
[1] "12 0.04"21:44:18 UMAP embedding parameters a = 1.786 b = 0.8316
21:44:18 Read 1203 rows and found 38 numeric columns
21:44:18 Using Annoy for neighbor search, n_neighbors = 12
21:44:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a993ef9
21:44:18 Searching Annoy index using 1 thread, search_k = 1200
21:44:18 Annoy recall = 100%
21:44:19 Commencing smooth kNN distance calibration using 1 thread
21:44:21 Initializing from normalized Laplacian + noise
21:44:21 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:24 Optimization finished
[1] "12 0.05"21:44:24 UMAP embedding parameters a = 1.75 b = 0.8421
21:44:24 Read 1203 rows and found 38 numeric columns
21:44:24 Using Annoy for neighbor search, n_neighbors = 12
21:44:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bf99fe6
21:44:24 Searching Annoy index using 1 thread, search_k = 1200
21:44:24 Annoy recall = 100%
21:44:25 Commencing smooth kNN distance calibration using 1 thread
21:44:27 Initializing from normalized Laplacian + noise
21:44:27 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:30 Optimization finished
[1] "12 0.06"21:44:30 UMAP embedding parameters a = 1.715 b = 0.8526
21:44:30 Read 1203 rows and found 38 numeric columns
21:44:30 Using Annoy for neighbor search, n_neighbors = 12
21:44:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871247b55b
21:44:30 Searching Annoy index using 1 thread, search_k = 1200
21:44:31 Annoy recall = 100%
21:44:32 Commencing smooth kNN distance calibration using 1 thread
21:44:34 Initializing from normalized Laplacian + noise
21:44:34 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:36 Optimization finished
[1] "12 0.07"21:44:37 UMAP embedding parameters a = 1.68 b = 0.8631
21:44:37 Read 1203 rows and found 38 numeric columns
21:44:37 Using Annoy for neighbor search, n_neighbors = 12
21:44:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fea1313
21:44:37 Searching Annoy index using 1 thread, search_k = 1200
21:44:37 Annoy recall = 100%
21:44:38 Commencing smooth kNN distance calibration using 1 thread
21:44:40 Initializing from normalized Laplacian + noise
21:44:40 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:43 Optimization finished
[1] "12 0.08"21:44:43 UMAP embedding parameters a = 1.645 b = 0.8737
21:44:43 Read 1203 rows and found 38 numeric columns
21:44:43 Using Annoy for neighbor search, n_neighbors = 12
21:44:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744f1e3ea
21:44:44 Searching Annoy index using 1 thread, search_k = 1200
21:44:44 Annoy recall = 100%
21:44:45 Commencing smooth kNN distance calibration using 1 thread
21:44:47 Initializing from normalized Laplacian + noise
21:44:47 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:50 Optimization finished
[1] "12 0.09"21:44:50 UMAP embedding parameters a = 1.611 b = 0.8844
21:44:50 Read 1203 rows and found 38 numeric columns
21:44:50 Using Annoy for neighbor search, n_neighbors = 12
21:44:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727affe89
21:44:50 Searching Annoy index using 1 thread, search_k = 1200
21:44:50 Annoy recall = 100%
21:44:51 Commencing smooth kNN distance calibration using 1 thread
21:44:53 Initializing from normalized Laplacian + noise
21:44:53 Commencing optimization for 500 epochs, with 17994 positive edges
21:44:56 Optimization finished
[1] "12 0.1"21:44:56 UMAP embedding parameters a = 1.577 b = 0.8951
21:44:56 Read 1203 rows and found 38 numeric columns
21:44:56 Using Annoy for neighbor search, n_neighbors = 12
21:44:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f6ea27e
21:44:57 Searching Annoy index using 1 thread, search_k = 1200
21:44:57 Annoy recall = 100%
21:44:58 Commencing smooth kNN distance calibration using 1 thread
21:45:00 Initializing from normalized Laplacian + noise
21:45:00 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:03 Optimization finished
[1] "12 0.11"21:45:03 UMAP embedding parameters a = 1.544 b = 0.9058
21:45:03 Read 1203 rows and found 38 numeric columns
21:45:03 Using Annoy for neighbor search, n_neighbors = 12
21:45:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765983401
21:45:03 Searching Annoy index using 1 thread, search_k = 1200
21:45:03 Annoy recall = 100%
21:45:04 Commencing smooth kNN distance calibration using 1 thread
21:45:06 Initializing from normalized Laplacian + noise
21:45:06 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:09 Optimization finished
[1] "12 0.12"21:45:09 UMAP embedding parameters a = 1.51 b = 0.9165
21:45:09 Read 1203 rows and found 38 numeric columns
21:45:09 Using Annoy for neighbor search, n_neighbors = 12
21:45:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87507141ef
21:45:10 Searching Annoy index using 1 thread, search_k = 1200
21:45:10 Annoy recall = 100%
21:45:11 Commencing smooth kNN distance calibration using 1 thread
21:45:13 Initializing from normalized Laplacian + noise
21:45:13 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:16 Optimization finished
[1] "12 0.13"21:45:16 UMAP embedding parameters a = 1.478 b = 0.9272
21:45:16 Read 1203 rows and found 38 numeric columns
21:45:16 Using Annoy for neighbor search, n_neighbors = 12
21:45:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876efb3701
21:45:16 Searching Annoy index using 1 thread, search_k = 1200
21:45:16 Annoy recall = 100%
21:45:17 Commencing smooth kNN distance calibration using 1 thread
21:45:19 Initializing from normalized Laplacian + noise
21:45:19 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:22 Optimization finished
[1] "12 0.14"21:45:22 UMAP embedding parameters a = 1.446 b = 0.938
21:45:22 Read 1203 rows and found 38 numeric columns
21:45:22 Using Annoy for neighbor search, n_neighbors = 12
21:45:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a760789
21:45:23 Searching Annoy index using 1 thread, search_k = 1200
21:45:23 Annoy recall = 100%
21:45:24 Commencing smooth kNN distance calibration using 1 thread
21:45:26 Initializing from normalized Laplacian + noise
21:45:26 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:29 Optimization finished
[1] "12 0.15"21:45:29 UMAP embedding parameters a = 1.414 b = 0.9488
21:45:29 Read 1203 rows and found 38 numeric columns
21:45:29 Using Annoy for neighbor search, n_neighbors = 12
21:45:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f22ee43
21:45:29 Searching Annoy index using 1 thread, search_k = 1200
21:45:29 Annoy recall = 100%
21:45:30 Commencing smooth kNN distance calibration using 1 thread
21:45:32 Initializing from normalized Laplacian + noise
21:45:32 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:35 Optimization finished
[1] "12 0.16"21:45:35 UMAP embedding parameters a = 1.383 b = 0.9596
21:45:35 Read 1203 rows and found 38 numeric columns
21:45:35 Using Annoy for neighbor search, n_neighbors = 12
21:45:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779593e2d
21:45:35 Searching Annoy index using 1 thread, search_k = 1200
21:45:36 Annoy recall = 100%
21:45:37 Commencing smooth kNN distance calibration using 1 thread
21:45:39 Initializing from normalized Laplacian + noise
21:45:39 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:42 Optimization finished
[1] "12 0.17"21:45:42 UMAP embedding parameters a = 1.352 b = 0.9704
21:45:42 Read 1203 rows and found 38 numeric columns
21:45:42 Using Annoy for neighbor search, n_neighbors = 12
21:45:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87729906b5
21:45:42 Searching Annoy index using 1 thread, search_k = 1200
21:45:42 Annoy recall = 100%
21:45:43 Commencing smooth kNN distance calibration using 1 thread
21:45:45 Initializing from normalized Laplacian + noise
21:45:45 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:48 Optimization finished
[1] "12 0.18"21:45:48 UMAP embedding parameters a = 1.321 b = 0.9813
21:45:48 Read 1203 rows and found 38 numeric columns
21:45:48 Using Annoy for neighbor search, n_neighbors = 12
21:45:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745b1d080
21:45:49 Searching Annoy index using 1 thread, search_k = 1200
21:45:49 Annoy recall = 100%
21:45:50 Commencing smooth kNN distance calibration using 1 thread
21:45:52 Initializing from normalized Laplacian + noise
21:45:52 Commencing optimization for 500 epochs, with 17994 positive edges
21:45:55 Optimization finished
[1] "12 0.19"21:45:55 UMAP embedding parameters a = 1.292 b = 0.9921
21:45:55 Read 1203 rows and found 38 numeric columns
21:45:55 Using Annoy for neighbor search, n_neighbors = 12
21:45:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87680bc2f2
21:45:55 Searching Annoy index using 1 thread, search_k = 1200
21:45:55 Annoy recall = 100%
21:45:56 Commencing smooth kNN distance calibration using 1 thread
21:45:58 Initializing from normalized Laplacian + noise
21:45:58 Commencing optimization for 500 epochs, with 17994 positive edges
21:46:01 Optimization finished
[1] "12 0.2"21:46:02 UMAP embedding parameters a = 1.262 b = 1.003
21:46:02 Read 1203 rows and found 38 numeric columns
21:46:02 Using Annoy for neighbor search, n_neighbors = 12
21:46:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ca6bfce
21:46:02 Searching Annoy index using 1 thread, search_k = 1200
21:46:02 Annoy recall = 100%
21:46:03 Commencing smooth kNN distance calibration using 1 thread
21:46:05 Initializing from normalized Laplacian + noise
21:46:05 Commencing optimization for 500 epochs, with 17994 positive edges
21:46:08 Optimization finished
[1] "13 0"21:46:08 UMAP embedding parameters a = 1.933 b = 0.7905
21:46:08 Read 1203 rows and found 38 numeric columns
21:46:08 Using Annoy for neighbor search, n_neighbors = 13
21:46:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c6cb6fe
21:46:08 Searching Annoy index using 1 thread, search_k = 1300
21:46:09 Annoy recall = 100%
21:46:10 Commencing smooth kNN distance calibration using 1 thread
21:46:12 Initializing from normalized Laplacian + noise
21:46:12 Commencing optimization for 500 epochs, with 19558 positive edges
21:46:15 Optimization finished
[1] "13 0.01"21:46:15 UMAP embedding parameters a = 1.896 b = 0.8006
21:46:15 Read 1203 rows and found 38 numeric columns
21:46:15 Using Annoy for neighbor search, n_neighbors = 13
21:46:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765539358
21:46:15 Searching Annoy index using 1 thread, search_k = 1300
21:46:15 Annoy recall = 100%
21:46:16 Commencing smooth kNN distance calibration using 1 thread
21:46:18 Initializing from normalized Laplacian + noise
21:46:18 Commencing optimization for 500 epochs, with 19558 positive edges
21:46:21 Optimization finished
[1] "13 0.02"21:46:21 UMAP embedding parameters a = 1.859 b = 0.8109
21:46:21 Read 1203 rows and found 38 numeric columns
21:46:21 Using Annoy for neighbor search, n_neighbors = 13
21:46:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c8ff70
21:46:22 Searching Annoy index using 1 thread, search_k = 1300
21:46:22 Annoy recall = 100%
21:46:23 Commencing smooth kNN distance calibration using 1 thread
21:46:25 Initializing from normalized Laplacian + noise
21:46:25 Commencing optimization for 500 epochs, with 19558 positive edges
21:46:28 Optimization finished
[1] "13 0.03"21:46:28 UMAP embedding parameters a = 1.822 b = 0.8212
21:46:28 Read 1203 rows and found 38 numeric columns
21:46:28 Using Annoy for neighbor search, n_neighbors = 13
21:46:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fdd97e0
21:46:28 Searching Annoy index using 1 thread, search_k = 1300
21:46:29 Annoy recall = 100%
21:46:30 Commencing smooth kNN distance calibration using 1 thread
21:46:32 Initializing from normalized Laplacian + noise
21:46:32 Commencing optimization for 500 epochs, with 19558 positive edges
21:46:35 Optimization finished
[1] "13 0.04"21:46:35 UMAP embedding parameters a = 1.786 b = 0.8316
21:46:35 Read 1203 rows and found 38 numeric columns
21:46:35 Using Annoy for neighbor search, n_neighbors = 13
21:46:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a00561f
21:46:35 Searching Annoy index using 1 thread, search_k = 1300
21:46:35 Annoy recall = 100%
21:46:36 Commencing smooth kNN distance calibration using 1 thread
21:46:38 Initializing from normalized Laplacian + noise
21:46:38 Commencing optimization for 500 epochs, with 19558 positive edges
21:46:41 Optimization finished
[1] "13 0.05"21:46:42 UMAP embedding parameters a = 1.75 b = 0.8421
21:46:42 Read 1203 rows and found 38 numeric columns
21:46:42 Using Annoy for neighbor search, n_neighbors = 13
21:46:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874621e293
21:46:42 Searching Annoy index using 1 thread, search_k = 1300
21:46:42 Annoy recall = 100%
21:46:43 Commencing smooth kNN distance calibration using 1 thread
21:46:45 Initializing from normalized Laplacian + noise
21:46:45 Commencing optimization for 500 epochs, with 19558 positive edges
21:46:48 Optimization finished
[1] "13 0.06"21:46:48 UMAP embedding parameters a = 1.715 b = 0.8526
21:46:48 Read 1203 rows and found 38 numeric columns
21:46:48 Using Annoy for neighbor search, n_neighbors = 13
21:46:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743cf8d04
21:46:49 Searching Annoy index using 1 thread, search_k = 1300
21:46:49 Annoy recall = 100%
21:46:50 Commencing smooth kNN distance calibration using 1 thread
21:46:52 Initializing from normalized Laplacian + noise
21:46:52 Commencing optimization for 500 epochs, with 19558 positive edges
21:46:55 Optimization finished
[1] "13 0.07"21:46:55 UMAP embedding parameters a = 1.68 b = 0.8631
21:46:55 Read 1203 rows and found 38 numeric columns
21:46:55 Using Annoy for neighbor search, n_neighbors = 13
21:46:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874113f12a
21:46:55 Searching Annoy index using 1 thread, search_k = 1300
21:46:56 Annoy recall = 100%
21:46:57 Commencing smooth kNN distance calibration using 1 thread
21:46:59 Initializing from normalized Laplacian + noise
21:46:59 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:02 Optimization finished
[1] "13 0.08"21:47:02 UMAP embedding parameters a = 1.645 b = 0.8737
21:47:02 Read 1203 rows and found 38 numeric columns
21:47:02 Using Annoy for neighbor search, n_neighbors = 13
21:47:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b6cd72c
21:47:02 Searching Annoy index using 1 thread, search_k = 1300
21:47:02 Annoy recall = 100%
21:47:03 Commencing smooth kNN distance calibration using 1 thread
21:47:06 Initializing from normalized Laplacian + noise
21:47:06 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:09 Optimization finished
[1] "13 0.09"21:47:09 UMAP embedding parameters a = 1.611 b = 0.8844
21:47:09 Read 1203 rows and found 38 numeric columns
21:47:09 Using Annoy for neighbor search, n_neighbors = 13
21:47:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a9e7ec9
21:47:09 Searching Annoy index using 1 thread, search_k = 1300
21:47:09 Annoy recall = 100%
21:47:10 Commencing smooth kNN distance calibration using 1 thread
21:47:12 Initializing from normalized Laplacian + noise
21:47:12 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:15 Optimization finished
[1] "13 0.1"21:47:16 UMAP embedding parameters a = 1.577 b = 0.8951
21:47:16 Read 1203 rows and found 38 numeric columns
21:47:16 Using Annoy for neighbor search, n_neighbors = 13
21:47:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723c3424e
21:47:16 Searching Annoy index using 1 thread, search_k = 1300
21:47:16 Annoy recall = 100%
21:47:17 Commencing smooth kNN distance calibration using 1 thread
21:47:19 Initializing from normalized Laplacian + noise
21:47:19 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:22 Optimization finished
[1] "13 0.11"21:47:22 UMAP embedding parameters a = 1.544 b = 0.9058
21:47:22 Read 1203 rows and found 38 numeric columns
21:47:22 Using Annoy for neighbor search, n_neighbors = 13
21:47:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87356e767b
21:47:23 Searching Annoy index using 1 thread, search_k = 1300
21:47:23 Annoy recall = 100%
21:47:24 Commencing smooth kNN distance calibration using 1 thread
21:47:26 Initializing from normalized Laplacian + noise
21:47:26 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:29 Optimization finished
[1] "13 0.12"21:47:29 UMAP embedding parameters a = 1.51 b = 0.9165
21:47:29 Read 1203 rows and found 38 numeric columns
21:47:29 Using Annoy for neighbor search, n_neighbors = 13
21:47:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752e16b7e
21:47:29 Searching Annoy index using 1 thread, search_k = 1300
21:47:30 Annoy recall = 100%
21:47:31 Commencing smooth kNN distance calibration using 1 thread
21:47:33 Initializing from normalized Laplacian + noise
21:47:33 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:36 Optimization finished
[1] "13 0.13"21:47:36 UMAP embedding parameters a = 1.478 b = 0.9272
21:47:36 Read 1203 rows and found 38 numeric columns
21:47:36 Using Annoy for neighbor search, n_neighbors = 13
21:47:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871949d16
21:47:36 Searching Annoy index using 1 thread, search_k = 1300
21:47:36 Annoy recall = 100%
21:47:37 Commencing smooth kNN distance calibration using 1 thread
21:47:40 Initializing from normalized Laplacian + noise
21:47:40 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:43 Optimization finished
[1] "13 0.14"21:47:43 UMAP embedding parameters a = 1.446 b = 0.938
21:47:43 Read 1203 rows and found 38 numeric columns
21:47:43 Using Annoy for neighbor search, n_neighbors = 13
21:47:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873007b575
21:47:43 Searching Annoy index using 1 thread, search_k = 1300
21:47:43 Annoy recall = 100%
21:47:44 Commencing smooth kNN distance calibration using 1 thread
21:47:46 Initializing from normalized Laplacian + noise
21:47:46 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:49 Optimization finished
[1] "13 0.15"21:47:50 UMAP embedding parameters a = 1.414 b = 0.9488
21:47:50 Read 1203 rows and found 38 numeric columns
21:47:50 Using Annoy for neighbor search, n_neighbors = 13
21:47:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875edb0b65
21:47:50 Searching Annoy index using 1 thread, search_k = 1300
21:47:50 Annoy recall = 100%
21:47:51 Commencing smooth kNN distance calibration using 1 thread
21:47:53 Initializing from normalized Laplacian + noise
21:47:53 Commencing optimization for 500 epochs, with 19558 positive edges
21:47:56 Optimization finished
[1] "13 0.16"21:47:57 UMAP embedding parameters a = 1.383 b = 0.9596
21:47:57 Read 1203 rows and found 38 numeric columns
21:47:57 Using Annoy for neighbor search, n_neighbors = 13
21:47:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713dc5271
21:47:57 Searching Annoy index using 1 thread, search_k = 1300
21:47:57 Annoy recall = 100%
21:47:58 Commencing smooth kNN distance calibration using 1 thread
21:48:00 Initializing from normalized Laplacian + noise
21:48:00 Commencing optimization for 500 epochs, with 19558 positive edges
21:48:03 Optimization finished
[1] "13 0.17"21:48:03 UMAP embedding parameters a = 1.352 b = 0.9704
21:48:03 Read 1203 rows and found 38 numeric columns
21:48:03 Using Annoy for neighbor search, n_neighbors = 13
21:48:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ff1c888
21:48:04 Searching Annoy index using 1 thread, search_k = 1300
21:48:04 Annoy recall = 100%
21:48:05 Commencing smooth kNN distance calibration using 1 thread
21:48:07 Initializing from normalized Laplacian + noise
21:48:07 Commencing optimization for 500 epochs, with 19558 positive edges
21:48:10 Optimization finished
[1] "13 0.18"21:48:10 UMAP embedding parameters a = 1.321 b = 0.9813
21:48:10 Read 1203 rows and found 38 numeric columns
21:48:10 Using Annoy for neighbor search, n_neighbors = 13
21:48:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723ccef4f
21:48:11 Searching Annoy index using 1 thread, search_k = 1300
21:48:11 Annoy recall = 100%
21:48:12 Commencing smooth kNN distance calibration using 1 thread
21:48:14 Initializing from normalized Laplacian + noise
21:48:14 Commencing optimization for 500 epochs, with 19558 positive edges
21:48:17 Optimization finished
[1] "13 0.19"21:48:17 UMAP embedding parameters a = 1.292 b = 0.9921
21:48:17 Read 1203 rows and found 38 numeric columns
21:48:17 Using Annoy for neighbor search, n_neighbors = 13
21:48:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b8c50fa
21:48:18 Searching Annoy index using 1 thread, search_k = 1300
21:48:18 Annoy recall = 100%
21:48:19 Commencing smooth kNN distance calibration using 1 thread
21:48:21 Initializing from normalized Laplacian + noise
21:48:21 Commencing optimization for 500 epochs, with 19558 positive edges
21:48:24 Optimization finished
[1] "13 0.2"21:48:24 UMAP embedding parameters a = 1.262 b = 1.003
21:48:24 Read 1203 rows and found 38 numeric columns
21:48:24 Using Annoy for neighbor search, n_neighbors = 13
21:48:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f606b06
21:48:24 Searching Annoy index using 1 thread, search_k = 1300
21:48:25 Annoy recall = 100%
21:48:26 Commencing smooth kNN distance calibration using 1 thread
21:48:28 Initializing from normalized Laplacian + noise
21:48:28 Commencing optimization for 500 epochs, with 19558 positive edges
21:48:31 Optimization finished
[1] "14 0"21:48:31 UMAP embedding parameters a = 1.933 b = 0.7905
21:48:31 Read 1203 rows and found 38 numeric columns
21:48:31 Using Annoy for neighbor search, n_neighbors = 14
21:48:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879652350
21:48:31 Searching Annoy index using 1 thread, search_k = 1400
21:48:32 Annoy recall = 100%
21:48:33 Commencing smooth kNN distance calibration using 1 thread
21:48:35 Initializing from normalized Laplacian + noise
21:48:35 Commencing optimization for 500 epochs, with 21150 positive edges
21:48:38 Optimization finished
[1] "14 0.01"21:48:38 UMAP embedding parameters a = 1.896 b = 0.8006
21:48:38 Read 1203 rows and found 38 numeric columns
21:48:38 Using Annoy for neighbor search, n_neighbors = 14
21:48:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bfd92e9
21:48:38 Searching Annoy index using 1 thread, search_k = 1400
21:48:39 Annoy recall = 100%
21:48:40 Commencing smooth kNN distance calibration using 1 thread
21:48:42 Initializing from normalized Laplacian + noise
21:48:42 Commencing optimization for 500 epochs, with 21150 positive edges
21:48:45 Optimization finished
[1] "14 0.02"21:48:45 UMAP embedding parameters a = 1.859 b = 0.8109
21:48:45 Read 1203 rows and found 38 numeric columns
21:48:45 Using Annoy for neighbor search, n_neighbors = 14
21:48:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e5ba207
21:48:45 Searching Annoy index using 1 thread, search_k = 1400
21:48:46 Annoy recall = 100%
21:48:47 Commencing smooth kNN distance calibration using 1 thread
21:48:49 Initializing from normalized Laplacian + noise
21:48:49 Commencing optimization for 500 epochs, with 21150 positive edges
21:48:52 Optimization finished
[1] "14 0.03"21:48:52 UMAP embedding parameters a = 1.822 b = 0.8212
21:48:52 Read 1203 rows and found 38 numeric columns
21:48:52 Using Annoy for neighbor search, n_neighbors = 14
21:48:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763db2ad9
21:48:53 Searching Annoy index using 1 thread, search_k = 1400
21:48:53 Annoy recall = 100%
21:48:54 Commencing smooth kNN distance calibration using 1 thread
21:48:56 Initializing from normalized Laplacian + noise
21:48:56 Commencing optimization for 500 epochs, with 21150 positive edges
21:48:59 Optimization finished
[1] "14 0.04"21:48:59 UMAP embedding parameters a = 1.786 b = 0.8316
21:48:59 Read 1203 rows and found 38 numeric columns
21:48:59 Using Annoy for neighbor search, n_neighbors = 14
21:48:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b20812c
21:49:00 Searching Annoy index using 1 thread, search_k = 1400
21:49:00 Annoy recall = 100%
21:49:01 Commencing smooth kNN distance calibration using 1 thread
21:49:03 Initializing from normalized Laplacian + noise
21:49:03 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:06 Optimization finished
[1] "14 0.05"21:49:07 UMAP embedding parameters a = 1.75 b = 0.8421
21:49:07 Read 1203 rows and found 38 numeric columns
21:49:07 Using Annoy for neighbor search, n_neighbors = 14
21:49:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727b4e034
21:49:07 Searching Annoy index using 1 thread, search_k = 1400
21:49:07 Annoy recall = 100%
21:49:08 Commencing smooth kNN distance calibration using 1 thread
21:49:10 Initializing from normalized Laplacian + noise
21:49:10 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:13 Optimization finished
[1] "14 0.06"21:49:14 UMAP embedding parameters a = 1.715 b = 0.8526
21:49:14 Read 1203 rows and found 38 numeric columns
21:49:14 Using Annoy for neighbor search, n_neighbors = 14
21:49:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875674318f
21:49:14 Searching Annoy index using 1 thread, search_k = 1400
21:49:14 Annoy recall = 100%
21:49:15 Commencing smooth kNN distance calibration using 1 thread
21:49:17 Initializing from normalized Laplacian + noise
21:49:17 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:20 Optimization finished
[1] "14 0.07"21:49:21 UMAP embedding parameters a = 1.68 b = 0.8631
21:49:21 Read 1203 rows and found 38 numeric columns
21:49:21 Using Annoy for neighbor search, n_neighbors = 14
21:49:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770d251ad
21:49:21 Searching Annoy index using 1 thread, search_k = 1400
21:49:21 Annoy recall = 100%
21:49:22 Commencing smooth kNN distance calibration using 1 thread
21:49:25 Initializing from normalized Laplacian + noise
21:49:25 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:28 Optimization finished
[1] "14 0.08"21:49:28 UMAP embedding parameters a = 1.645 b = 0.8737
21:49:28 Read 1203 rows and found 38 numeric columns
21:49:28 Using Annoy for neighbor search, n_neighbors = 14
21:49:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fc0a326
21:49:28 Searching Annoy index using 1 thread, search_k = 1400
21:49:28 Annoy recall = 100%
21:49:29 Commencing smooth kNN distance calibration using 1 thread
21:49:32 Initializing from normalized Laplacian + noise
21:49:32 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:35 Optimization finished
[1] "14 0.09"21:49:35 UMAP embedding parameters a = 1.611 b = 0.8844
21:49:35 Read 1203 rows and found 38 numeric columns
21:49:35 Using Annoy for neighbor search, n_neighbors = 14
21:49:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87631af15d
21:49:35 Searching Annoy index using 1 thread, search_k = 1400
21:49:35 Annoy recall = 100%
21:49:37 Commencing smooth kNN distance calibration using 1 thread
21:49:39 Initializing from normalized Laplacian + noise
21:49:39 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:42 Optimization finished
[1] "14 0.1"21:49:42 UMAP embedding parameters a = 1.577 b = 0.8951
21:49:42 Read 1203 rows and found 38 numeric columns
21:49:42 Using Annoy for neighbor search, n_neighbors = 14
21:49:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d3f08ab
21:49:42 Searching Annoy index using 1 thread, search_k = 1400
21:49:43 Annoy recall = 100%
21:49:44 Commencing smooth kNN distance calibration using 1 thread
21:49:46 Initializing from normalized Laplacian + noise
21:49:46 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:49 Optimization finished
[1] "14 0.11"21:49:49 UMAP embedding parameters a = 1.544 b = 0.9058
21:49:49 Read 1203 rows and found 38 numeric columns
21:49:49 Using Annoy for neighbor search, n_neighbors = 14
21:49:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877514367e
21:49:50 Searching Annoy index using 1 thread, search_k = 1400
21:49:50 Annoy recall = 100%
21:49:51 Commencing smooth kNN distance calibration using 1 thread
21:49:53 Initializing from normalized Laplacian + noise
21:49:53 Commencing optimization for 500 epochs, with 21150 positive edges
21:49:56 Optimization finished
[1] "14 0.12"21:49:57 UMAP embedding parameters a = 1.51 b = 0.9165
21:49:57 Read 1203 rows and found 38 numeric columns
21:49:57 Using Annoy for neighbor search, n_neighbors = 14
21:49:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766e3f0cd
21:49:57 Searching Annoy index using 1 thread, search_k = 1400
21:49:57 Annoy recall = 100%
21:49:58 Commencing smooth kNN distance calibration using 1 thread
21:50:00 Initializing from normalized Laplacian + noise
21:50:00 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:04 Optimization finished
[1] "14 0.13"21:50:04 UMAP embedding parameters a = 1.478 b = 0.9272
21:50:04 Read 1203 rows and found 38 numeric columns
21:50:04 Using Annoy for neighbor search, n_neighbors = 14
21:50:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d1ca08c
21:50:04 Searching Annoy index using 1 thread, search_k = 1400
21:50:04 Annoy recall = 100%
21:50:05 Commencing smooth kNN distance calibration using 1 thread
21:50:08 Initializing from normalized Laplacian + noise
21:50:08 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:11 Optimization finished
[1] "14 0.14"21:50:11 UMAP embedding parameters a = 1.446 b = 0.938
21:50:11 Read 1203 rows and found 38 numeric columns
21:50:11 Using Annoy for neighbor search, n_neighbors = 14
21:50:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f148c9d
21:50:11 Searching Annoy index using 1 thread, search_k = 1400
21:50:11 Annoy recall = 100%
21:50:13 Commencing smooth kNN distance calibration using 1 thread
21:50:15 Initializing from normalized Laplacian + noise
21:50:15 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:18 Optimization finished
[1] "14 0.15"21:50:18 UMAP embedding parameters a = 1.414 b = 0.9488
21:50:18 Read 1203 rows and found 38 numeric columns
21:50:18 Using Annoy for neighbor search, n_neighbors = 14
21:50:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d05d360
21:50:18 Searching Annoy index using 1 thread, search_k = 1400
21:50:19 Annoy recall = 100%
21:50:20 Commencing smooth kNN distance calibration using 1 thread
21:50:22 Initializing from normalized Laplacian + noise
21:50:22 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:25 Optimization finished
[1] "14 0.16"21:50:25 UMAP embedding parameters a = 1.383 b = 0.9596
21:50:25 Read 1203 rows and found 38 numeric columns
21:50:25 Using Annoy for neighbor search, n_neighbors = 14
21:50:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ec2d90
21:50:26 Searching Annoy index using 1 thread, search_k = 1400
21:50:26 Annoy recall = 100%
21:50:27 Commencing smooth kNN distance calibration using 1 thread
21:50:29 Initializing from normalized Laplacian + noise
21:50:29 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:32 Optimization finished
[1] "14 0.17"21:50:33 UMAP embedding parameters a = 1.352 b = 0.9704
21:50:33 Read 1203 rows and found 38 numeric columns
21:50:33 Using Annoy for neighbor search, n_neighbors = 14
21:50:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770287dc7
21:50:33 Searching Annoy index using 1 thread, search_k = 1400
21:50:33 Annoy recall = 100%
21:50:34 Commencing smooth kNN distance calibration using 1 thread
21:50:37 Initializing from normalized Laplacian + noise
21:50:37 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:40 Optimization finished
[1] "14 0.18"21:50:40 UMAP embedding parameters a = 1.321 b = 0.9813
21:50:40 Read 1203 rows and found 38 numeric columns
21:50:40 Using Annoy for neighbor search, n_neighbors = 14
21:50:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877872aa8c
21:50:40 Searching Annoy index using 1 thread, search_k = 1400
21:50:40 Annoy recall = 100%
21:50:41 Commencing smooth kNN distance calibration using 1 thread
21:50:44 Initializing from normalized Laplacian + noise
21:50:44 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:47 Optimization finished
[1] "14 0.19"21:50:47 UMAP embedding parameters a = 1.292 b = 0.9921
21:50:47 Read 1203 rows and found 38 numeric columns
21:50:47 Using Annoy for neighbor search, n_neighbors = 14
21:50:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b8aac5a
21:50:47 Searching Annoy index using 1 thread, search_k = 1400
21:50:48 Annoy recall = 100%
21:50:49 Commencing smooth kNN distance calibration using 1 thread
21:50:51 Initializing from normalized Laplacian + noise
21:50:51 Commencing optimization for 500 epochs, with 21150 positive edges
21:50:54 Optimization finished
[1] "14 0.2"21:50:55 UMAP embedding parameters a = 1.262 b = 1.003
21:50:55 Read 1203 rows and found 38 numeric columns
21:50:55 Using Annoy for neighbor search, n_neighbors = 14
21:50:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713ebc015
21:50:55 Searching Annoy index using 1 thread, search_k = 1400
21:50:55 Annoy recall = 100%
21:50:56 Commencing smooth kNN distance calibration using 1 thread
21:50:58 Initializing from normalized Laplacian + noise
21:50:59 Commencing optimization for 500 epochs, with 21150 positive edges
21:51:02 Optimization finished
[1] "15 0"21:51:02 UMAP embedding parameters a = 1.933 b = 0.7905
21:51:02 Read 1203 rows and found 38 numeric columns
21:51:02 Using Annoy for neighbor search, n_neighbors = 15
21:51:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872de12108
21:51:02 Searching Annoy index using 1 thread, search_k = 1500
21:51:02 Annoy recall = 100%
21:51:03 Commencing smooth kNN distance calibration using 1 thread
21:51:06 Initializing from normalized Laplacian + noise
21:51:06 Commencing optimization for 500 epochs, with 22670 positive edges
21:51:09 Optimization finished
[1] "15 0.01"21:51:09 UMAP embedding parameters a = 1.896 b = 0.8006
21:51:09 Read 1203 rows and found 38 numeric columns
21:51:09 Using Annoy for neighbor search, n_neighbors = 15
21:51:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e6c17d8
21:51:09 Searching Annoy index using 1 thread, search_k = 1500
21:51:10 Annoy recall = 100%
21:51:11 Commencing smooth kNN distance calibration using 1 thread
21:51:13 Initializing from normalized Laplacian + noise
21:51:13 Commencing optimization for 500 epochs, with 22670 positive edges
21:51:16 Optimization finished
[1] "15 0.02"21:51:17 UMAP embedding parameters a = 1.859 b = 0.8109
21:51:17 Read 1203 rows and found 38 numeric columns
21:51:17 Using Annoy for neighbor search, n_neighbors = 15
21:51:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715805d2b
21:51:17 Searching Annoy index using 1 thread, search_k = 1500
21:51:17 Annoy recall = 100%
21:51:18 Commencing smooth kNN distance calibration using 1 thread
21:51:21 Initializing from normalized Laplacian + noise
21:51:21 Commencing optimization for 500 epochs, with 22670 positive edges
21:51:24 Optimization finished
[1] "15 0.03"21:51:24 UMAP embedding parameters a = 1.822 b = 0.8212
21:51:24 Read 1203 rows and found 38 numeric columns
21:51:24 Using Annoy for neighbor search, n_neighbors = 15
21:51:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875de8d67d
21:51:24 Searching Annoy index using 1 thread, search_k = 1500
21:51:24 Annoy recall = 100%
21:51:26 Commencing smooth kNN distance calibration using 1 thread
21:51:28 Initializing from normalized Laplacian + noise
21:51:28 Commencing optimization for 500 epochs, with 22670 positive edges
21:51:31 Optimization finished
[1] "15 0.04"21:51:31 UMAP embedding parameters a = 1.786 b = 0.8316
21:51:31 Read 1203 rows and found 38 numeric columns
21:51:31 Using Annoy for neighbor search, n_neighbors = 15
21:51:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d47233d
21:51:32 Searching Annoy index using 1 thread, search_k = 1500
21:51:32 Annoy recall = 100%
21:51:33 Commencing smooth kNN distance calibration using 1 thread
21:51:35 Initializing from normalized Laplacian + noise
21:51:35 Commencing optimization for 500 epochs, with 22670 positive edges
21:51:39 Optimization finished
[1] "15 0.05"21:51:39 UMAP embedding parameters a = 1.75 b = 0.8421
21:51:39 Read 1203 rows and found 38 numeric columns
21:51:39 Using Annoy for neighbor search, n_neighbors = 15
21:51:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87295caf9c
21:51:39 Searching Annoy index using 1 thread, search_k = 1500
21:51:39 Annoy recall = 100%
21:51:41 Commencing smooth kNN distance calibration using 1 thread
21:51:43 Initializing from normalized Laplacian + noise
21:51:43 Commencing optimization for 500 epochs, with 22670 positive edges
21:51:46 Optimization finished
[1] "15 0.06"21:51:46 UMAP embedding parameters a = 1.715 b = 0.8526
21:51:46 Read 1203 rows and found 38 numeric columns
21:51:46 Using Annoy for neighbor search, n_neighbors = 15
21:51:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dda9f05
21:51:47 Searching Annoy index using 1 thread, search_k = 1500
21:51:47 Annoy recall = 100%
21:51:48 Commencing smooth kNN distance calibration using 1 thread
21:51:50 Initializing from normalized Laplacian + noise
21:51:50 Commencing optimization for 500 epochs, with 22670 positive edges
21:51:54 Optimization finished
[1] "15 0.07"21:51:54 UMAP embedding parameters a = 1.68 b = 0.8631
21:51:54 Read 1203 rows and found 38 numeric columns
21:51:54 Using Annoy for neighbor search, n_neighbors = 15
21:51:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87114128c
21:51:54 Searching Annoy index using 1 thread, search_k = 1500
21:51:54 Annoy recall = 100%
21:51:56 Commencing smooth kNN distance calibration using 1 thread
21:51:58 Initializing from normalized Laplacian + noise
21:51:58 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:01 Optimization finished
[1] "15 0.08"21:52:01 UMAP embedding parameters a = 1.645 b = 0.8737
21:52:01 Read 1203 rows and found 38 numeric columns
21:52:01 Using Annoy for neighbor search, n_neighbors = 15
21:52:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764e90096
21:52:02 Searching Annoy index using 1 thread, search_k = 1500
21:52:02 Annoy recall = 100%
21:52:03 Commencing smooth kNN distance calibration using 1 thread
21:52:05 Initializing from normalized Laplacian + noise
21:52:05 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:09 Optimization finished
[1] "15 0.09"21:52:09 UMAP embedding parameters a = 1.611 b = 0.8844
21:52:09 Read 1203 rows and found 38 numeric columns
21:52:09 Using Annoy for neighbor search, n_neighbors = 15
21:52:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d3b0a0b
21:52:09 Searching Annoy index using 1 thread, search_k = 1500
21:52:09 Annoy recall = 100%
21:52:10 Commencing smooth kNN distance calibration using 1 thread
21:52:13 Initializing from normalized Laplacian + noise
21:52:13 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:16 Optimization finished
[1] "15 0.1"21:52:16 UMAP embedding parameters a = 1.577 b = 0.8951
21:52:16 Read 1203 rows and found 38 numeric columns
21:52:16 Using Annoy for neighbor search, n_neighbors = 15
21:52:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a7935dd
21:52:17 Searching Annoy index using 1 thread, search_k = 1500
21:52:17 Annoy recall = 100%
21:52:18 Commencing smooth kNN distance calibration using 1 thread
21:52:20 Initializing from normalized Laplacian + noise
21:52:20 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:24 Optimization finished
[1] "15 0.11"21:52:24 UMAP embedding parameters a = 1.544 b = 0.9058
21:52:24 Read 1203 rows and found 38 numeric columns
21:52:24 Using Annoy for neighbor search, n_neighbors = 15
21:52:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770e69380
21:52:24 Searching Annoy index using 1 thread, search_k = 1500
21:52:24 Annoy recall = 100%
21:52:26 Commencing smooth kNN distance calibration using 1 thread
21:52:28 Initializing from normalized Laplacian + noise
21:52:28 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:31 Optimization finished
[1] "15 0.12"21:52:31 UMAP embedding parameters a = 1.51 b = 0.9165
21:52:31 Read 1203 rows and found 38 numeric columns
21:52:31 Using Annoy for neighbor search, n_neighbors = 15
21:52:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b96ac12
21:52:32 Searching Annoy index using 1 thread, search_k = 1500
21:52:32 Annoy recall = 100%
21:52:33 Commencing smooth kNN distance calibration using 1 thread
21:52:35 Initializing from normalized Laplacian + noise
21:52:36 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:39 Optimization finished
[1] "15 0.13"21:52:39 UMAP embedding parameters a = 1.478 b = 0.9272
21:52:39 Read 1203 rows and found 38 numeric columns
21:52:39 Using Annoy for neighbor search, n_neighbors = 15
21:52:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e5460b6
21:52:39 Searching Annoy index using 1 thread, search_k = 1500
21:52:39 Annoy recall = 100%
21:52:41 Commencing smooth kNN distance calibration using 1 thread
21:52:43 Initializing from normalized Laplacian + noise
21:52:43 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:46 Optimization finished
[1] "15 0.14"21:52:47 UMAP embedding parameters a = 1.446 b = 0.938
21:52:47 Read 1203 rows and found 38 numeric columns
21:52:47 Using Annoy for neighbor search, n_neighbors = 15
21:52:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c0714ac
21:52:47 Searching Annoy index using 1 thread, search_k = 1500
21:52:47 Annoy recall = 100%
21:52:48 Commencing smooth kNN distance calibration using 1 thread
21:52:51 Initializing from normalized Laplacian + noise
21:52:51 Commencing optimization for 500 epochs, with 22670 positive edges
21:52:54 Optimization finished
[1] "15 0.15"21:52:54 UMAP embedding parameters a = 1.414 b = 0.9488
21:52:54 Read 1203 rows and found 38 numeric columns
21:52:54 Using Annoy for neighbor search, n_neighbors = 15
21:52:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87234b8c46
21:52:54 Searching Annoy index using 1 thread, search_k = 1500
21:52:55 Annoy recall = 100%
21:52:56 Commencing smooth kNN distance calibration using 1 thread
21:52:58 Initializing from normalized Laplacian + noise
21:52:58 Commencing optimization for 500 epochs, with 22670 positive edges
21:53:01 Optimization finished
[1] "15 0.16"21:53:02 UMAP embedding parameters a = 1.383 b = 0.9596
21:53:02 Read 1203 rows and found 38 numeric columns
21:53:02 Using Annoy for neighbor search, n_neighbors = 15
21:53:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744c89245
21:53:02 Searching Annoy index using 1 thread, search_k = 1500
21:53:02 Annoy recall = 100%
21:53:03 Commencing smooth kNN distance calibration using 1 thread
21:53:06 Initializing from normalized Laplacian + noise
21:53:06 Commencing optimization for 500 epochs, with 22670 positive edges
21:53:09 Optimization finished
[1] "15 0.17"21:53:09 UMAP embedding parameters a = 1.352 b = 0.9704
21:53:09 Read 1203 rows and found 38 numeric columns
21:53:09 Using Annoy for neighbor search, n_neighbors = 15
21:53:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cd96659
21:53:10 Searching Annoy index using 1 thread, search_k = 1500
21:53:10 Annoy recall = 100%
21:53:11 Commencing smooth kNN distance calibration using 1 thread
21:53:13 Initializing from normalized Laplacian + noise
21:53:13 Commencing optimization for 500 epochs, with 22670 positive edges
21:53:17 Optimization finished
[1] "15 0.18"21:53:17 UMAP embedding parameters a = 1.321 b = 0.9813
21:53:17 Read 1203 rows and found 38 numeric columns
21:53:17 Using Annoy for neighbor search, n_neighbors = 15
21:53:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87330c2f6c
21:53:17 Searching Annoy index using 1 thread, search_k = 1500
21:53:17 Annoy recall = 100%
21:53:19 Commencing smooth kNN distance calibration using 1 thread
21:53:21 Initializing from normalized Laplacian + noise
21:53:21 Commencing optimization for 500 epochs, with 22670 positive edges
21:53:24 Optimization finished
[1] "15 0.19"21:53:25 UMAP embedding parameters a = 1.292 b = 0.9921
21:53:25 Read 1203 rows and found 38 numeric columns
21:53:25 Using Annoy for neighbor search, n_neighbors = 15
21:53:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727e383a2
21:53:25 Searching Annoy index using 1 thread, search_k = 1500
21:53:25 Annoy recall = 100%
21:53:26 Commencing smooth kNN distance calibration using 1 thread
21:53:29 Initializing from normalized Laplacian + noise
21:53:29 Commencing optimization for 500 epochs, with 22670 positive edges
21:53:32 Optimization finished
[1] "15 0.2"21:53:32 UMAP embedding parameters a = 1.262 b = 1.003
21:53:32 Read 1203 rows and found 38 numeric columns
21:53:32 Using Annoy for neighbor search, n_neighbors = 15
21:53:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a186f05
21:53:32 Searching Annoy index using 1 thread, search_k = 1500
21:53:33 Annoy recall = 100%
21:53:34 Commencing smooth kNN distance calibration using 1 thread
21:53:36 Initializing from normalized Laplacian + noise
21:53:36 Commencing optimization for 500 epochs, with 22670 positive edges
21:53:40 Optimization finished
[1] "16 0"21:53:40 UMAP embedding parameters a = 1.933 b = 0.7905
21:53:40 Read 1203 rows and found 38 numeric columns
21:53:40 Using Annoy for neighbor search, n_neighbors = 16
21:53:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87282065ea
21:53:40 Searching Annoy index using 1 thread, search_k = 1600
21:53:40 Annoy recall = 100%
21:53:42 Commencing smooth kNN distance calibration using 1 thread
21:53:44 Initializing from normalized Laplacian + noise
21:53:44 Commencing optimization for 500 epochs, with 24158 positive edges
21:53:47 Optimization finished
[1] "16 0.01"21:53:48 UMAP embedding parameters a = 1.896 b = 0.8006
21:53:48 Read 1203 rows and found 38 numeric columns
21:53:48 Using Annoy for neighbor search, n_neighbors = 16
21:53:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ec7746f
21:53:48 Searching Annoy index using 1 thread, search_k = 1600
21:53:48 Annoy recall = 100%
21:53:49 Commencing smooth kNN distance calibration using 1 thread
21:53:52 Initializing from normalized Laplacian + noise
21:53:52 Commencing optimization for 500 epochs, with 24158 positive edges
21:53:55 Optimization finished
[1] "16 0.02"21:53:55 UMAP embedding parameters a = 1.859 b = 0.8109
21:53:55 Read 1203 rows and found 38 numeric columns
21:53:55 Using Annoy for neighbor search, n_neighbors = 16
21:53:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757350f91
21:53:56 Searching Annoy index using 1 thread, search_k = 1600
21:53:56 Annoy recall = 100%
21:53:57 Commencing smooth kNN distance calibration using 1 thread
21:54:00 Initializing from normalized Laplacian + noise
21:54:00 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:03 Optimization finished
[1] "16 0.03"21:54:03 UMAP embedding parameters a = 1.822 b = 0.8212
21:54:03 Read 1203 rows and found 38 numeric columns
21:54:03 Using Annoy for neighbor search, n_neighbors = 16
21:54:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875734f287
21:54:03 Searching Annoy index using 1 thread, search_k = 1600
21:54:03 Annoy recall = 100%
21:54:05 Commencing smooth kNN distance calibration using 1 thread
21:54:07 Initializing from normalized Laplacian + noise
21:54:07 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:11 Optimization finished
[1] "16 0.04"21:54:11 UMAP embedding parameters a = 1.786 b = 0.8316
21:54:11 Read 1203 rows and found 38 numeric columns
21:54:11 Using Annoy for neighbor search, n_neighbors = 16
21:54:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bcd47d0
21:54:11 Searching Annoy index using 1 thread, search_k = 1600
21:54:11 Annoy recall = 100%
21:54:12 Commencing smooth kNN distance calibration using 1 thread
21:54:15 Initializing from normalized Laplacian + noise
21:54:15 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:18 Optimization finished
[1] "16 0.05"21:54:19 UMAP embedding parameters a = 1.75 b = 0.8421
21:54:19 Read 1203 rows and found 38 numeric columns
21:54:19 Using Annoy for neighbor search, n_neighbors = 16
21:54:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758213d21
21:54:19 Searching Annoy index using 1 thread, search_k = 1600
21:54:19 Annoy recall = 100%
21:54:20 Commencing smooth kNN distance calibration using 1 thread
21:54:23 Initializing from normalized Laplacian + noise
21:54:23 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:26 Optimization finished
[1] "16 0.06"21:54:26 UMAP embedding parameters a = 1.715 b = 0.8526
21:54:26 Read 1203 rows and found 38 numeric columns
21:54:26 Using Annoy for neighbor search, n_neighbors = 16
21:54:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87475d704e
21:54:27 Searching Annoy index using 1 thread, search_k = 1600
21:54:27 Annoy recall = 100%
21:54:28 Commencing smooth kNN distance calibration using 1 thread
21:54:31 Initializing from normalized Laplacian + noise
21:54:31 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:34 Optimization finished
[1] "16 0.07"21:54:34 UMAP embedding parameters a = 1.68 b = 0.8631
21:54:34 Read 1203 rows and found 38 numeric columns
21:54:34 Using Annoy for neighbor search, n_neighbors = 16
21:54:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87343ff25c
21:54:34 Searching Annoy index using 1 thread, search_k = 1600
21:54:35 Annoy recall = 100%
21:54:36 Commencing smooth kNN distance calibration using 1 thread
21:54:38 Initializing from normalized Laplacian + noise
21:54:39 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:42 Optimization finished
[1] "16 0.08"21:54:42 UMAP embedding parameters a = 1.645 b = 0.8737
21:54:42 Read 1203 rows and found 38 numeric columns
21:54:42 Using Annoy for neighbor search, n_neighbors = 16
21:54:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873abe97b
21:54:42 Searching Annoy index using 1 thread, search_k = 1600
21:54:42 Annoy recall = 100%
21:54:44 Commencing smooth kNN distance calibration using 1 thread
21:54:46 Initializing from normalized Laplacian + noise
21:54:46 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:50 Optimization finished
[1] "16 0.09"21:54:50 UMAP embedding parameters a = 1.611 b = 0.8844
21:54:50 Read 1203 rows and found 38 numeric columns
21:54:50 Using Annoy for neighbor search, n_neighbors = 16
21:54:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b493063
21:54:50 Searching Annoy index using 1 thread, search_k = 1600
21:54:50 Annoy recall = 100%
21:54:52 Commencing smooth kNN distance calibration using 1 thread
21:54:54 Initializing from normalized Laplacian + noise
21:54:54 Commencing optimization for 500 epochs, with 24158 positive edges
21:54:58 Optimization finished
[1] "16 0.1"21:54:58 UMAP embedding parameters a = 1.577 b = 0.8951
21:54:58 Read 1203 rows and found 38 numeric columns
21:54:58 Using Annoy for neighbor search, n_neighbors = 16
21:54:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762211364
21:54:58 Searching Annoy index using 1 thread, search_k = 1600
21:54:58 Annoy recall = 100%
21:54:59 Commencing smooth kNN distance calibration using 1 thread
21:55:02 Initializing from normalized Laplacian + noise
21:55:02 Commencing optimization for 500 epochs, with 24158 positive edges
21:55:05 Optimization finished
[1] "16 0.11"21:55:06 UMAP embedding parameters a = 1.544 b = 0.9058
21:55:06 Read 1203 rows and found 38 numeric columns
21:55:06 Using Annoy for neighbor search, n_neighbors = 16
21:55:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872180154
21:55:06 Searching Annoy index using 1 thread, search_k = 1600
21:55:06 Annoy recall = 100%
21:55:07 Commencing smooth kNN distance calibration using 1 thread
21:55:10 Initializing from normalized Laplacian + noise
21:55:10 Commencing optimization for 500 epochs, with 24158 positive edges
21:55:13 Optimization finished
[1] "16 0.12"21:55:13 UMAP embedding parameters a = 1.51 b = 0.9165
21:55:13 Read 1203 rows and found 38 numeric columns
21:55:13 Using Annoy for neighbor search, n_neighbors = 16
21:55:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770c98d8e
21:55:14 Searching Annoy index using 1 thread, search_k = 1600
21:55:14 Annoy recall = 100%
21:55:15 Commencing smooth kNN distance calibration using 1 thread
21:55:18 Initializing from normalized Laplacian + noise
21:55:18 Commencing optimization for 500 epochs, with 24158 positive edges
21:55:21 Optimization finished
[1] "16 0.13"21:55:21 UMAP embedding parameters a = 1.478 b = 0.9272
21:55:21 Read 1203 rows and found 38 numeric columns
21:55:21 Using Annoy for neighbor search, n_neighbors = 16
21:55:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874009e9e1
21:55:22 Searching Annoy index using 1 thread, search_k = 1600
21:55:22 Annoy recall = 100%
21:55:23 Commencing smooth kNN distance calibration using 1 thread
21:55:26 Initializing from normalized Laplacian + noise
21:55:26 Commencing optimization for 500 epochs, with 24158 positive edges
21:55:29 Optimization finished
[1] "16 0.14"21:55:29 UMAP embedding parameters a = 1.446 b = 0.938
21:55:29 Read 1203 rows and found 38 numeric columns
21:55:29 Using Annoy for neighbor search, n_neighbors = 16
21:55:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f5f2491
21:55:30 Searching Annoy index using 1 thread, search_k = 1600
21:55:30 Annoy recall = 100%
21:55:31 Commencing smooth kNN distance calibration using 1 thread
21:55:34 Initializing from normalized Laplacian + noise
21:55:34 Commencing optimization for 500 epochs, with 24158 positive edges
21:55:37 Optimization finished
[1] "16 0.15"21:55:37 UMAP embedding parameters a = 1.414 b = 0.9488
21:55:37 Read 1203 rows and found 38 numeric columns
21:55:37 Using Annoy for neighbor search, n_neighbors = 16
21:55:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a263d2a
21:55:37 Searching Annoy index using 1 thread, search_k = 1600
21:55:38 Annoy recall = 100%
21:55:39 Commencing smooth kNN distance calibration using 1 thread
21:55:42 Initializing from normalized Laplacian + noise
21:55:42 Commencing optimization for 500 epochs, with 24158 positive edges
21:55:45 Optimization finished
[1] "16 0.16"21:55:45 UMAP embedding parameters a = 1.383 b = 0.9596
21:55:45 Read 1203 rows and found 38 numeric columns
21:55:45 Using Annoy for neighbor search, n_neighbors = 16
21:55:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874de488e6
21:55:45 Searching Annoy index using 1 thread, search_k = 1600
21:55:46 Annoy recall = 100%
21:55:47 Commencing smooth kNN distance calibration using 1 thread
21:55:49 Initializing from normalized Laplacian + noise
21:55:50 Commencing optimization for 500 epochs, with 24158 positive edges
21:55:53 Optimization finished
[1] "16 0.17"21:55:53 UMAP embedding parameters a = 1.352 b = 0.9704
21:55:53 Read 1203 rows and found 38 numeric columns
21:55:53 Using Annoy for neighbor search, n_neighbors = 16
21:55:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876073371e
21:55:53 Searching Annoy index using 1 thread, search_k = 1600
21:55:54 Annoy recall = 100%
21:55:55 Commencing smooth kNN distance calibration using 1 thread
21:55:57 Initializing from normalized Laplacian + noise
21:55:57 Commencing optimization for 500 epochs, with 24158 positive edges
21:56:01 Optimization finished
[1] "16 0.18"21:56:01 UMAP embedding parameters a = 1.321 b = 0.9813
21:56:01 Read 1203 rows and found 38 numeric columns
21:56:01 Using Annoy for neighbor search, n_neighbors = 16
21:56:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f0f3dc0
21:56:01 Searching Annoy index using 1 thread, search_k = 1600
21:56:01 Annoy recall = 100%
21:56:03 Commencing smooth kNN distance calibration using 1 thread
21:56:05 Initializing from normalized Laplacian + noise
21:56:05 Commencing optimization for 500 epochs, with 24158 positive edges
21:56:09 Optimization finished
[1] "16 0.19"21:56:09 UMAP embedding parameters a = 1.292 b = 0.9921
21:56:09 Read 1203 rows and found 38 numeric columns
21:56:09 Using Annoy for neighbor search, n_neighbors = 16
21:56:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b1f92f1
21:56:09 Searching Annoy index using 1 thread, search_k = 1600
21:56:09 Annoy recall = 100%
21:56:11 Commencing smooth kNN distance calibration using 1 thread
21:56:13 Initializing from normalized Laplacian + noise
21:56:13 Commencing optimization for 500 epochs, with 24158 positive edges
21:56:17 Optimization finished
[1] "16 0.2"21:56:17 UMAP embedding parameters a = 1.262 b = 1.003
21:56:17 Read 1203 rows and found 38 numeric columns
21:56:17 Using Annoy for neighbor search, n_neighbors = 16
21:56:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aec6cfb
21:56:17 Searching Annoy index using 1 thread, search_k = 1600
21:56:17 Annoy recall = 100%
21:56:19 Commencing smooth kNN distance calibration using 1 thread
21:56:21 Initializing from normalized Laplacian + noise
21:56:21 Commencing optimization for 500 epochs, with 24158 positive edges
21:56:25 Optimization finished
[1] "17 0"21:56:25 UMAP embedding parameters a = 1.933 b = 0.7905
21:56:25 Read 1203 rows and found 38 numeric columns
21:56:25 Using Annoy for neighbor search, n_neighbors = 17
21:56:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ff5d140
21:56:25 Searching Annoy index using 1 thread, search_k = 1700
21:56:25 Annoy recall = 100%
21:56:27 Commencing smooth kNN distance calibration using 1 thread
21:56:29 Initializing from normalized Laplacian + noise
21:56:29 Commencing optimization for 500 epochs, with 25756 positive edges
21:56:33 Optimization finished
[1] "17 0.01"21:56:33 UMAP embedding parameters a = 1.896 b = 0.8006
21:56:33 Read 1203 rows and found 38 numeric columns
21:56:33 Using Annoy for neighbor search, n_neighbors = 17
21:56:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716b63f03
21:56:33 Searching Annoy index using 1 thread, search_k = 1700
21:56:34 Annoy recall = 100%
21:56:35 Commencing smooth kNN distance calibration using 1 thread
21:56:38 Initializing from normalized Laplacian + noise
21:56:38 Commencing optimization for 500 epochs, with 25756 positive edges
21:56:41 Optimization finished
[1] "17 0.02"21:56:41 UMAP embedding parameters a = 1.859 b = 0.8109
21:56:41 Read 1203 rows and found 38 numeric columns
21:56:41 Using Annoy for neighbor search, n_neighbors = 17
21:56:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875940cdb1
21:56:41 Searching Annoy index using 1 thread, search_k = 1700
21:56:42 Annoy recall = 100%
21:56:43 Commencing smooth kNN distance calibration using 1 thread
21:56:46 Initializing from normalized Laplacian + noise
21:56:46 Commencing optimization for 500 epochs, with 25756 positive edges
21:56:49 Optimization finished
[1] "17 0.03"21:56:49 UMAP embedding parameters a = 1.822 b = 0.8212
21:56:49 Read 1203 rows and found 38 numeric columns
21:56:49 Using Annoy for neighbor search, n_neighbors = 17
21:56:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bfce5ed
21:56:50 Searching Annoy index using 1 thread, search_k = 1700
21:56:50 Annoy recall = 100%
21:56:51 Commencing smooth kNN distance calibration using 1 thread
21:56:54 Initializing from normalized Laplacian + noise
21:56:54 Commencing optimization for 500 epochs, with 25756 positive edges
21:56:57 Optimization finished
[1] "17 0.04"21:56:57 UMAP embedding parameters a = 1.786 b = 0.8316
21:56:57 Read 1203 rows and found 38 numeric columns
21:56:57 Using Annoy for neighbor search, n_neighbors = 17
21:56:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a01cb49
21:56:58 Searching Annoy index using 1 thread, search_k = 1700
21:56:58 Annoy recall = 100%
21:56:59 Commencing smooth kNN distance calibration using 1 thread
21:57:02 Initializing from normalized Laplacian + noise
21:57:02 Commencing optimization for 500 epochs, with 25756 positive edges
21:57:05 Optimization finished
[1] "17 0.05"21:57:05 UMAP embedding parameters a = 1.75 b = 0.8421
21:57:05 Read 1203 rows and found 38 numeric columns
21:57:05 Using Annoy for neighbor search, n_neighbors = 17
21:57:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e095ff7
21:57:06 Searching Annoy index using 1 thread, search_k = 1700
21:57:06 Annoy recall = 100%
21:57:07 Commencing smooth kNN distance calibration using 1 thread
21:57:10 Initializing from normalized Laplacian + noise
21:57:10 Commencing optimization for 500 epochs, with 25756 positive edges
21:57:13 Optimization finished
[1] "17 0.06"21:57:14 UMAP embedding parameters a = 1.715 b = 0.8526
21:57:14 Read 1203 rows and found 38 numeric columns
21:57:14 Using Annoy for neighbor search, n_neighbors = 17
21:57:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718d64c46
21:57:14 Searching Annoy index using 1 thread, search_k = 1700
21:57:14 Annoy recall = 100%
21:57:15 Commencing smooth kNN distance calibration using 1 thread
21:57:18 Initializing from normalized Laplacian + noise
21:57:18 Commencing optimization for 500 epochs, with 25756 positive edges
21:57:22 Optimization finished
[1] "17 0.07"21:57:22 UMAP embedding parameters a = 1.68 b = 0.8631
21:57:22 Read 1203 rows and found 38 numeric columns
21:57:22 Using Annoy for neighbor search, n_neighbors = 17
21:57:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d0dfab5
21:57:22 Searching Annoy index using 1 thread, search_k = 1700
21:57:22 Annoy recall = 100%
21:57:24 Commencing smooth kNN distance calibration using 1 thread
21:57:26 Initializing from normalized Laplacian + noise
21:57:26 Commencing optimization for 500 epochs, with 25756 positive edges
21:57:30 Optimization finished
[1] "17 0.08"21:57:30 UMAP embedding parameters a = 1.645 b = 0.8737
21:57:30 Read 1203 rows and found 38 numeric columns
21:57:30 Using Annoy for neighbor search, n_neighbors = 17
21:57:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745ece399
21:57:30 Searching Annoy index using 1 thread, search_k = 1700
21:57:30 Annoy recall = 100%
21:57:32 Commencing smooth kNN distance calibration using 1 thread
21:57:34 Initializing from normalized Laplacian + noise
21:57:34 Commencing optimization for 500 epochs, with 25756 positive edges
21:57:38 Optimization finished
[1] "17 0.09"21:57:38 UMAP embedding parameters a = 1.611 b = 0.8844
21:57:38 Read 1203 rows and found 38 numeric columns
21:57:38 Using Annoy for neighbor search, n_neighbors = 17
21:57:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732eebb4b
21:57:38 Searching Annoy index using 1 thread, search_k = 1700
21:57:39 Annoy recall = 100%
21:57:40 Commencing smooth kNN distance calibration using 1 thread
21:57:43 Initializing from normalized Laplacian + noise
21:57:43 Commencing optimization for 500 epochs, with 25756 positive edges
21:57:46 Optimization finished
[1] "17 0.1"21:57:46 UMAP embedding parameters a = 1.577 b = 0.8951
21:57:46 Read 1203 rows and found 38 numeric columns
21:57:46 Using Annoy for neighbor search, n_neighbors = 17
21:57:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87152e609f
21:57:47 Searching Annoy index using 1 thread, search_k = 1700
21:57:47 Annoy recall = 100%
21:57:48 Commencing smooth kNN distance calibration using 1 thread
21:57:51 Initializing from normalized Laplacian + noise
21:57:51 Commencing optimization for 500 epochs, with 25756 positive edges
21:57:54 Optimization finished
[1] "17 0.11"21:57:54 UMAP embedding parameters a = 1.544 b = 0.9058
21:57:55 Read 1203 rows and found 38 numeric columns
21:57:55 Using Annoy for neighbor search, n_neighbors = 17
21:57:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754b45809
21:57:55 Searching Annoy index using 1 thread, search_k = 1700
21:57:55 Annoy recall = 100%
21:57:56 Commencing smooth kNN distance calibration using 1 thread
21:57:59 Initializing from normalized Laplacian + noise
21:57:59 Commencing optimization for 500 epochs, with 25756 positive edges
21:58:02 Optimization finished
[1] "17 0.12"21:58:03 UMAP embedding parameters a = 1.51 b = 0.9165
21:58:03 Read 1203 rows and found 38 numeric columns
21:58:03 Using Annoy for neighbor search, n_neighbors = 17
21:58:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a23cadc
21:58:03 Searching Annoy index using 1 thread, search_k = 1700
21:58:03 Annoy recall = 100%
21:58:05 Commencing smooth kNN distance calibration using 1 thread
21:58:07 Initializing from normalized Laplacian + noise
21:58:07 Commencing optimization for 500 epochs, with 25756 positive edges
21:58:11 Optimization finished
[1] "17 0.13"21:58:11 UMAP embedding parameters a = 1.478 b = 0.9272
21:58:11 Read 1203 rows and found 38 numeric columns
21:58:11 Using Annoy for neighbor search, n_neighbors = 17
21:58:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c635326
21:58:11 Searching Annoy index using 1 thread, search_k = 1700
21:58:12 Annoy recall = 100%
21:58:13 Commencing smooth kNN distance calibration using 1 thread
21:58:16 Initializing from normalized Laplacian + noise
21:58:16 Commencing optimization for 500 epochs, with 25756 positive edges
21:58:19 Optimization finished
[1] "17 0.14"21:58:19 UMAP embedding parameters a = 1.446 b = 0.938
21:58:19 Read 1203 rows and found 38 numeric columns
21:58:19 Using Annoy for neighbor search, n_neighbors = 17
21:58:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710819fd9
21:58:20 Searching Annoy index using 1 thread, search_k = 1700
21:58:20 Annoy recall = 100%
21:58:21 Commencing smooth kNN distance calibration using 1 thread
21:58:24 Initializing from normalized Laplacian + noise
21:58:24 Commencing optimization for 500 epochs, with 25756 positive edges
21:58:28 Optimization finished
[1] "17 0.15"21:58:28 UMAP embedding parameters a = 1.414 b = 0.9488
21:58:28 Read 1203 rows and found 38 numeric columns
21:58:28 Using Annoy for neighbor search, n_neighbors = 17
21:58:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87624507fe
21:58:28 Searching Annoy index using 1 thread, search_k = 1700
21:58:28 Annoy recall = 100%
21:58:30 Commencing smooth kNN distance calibration using 1 thread
21:58:33 Initializing from normalized Laplacian + noise
21:58:33 Commencing optimization for 500 epochs, with 25756 positive edges
21:58:36 Optimization finished
[1] "17 0.16"21:58:36 UMAP embedding parameters a = 1.383 b = 0.9596
21:58:36 Read 1203 rows and found 38 numeric columns
21:58:36 Using Annoy for neighbor search, n_neighbors = 17
21:58:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733c0c374
21:58:36 Searching Annoy index using 1 thread, search_k = 1700
21:58:37 Annoy recall = 100%
21:58:38 Commencing smooth kNN distance calibration using 1 thread
21:58:41 Initializing from normalized Laplacian + noise
21:58:41 Commencing optimization for 500 epochs, with 25756 positive edges
21:58:44 Optimization finished
[1] "17 0.17"21:58:44 UMAP embedding parameters a = 1.352 b = 0.9704
21:58:44 Read 1203 rows and found 38 numeric columns
21:58:44 Using Annoy for neighbor search, n_neighbors = 17
21:58:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744c19235
21:58:45 Searching Annoy index using 1 thread, search_k = 1700
21:58:45 Annoy recall = 100%
21:58:46 Commencing smooth kNN distance calibration using 1 thread
21:58:49 Initializing from normalized Laplacian + noise
21:58:49 Commencing optimization for 500 epochs, with 25756 positive edges
21:58:52 Optimization finished
[1] "17 0.18"21:58:53 UMAP embedding parameters a = 1.321 b = 0.9813
21:58:53 Read 1203 rows and found 38 numeric columns
21:58:53 Using Annoy for neighbor search, n_neighbors = 17
21:58:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765f0f179
21:58:53 Searching Annoy index using 1 thread, search_k = 1700
21:58:53 Annoy recall = 100%
21:58:54 Commencing smooth kNN distance calibration using 1 thread
21:58:57 Initializing from normalized Laplacian + noise
21:58:57 Commencing optimization for 500 epochs, with 25756 positive edges
21:59:01 Optimization finished
[1] "17 0.19"21:59:01 UMAP embedding parameters a = 1.292 b = 0.9921
21:59:01 Read 1203 rows and found 38 numeric columns
21:59:01 Using Annoy for neighbor search, n_neighbors = 17
21:59:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f09f3d7
21:59:01 Searching Annoy index using 1 thread, search_k = 1700
21:59:01 Annoy recall = 100%
21:59:03 Commencing smooth kNN distance calibration using 1 thread
21:59:05 Initializing from normalized Laplacian + noise
21:59:05 Commencing optimization for 500 epochs, with 25756 positive edges
21:59:09 Optimization finished
[1] "17 0.2"21:59:09 UMAP embedding parameters a = 1.262 b = 1.003
21:59:09 Read 1203 rows and found 38 numeric columns
21:59:09 Using Annoy for neighbor search, n_neighbors = 17
21:59:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726e2a59a
21:59:09 Searching Annoy index using 1 thread, search_k = 1700
21:59:10 Annoy recall = 100%
21:59:11 Commencing smooth kNN distance calibration using 1 thread
21:59:14 Initializing from normalized Laplacian + noise
21:59:14 Commencing optimization for 500 epochs, with 25756 positive edges
21:59:17 Optimization finished
[1] "18 0"21:59:17 UMAP embedding parameters a = 1.933 b = 0.7905
21:59:17 Read 1203 rows and found 38 numeric columns
21:59:17 Using Annoy for neighbor search, n_neighbors = 18
21:59:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876808f2cd
21:59:18 Searching Annoy index using 1 thread, search_k = 1800
21:59:18 Annoy recall = 100%
21:59:19 Commencing smooth kNN distance calibration using 1 thread
21:59:22 Initializing from normalized Laplacian + noise
21:59:22 Commencing optimization for 500 epochs, with 27270 positive edges
21:59:26 Optimization finished
[1] "18 0.01"21:59:26 UMAP embedding parameters a = 1.896 b = 0.8006
21:59:26 Read 1203 rows and found 38 numeric columns
21:59:26 Using Annoy for neighbor search, n_neighbors = 18
21:59:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fd38165
21:59:26 Searching Annoy index using 1 thread, search_k = 1800
21:59:26 Annoy recall = 100%
21:59:28 Commencing smooth kNN distance calibration using 1 thread
21:59:30 Initializing from normalized Laplacian + noise
21:59:30 Commencing optimization for 500 epochs, with 27270 positive edges
21:59:34 Optimization finished
[1] "18 0.02"21:59:34 UMAP embedding parameters a = 1.859 b = 0.8109
21:59:34 Read 1203 rows and found 38 numeric columns
21:59:34 Using Annoy for neighbor search, n_neighbors = 18
21:59:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766ec8f7b
21:59:34 Searching Annoy index using 1 thread, search_k = 1800
21:59:35 Annoy recall = 100%
21:59:36 Commencing smooth kNN distance calibration using 1 thread
21:59:39 Initializing from normalized Laplacian + noise
21:59:39 Commencing optimization for 500 epochs, with 27270 positive edges
21:59:42 Optimization finished
[1] "18 0.03"21:59:43 UMAP embedding parameters a = 1.822 b = 0.8212
21:59:43 Read 1203 rows and found 38 numeric columns
21:59:43 Using Annoy for neighbor search, n_neighbors = 18
21:59:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874768175f
21:59:43 Searching Annoy index using 1 thread, search_k = 1800
21:59:43 Annoy recall = 100%
21:59:44 Commencing smooth kNN distance calibration using 1 thread
21:59:47 Initializing from normalized Laplacian + noise
21:59:47 Commencing optimization for 500 epochs, with 27270 positive edges
21:59:51 Optimization finished
[1] "18 0.04"21:59:51 UMAP embedding parameters a = 1.786 b = 0.8316
21:59:51 Read 1203 rows and found 38 numeric columns
21:59:51 Using Annoy for neighbor search, n_neighbors = 18
21:59:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719f9be8f
21:59:51 Searching Annoy index using 1 thread, search_k = 1800
21:59:51 Annoy recall = 100%
21:59:53 Commencing smooth kNN distance calibration using 1 thread
21:59:55 Initializing from normalized Laplacian + noise
21:59:55 Commencing optimization for 500 epochs, with 27270 positive edges
21:59:59 Optimization finished
[1] "18 0.05"21:59:59 UMAP embedding parameters a = 1.75 b = 0.8421
21:59:59 Read 1203 rows and found 38 numeric columns
21:59:59 Using Annoy for neighbor search, n_neighbors = 18
21:59:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734d11862
22:00:00 Searching Annoy index using 1 thread, search_k = 1800
22:00:00 Annoy recall = 100%
22:00:01 Commencing smooth kNN distance calibration using 1 thread
22:00:04 Initializing from normalized Laplacian + noise
22:00:04 Commencing optimization for 500 epochs, with 27270 positive edges
22:00:08 Optimization finished
[1] "18 0.06"22:00:08 UMAP embedding parameters a = 1.715 b = 0.8526
22:00:08 Read 1203 rows and found 38 numeric columns
22:00:08 Using Annoy for neighbor search, n_neighbors = 18
22:00:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727db4e7d
22:00:08 Searching Annoy index using 1 thread, search_k = 1800
22:00:08 Annoy recall = 100%
22:00:10 Commencing smooth kNN distance calibration using 1 thread
22:00:13 Initializing from normalized Laplacian + noise
22:00:13 Commencing optimization for 500 epochs, with 27270 positive edges
22:00:17 Optimization finished
[1] "18 0.07"22:00:17 UMAP embedding parameters a = 1.68 b = 0.8631
22:00:17 Read 1203 rows and found 38 numeric columns
22:00:17 Using Annoy for neighbor search, n_neighbors = 18
22:00:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871908fc50
22:00:17 Searching Annoy index using 1 thread, search_k = 1800
22:00:17 Annoy recall = 100%
22:00:19 Commencing smooth kNN distance calibration using 1 thread
22:00:21 Initializing from normalized Laplacian + noise
22:00:21 Commencing optimization for 500 epochs, with 27270 positive edges
22:00:25 Optimization finished
[1] "18 0.08"22:00:25 UMAP embedding parameters a = 1.645 b = 0.8737
22:00:25 Read 1203 rows and found 38 numeric columns
22:00:25 Using Annoy for neighbor search, n_neighbors = 18
22:00:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ff0ab53
22:00:26 Searching Annoy index using 1 thread, search_k = 1800
22:00:26 Annoy recall = 100%
22:00:27 Commencing smooth kNN distance calibration using 1 thread
22:00:30 Initializing from normalized Laplacian + noise
22:00:30 Commencing optimization for 500 epochs, with 27270 positive edges
22:00:34 Optimization finished
[1] "18 0.09"22:00:34 UMAP embedding parameters a = 1.611 b = 0.8844
22:00:34 Read 1203 rows and found 38 numeric columns
22:00:34 Using Annoy for neighbor search, n_neighbors = 18
22:00:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712c7bb78
22:00:34 Searching Annoy index using 1 thread, search_k = 1800
22:00:34 Annoy recall = 100%
22:00:36 Commencing smooth kNN distance calibration using 1 thread
22:00:39 Initializing from normalized Laplacian + noise
22:00:39 Commencing optimization for 500 epochs, with 27270 positive edges
22:00:42 Optimization finished
[1] "18 0.1"22:00:42 UMAP embedding parameters a = 1.577 b = 0.8951
22:00:42 Read 1203 rows and found 38 numeric columns
22:00:42 Using Annoy for neighbor search, n_neighbors = 18
22:00:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878fecd90
22:00:43 Searching Annoy index using 1 thread, search_k = 1800
22:00:43 Annoy recall = 100%
22:00:44 Commencing smooth kNN distance calibration using 1 thread
22:00:47 Initializing from normalized Laplacian + noise
22:00:47 Commencing optimization for 500 epochs, with 27270 positive edges
22:00:51 Optimization finished
[1] "18 0.11"22:00:51 UMAP embedding parameters a = 1.544 b = 0.9058
22:00:51 Read 1203 rows and found 38 numeric columns
22:00:51 Using Annoy for neighbor search, n_neighbors = 18
22:00:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766a6ea57
22:00:51 Searching Annoy index using 1 thread, search_k = 1800
22:00:52 Annoy recall = 100%
22:00:53 Commencing smooth kNN distance calibration using 1 thread
22:00:56 Initializing from normalized Laplacian + noise
22:00:56 Commencing optimization for 500 epochs, with 27270 positive edges
22:01:00 Optimization finished
[1] "18 0.12"22:01:00 UMAP embedding parameters a = 1.51 b = 0.9165
22:01:00 Read 1203 rows and found 38 numeric columns
22:01:00 Using Annoy for neighbor search, n_neighbors = 18
22:01:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c088929
22:01:00 Searching Annoy index using 1 thread, search_k = 1800
22:01:00 Annoy recall = 100%
22:01:02 Commencing smooth kNN distance calibration using 1 thread
22:01:05 Initializing from normalized Laplacian + noise
22:01:05 Commencing optimization for 500 epochs, with 27270 positive edges
22:01:09 Optimization finished
[1] "18 0.13"22:01:09 UMAP embedding parameters a = 1.478 b = 0.9272
22:01:09 Read 1203 rows and found 38 numeric columns
22:01:09 Using Annoy for neighbor search, n_neighbors = 18
22:01:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714fbb37d
22:01:09 Searching Annoy index using 1 thread, search_k = 1800
22:01:09 Annoy recall = 100%
22:01:11 Commencing smooth kNN distance calibration using 1 thread
22:01:15 Initializing from normalized Laplacian + noise
22:01:15 Commencing optimization for 500 epochs, with 27270 positive edges
22:01:19 Optimization finished
[1] "18 0.14"22:01:19 UMAP embedding parameters a = 1.446 b = 0.938
22:01:19 Read 1203 rows and found 38 numeric columns
22:01:19 Using Annoy for neighbor search, n_neighbors = 18
22:01:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720a8b5a0
22:01:19 Searching Annoy index using 1 thread, search_k = 1800
22:01:20 Annoy recall = 100%
22:01:22 Commencing smooth kNN distance calibration using 1 thread
22:01:25 Initializing from normalized Laplacian + noise
22:01:25 Commencing optimization for 500 epochs, with 27270 positive edges
22:01:29 Optimization finished
[1] "18 0.15"22:01:29 UMAP embedding parameters a = 1.414 b = 0.9488
22:01:29 Read 1203 rows and found 38 numeric columns
22:01:29 Using Annoy for neighbor search, n_neighbors = 18
22:01:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a11e920
22:01:29 Searching Annoy index using 1 thread, search_k = 1800
22:01:29 Annoy recall = 100%
22:01:31 Commencing smooth kNN distance calibration using 1 thread
22:01:34 Initializing from normalized Laplacian + noise
22:01:34 Commencing optimization for 500 epochs, with 27270 positive edges
22:01:38 Optimization finished
[1] "18 0.16"22:01:38 UMAP embedding parameters a = 1.383 b = 0.9596
22:01:38 Read 1203 rows and found 38 numeric columns
22:01:38 Using Annoy for neighbor search, n_neighbors = 18
22:01:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dd1ffc4
22:01:38 Searching Annoy index using 1 thread, search_k = 1800
22:01:39 Annoy recall = 100%
22:01:40 Commencing smooth kNN distance calibration using 1 thread
22:01:43 Initializing from normalized Laplacian + noise
22:01:43 Commencing optimization for 500 epochs, with 27270 positive edges
22:01:47 Optimization finished
[1] "18 0.17"22:01:47 UMAP embedding parameters a = 1.352 b = 0.9704
22:01:47 Read 1203 rows and found 38 numeric columns
22:01:47 Using Annoy for neighbor search, n_neighbors = 18
22:01:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87db6b056
22:01:48 Searching Annoy index using 1 thread, search_k = 1800
22:01:48 Annoy recall = 100%
22:01:49 Commencing smooth kNN distance calibration using 1 thread
22:01:52 Initializing from normalized Laplacian + noise
22:01:52 Commencing optimization for 500 epochs, with 27270 positive edges
22:01:56 Optimization finished
[1] "18 0.18"22:01:56 UMAP embedding parameters a = 1.321 b = 0.9813
22:01:56 Read 1203 rows and found 38 numeric columns
22:01:56 Using Annoy for neighbor search, n_neighbors = 18
22:01:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ffeccba
22:01:56 Searching Annoy index using 1 thread, search_k = 1800
22:01:57 Annoy recall = 100%
22:01:58 Commencing smooth kNN distance calibration using 1 thread
22:02:01 Initializing from normalized Laplacian + noise
22:02:01 Commencing optimization for 500 epochs, with 27270 positive edges
22:02:05 Optimization finished
[1] "18 0.19"22:02:05 UMAP embedding parameters a = 1.292 b = 0.9921
22:02:05 Read 1203 rows and found 38 numeric columns
22:02:05 Using Annoy for neighbor search, n_neighbors = 18
22:02:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:02:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760c0bb0f
22:02:06 Searching Annoy index using 1 thread, search_k = 1800
22:02:06 Annoy recall = 100%
22:02:07 Commencing smooth kNN distance calibration using 1 thread
22:02:11 Initializing from normalized Laplacian + noise
22:02:11 Commencing optimization for 500 epochs, with 27270 positive edges
22:02:15 Optimization finished
[1] "18 0.2"22:02:15 UMAP embedding parameters a = 1.262 b = 1.003
22:02:15 Read 1203 rows and found 38 numeric columns
22:02:15 Using Annoy for neighbor search, n_neighbors = 18
22:02:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:02:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722e510f5
22:02:15 Searching Annoy index using 1 thread, search_k = 1800
22:02:15 Annoy recall = 100%
22:02:17 Commencing smooth kNN distance calibration using 1 thread
22:02:20 Initializing from normalized Laplacian + noise
22:02:20 Commencing optimization for 500 epochs, with 27270 positive edges
22:02:26 Optimization finished
[1] "19 0"22:02:26 UMAP embedding parameters a = 1.933 b = 0.7905
22:02:26 Read 1203 rows and found 38 numeric columns
22:02:26 Using Annoy for neighbor search, n_neighbors = 19
22:02:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:02:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724b324c3
22:02:26 Searching Annoy index using 1 thread, search_k = 1900
22:02:27 Annoy recall = 100%
22:02:28 Commencing smooth kNN distance calibration using 1 thread
22:02:33 Initializing from normalized Laplacian + noise
22:02:33 Commencing optimization for 500 epochs, with 28796 positive edges
22:02:38 Optimization finished
[1] "19 0.01"22:02:38 UMAP embedding parameters a = 1.896 b = 0.8006
22:02:38 Read 1203 rows and found 38 numeric columns
22:02:38 Using Annoy for neighbor search, n_neighbors = 19
22:02:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:02:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ae485ec
22:02:39 Searching Annoy index using 1 thread, search_k = 1900
22:02:39 Annoy recall = 100%
22:02:40 Commencing smooth kNN distance calibration using 1 thread
22:02:44 Initializing from normalized Laplacian + noise
22:02:44 Commencing optimization for 500 epochs, with 28796 positive edges
22:02:48 Optimization finished
[1] "19 0.02"22:02:48 UMAP embedding parameters a = 1.859 b = 0.8109
22:02:48 Read 1203 rows and found 38 numeric columns
22:02:48 Using Annoy for neighbor search, n_neighbors = 19
22:02:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:02:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f48641c
22:02:48 Searching Annoy index using 1 thread, search_k = 1900
22:02:48 Annoy recall = 100%
22:02:50 Commencing smooth kNN distance calibration using 1 thread
22:02:53 Initializing from normalized Laplacian + noise
22:02:53 Commencing optimization for 500 epochs, with 28796 positive edges
22:02:57 Optimization finished
[1] "19 0.03"22:02:57 UMAP embedding parameters a = 1.822 b = 0.8212
22:02:57 Read 1203 rows and found 38 numeric columns
22:02:57 Using Annoy for neighbor search, n_neighbors = 19
22:02:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:02:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873534c49c
22:02:57 Searching Annoy index using 1 thread, search_k = 1900
22:02:58 Annoy recall = 100%
22:03:00 Commencing smooth kNN distance calibration using 1 thread
22:03:03 Initializing from normalized Laplacian + noise
22:03:03 Commencing optimization for 500 epochs, with 28796 positive edges
22:03:07 Optimization finished
[1] "19 0.04"22:03:07 UMAP embedding parameters a = 1.786 b = 0.8316
22:03:07 Read 1203 rows and found 38 numeric columns
22:03:07 Using Annoy for neighbor search, n_neighbors = 19
22:03:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d298dea
22:03:08 Searching Annoy index using 1 thread, search_k = 1900
22:03:08 Annoy recall = 100%
22:03:10 Commencing smooth kNN distance calibration using 1 thread
22:03:13 Initializing from normalized Laplacian + noise
22:03:13 Commencing optimization for 500 epochs, with 28796 positive edges
22:03:17 Optimization finished
[1] "19 0.05"22:03:18 UMAP embedding parameters a = 1.75 b = 0.8421
22:03:18 Read 1203 rows and found 38 numeric columns
22:03:18 Using Annoy for neighbor search, n_neighbors = 19
22:03:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743092790
22:03:18 Searching Annoy index using 1 thread, search_k = 1900
22:03:18 Annoy recall = 100%
22:03:20 Commencing smooth kNN distance calibration using 1 thread
22:03:23 Initializing from normalized Laplacian + noise
22:03:23 Commencing optimization for 500 epochs, with 28796 positive edges
22:03:28 Optimization finished
[1] "19 0.06"22:03:28 UMAP embedding parameters a = 1.715 b = 0.8526
22:03:28 Read 1203 rows and found 38 numeric columns
22:03:28 Using Annoy for neighbor search, n_neighbors = 19
22:03:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779f656d1
22:03:29 Searching Annoy index using 1 thread, search_k = 1900
22:03:29 Annoy recall = 100%
22:03:31 Commencing smooth kNN distance calibration using 1 thread
22:03:34 Initializing from normalized Laplacian + noise
22:03:34 Commencing optimization for 500 epochs, with 28796 positive edges
22:03:38 Optimization finished
[1] "19 0.07"22:03:38 UMAP embedding parameters a = 1.68 b = 0.8631
22:03:38 Read 1203 rows and found 38 numeric columns
22:03:38 Using Annoy for neighbor search, n_neighbors = 19
22:03:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87331a7f63
22:03:38 Searching Annoy index using 1 thread, search_k = 1900
22:03:38 Annoy recall = 100%
22:03:40 Commencing smooth kNN distance calibration using 1 thread
22:03:43 Initializing from normalized Laplacian + noise
22:03:43 Commencing optimization for 500 epochs, with 28796 positive edges
22:03:47 Optimization finished
[1] "19 0.08"22:03:47 UMAP embedding parameters a = 1.645 b = 0.8737
22:03:47 Read 1203 rows and found 38 numeric columns
22:03:47 Using Annoy for neighbor search, n_neighbors = 19
22:03:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752131b68
22:03:48 Searching Annoy index using 1 thread, search_k = 1900
22:03:48 Annoy recall = 100%
22:03:49 Commencing smooth kNN distance calibration using 1 thread
22:03:52 Initializing from normalized Laplacian + noise
22:03:52 Commencing optimization for 500 epochs, with 28796 positive edges
22:03:56 Optimization finished
[1] "19 0.09"22:03:57 UMAP embedding parameters a = 1.611 b = 0.8844
22:03:57 Read 1203 rows and found 38 numeric columns
22:03:57 Using Annoy for neighbor search, n_neighbors = 19
22:03:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720d8fc6b
22:03:57 Searching Annoy index using 1 thread, search_k = 1900
22:03:57 Annoy recall = 100%
22:03:59 Commencing smooth kNN distance calibration using 1 thread
22:04:02 Initializing from normalized Laplacian + noise
22:04:02 Commencing optimization for 500 epochs, with 28796 positive edges
22:04:06 Optimization finished
[1] "19 0.1"22:04:07 UMAP embedding parameters a = 1.577 b = 0.8951
22:04:07 Read 1203 rows and found 38 numeric columns
22:04:07 Using Annoy for neighbor search, n_neighbors = 19
22:04:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:04:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b237231
22:04:07 Searching Annoy index using 1 thread, search_k = 1900
22:04:07 Annoy recall = 100%
22:04:09 Commencing smooth kNN distance calibration using 1 thread
22:04:12 Initializing from normalized Laplacian + noise
22:04:12 Commencing optimization for 500 epochs, with 28796 positive edges
22:04:16 Optimization finished
[1] "19 0.11"22:04:16 UMAP embedding parameters a = 1.544 b = 0.9058
22:04:16 Read 1203 rows and found 38 numeric columns
22:04:16 Using Annoy for neighbor search, n_neighbors = 19
22:04:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:04:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751e69ccd
22:04:16 Searching Annoy index using 1 thread, search_k = 1900
22:04:16 Annoy recall = 100%
22:04:18 Commencing smooth kNN distance calibration using 1 thread
22:04:21 Initializing from normalized Laplacian + noise
22:04:21 Commencing optimization for 500 epochs, with 28796 positive edges
22:04:25 Optimization finished
[1] "19 0.12"22:04:25 UMAP embedding parameters a = 1.51 b = 0.9165
22:04:25 Read 1203 rows and found 38 numeric columns
22:04:25 Using Annoy for neighbor search, n_neighbors = 19
22:04:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:04:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c58be7
22:04:25 Searching Annoy index using 1 thread, search_k = 1900
22:04:25 Annoy recall = 100%
22:04:27 Commencing smooth kNN distance calibration using 1 thread
22:04:30 Initializing from normalized Laplacian + noise
22:04:30 Commencing optimization for 500 epochs, with 28796 positive edges
22:04:34 Optimization finished
[1] "19 0.13"22:04:34 UMAP embedding parameters a = 1.478 b = 0.9272
22:04:34 Read 1203 rows and found 38 numeric columns
22:04:34 Using Annoy for neighbor search, n_neighbors = 19
22:04:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:04:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87628b8990
22:04:34 Searching Annoy index using 1 thread, search_k = 1900
22:04:34 Annoy recall = 100%
22:04:36 Commencing smooth kNN distance calibration using 1 thread
22:04:39 Initializing from normalized Laplacian + noise
22:04:40 Commencing optimization for 500 epochs, with 28796 positive edges
22:04:44 Optimization finished
[1] "19 0.14"22:04:44 UMAP embedding parameters a = 1.446 b = 0.938
22:04:44 Read 1203 rows and found 38 numeric columns
22:04:44 Using Annoy for neighbor search, n_neighbors = 19
22:04:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:04:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876be05b5d
22:04:45 Searching Annoy index using 1 thread, search_k = 1900
22:04:45 Annoy recall = 100%
22:04:47 Commencing smooth kNN distance calibration using 1 thread
22:04:50 Initializing from normalized Laplacian + noise
22:04:50 Commencing optimization for 500 epochs, with 28796 positive edges
22:04:55 Optimization finished
[1] "19 0.15"22:04:55 UMAP embedding parameters a = 1.414 b = 0.9488
22:04:55 Read 1203 rows and found 38 numeric columns
22:04:55 Using Annoy for neighbor search, n_neighbors = 19
22:04:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:04:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c96a449
22:04:55 Searching Annoy index using 1 thread, search_k = 1900
22:04:56 Annoy recall = 100%
22:04:57 Commencing smooth kNN distance calibration using 1 thread
22:05:01 Initializing from normalized Laplacian + noise
22:05:01 Commencing optimization for 500 epochs, with 28796 positive edges
22:05:05 Optimization finished
[1] "19 0.16"22:05:05 UMAP embedding parameters a = 1.383 b = 0.9596
22:05:05 Read 1203 rows and found 38 numeric columns
22:05:05 Using Annoy for neighbor search, n_neighbors = 19
22:05:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:05:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a66d80d
22:05:05 Searching Annoy index using 1 thread, search_k = 1900
22:05:06 Annoy recall = 100%
22:05:07 Commencing smooth kNN distance calibration using 1 thread
22:05:11 Initializing from normalized Laplacian + noise
22:05:11 Commencing optimization for 500 epochs, with 28796 positive edges
22:05:15 Optimization finished
[1] "19 0.17"22:05:15 UMAP embedding parameters a = 1.352 b = 0.9704
22:05:15 Read 1203 rows and found 38 numeric columns
22:05:15 Using Annoy for neighbor search, n_neighbors = 19
22:05:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:05:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e957ad
22:05:16 Searching Annoy index using 1 thread, search_k = 1900
22:05:16 Annoy recall = 100%
22:05:17 Commencing smooth kNN distance calibration using 1 thread
22:05:21 Initializing from normalized Laplacian + noise
22:05:21 Commencing optimization for 500 epochs, with 28796 positive edges
22:05:26 Optimization finished
[1] "19 0.18"22:05:26 UMAP embedding parameters a = 1.321 b = 0.9813
22:05:26 Read 1203 rows and found 38 numeric columns
22:05:26 Using Annoy for neighbor search, n_neighbors = 19
22:05:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:05:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c874f9c
22:05:26 Searching Annoy index using 1 thread, search_k = 1900
22:05:26 Annoy recall = 100%
22:05:28 Commencing smooth kNN distance calibration using 1 thread
22:05:31 Initializing from normalized Laplacian + noise
22:05:31 Commencing optimization for 500 epochs, with 28796 positive edges
22:05:35 Optimization finished
[1] "19 0.19"22:05:36 UMAP embedding parameters a = 1.292 b = 0.9921
22:05:36 Read 1203 rows and found 38 numeric columns
22:05:36 Using Annoy for neighbor search, n_neighbors = 19
22:05:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:05:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d2e9385
22:05:36 Searching Annoy index using 1 thread, search_k = 1900
22:05:36 Annoy recall = 100%
22:05:38 Commencing smooth kNN distance calibration using 1 thread
22:05:41 Initializing from normalized Laplacian + noise
22:05:41 Commencing optimization for 500 epochs, with 28796 positive edges
22:05:45 Optimization finished
[1] "19 0.2"22:05:45 UMAP embedding parameters a = 1.262 b = 1.003
22:05:45 Read 1203 rows and found 38 numeric columns
22:05:45 Using Annoy for neighbor search, n_neighbors = 19
22:05:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:05:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87de8253d
22:05:45 Searching Annoy index using 1 thread, search_k = 1900
22:05:46 Annoy recall = 100%
22:05:47 Commencing smooth kNN distance calibration using 1 thread
22:05:50 Initializing from normalized Laplacian + noise
22:05:50 Commencing optimization for 500 epochs, with 28796 positive edges
22:05:54 Optimization finished
[1] "20 0"22:05:55 UMAP embedding parameters a = 1.933 b = 0.7905
22:05:55 Read 1203 rows and found 38 numeric columns
22:05:55 Using Annoy for neighbor search, n_neighbors = 20
22:05:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:05:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87732e39f3
22:05:55 Searching Annoy index using 1 thread, search_k = 2000
22:05:55 Annoy recall = 100%
22:05:57 Commencing smooth kNN distance calibration using 1 thread
22:06:00 Initializing from normalized Laplacian + noise
22:06:00 Commencing optimization for 500 epochs, with 30294 positive edges
22:06:04 Optimization finished
[1] "20 0.01"22:06:04 UMAP embedding parameters a = 1.896 b = 0.8006
22:06:04 Read 1203 rows and found 38 numeric columns
22:06:04 Using Annoy for neighbor search, n_neighbors = 20
22:06:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:06:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879371cae
22:06:04 Searching Annoy index using 1 thread, search_k = 2000
22:06:05 Annoy recall = 100%
22:06:06 Commencing smooth kNN distance calibration using 1 thread
22:06:09 Initializing from normalized Laplacian + noise
22:06:09 Commencing optimization for 500 epochs, with 30294 positive edges
22:06:13 Optimization finished
[1] "20 0.02"22:06:14 UMAP embedding parameters a = 1.859 b = 0.8109
22:06:14 Read 1203 rows and found 38 numeric columns
22:06:14 Using Annoy for neighbor search, n_neighbors = 20
22:06:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:06:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722e3d8bb
22:06:14 Searching Annoy index using 1 thread, search_k = 2000
22:06:14 Annoy recall = 100%
22:06:16 Commencing smooth kNN distance calibration using 1 thread
22:06:20 Initializing from normalized Laplacian + noise
22:06:20 Commencing optimization for 500 epochs, with 30294 positive edges
22:06:24 Optimization finished
[1] "20 0.03"22:06:24 UMAP embedding parameters a = 1.822 b = 0.8212
22:06:24 Read 1203 rows and found 38 numeric columns
22:06:24 Using Annoy for neighbor search, n_neighbors = 20
22:06:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:06:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713d6ef94
22:06:25 Searching Annoy index using 1 thread, search_k = 2000
22:06:25 Annoy recall = 100%
22:06:27 Commencing smooth kNN distance calibration using 1 thread
22:06:30 Initializing from normalized Laplacian + noise
22:06:30 Commencing optimization for 500 epochs, with 30294 positive edges
22:06:34 Optimization finished
[1] "20 0.04"22:06:35 UMAP embedding parameters a = 1.786 b = 0.8316
22:06:35 Read 1203 rows and found 38 numeric columns
22:06:35 Using Annoy for neighbor search, n_neighbors = 20
22:06:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:06:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87134905cf
22:06:35 Searching Annoy index using 1 thread, search_k = 2000
22:06:35 Annoy recall = 100%
22:06:37 Commencing smooth kNN distance calibration using 1 thread
22:06:40 Initializing from normalized Laplacian + noise
22:06:40 Commencing optimization for 500 epochs, with 30294 positive edges
22:06:44 Optimization finished
[1] "20 0.05"22:06:44 UMAP embedding parameters a = 1.75 b = 0.8421
22:06:44 Read 1203 rows and found 38 numeric columns
22:06:44 Using Annoy for neighbor search, n_neighbors = 20
22:06:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:06:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750b5d87f
22:06:44 Searching Annoy index using 1 thread, search_k = 2000
22:06:45 Annoy recall = 100%
22:06:46 Commencing smooth kNN distance calibration using 1 thread
22:06:50 Initializing from normalized Laplacian + noise
22:06:50 Commencing optimization for 500 epochs, with 30294 positive edges
22:06:54 Optimization finished
[1] "20 0.06"22:06:54 UMAP embedding parameters a = 1.715 b = 0.8526
22:06:54 Read 1203 rows and found 38 numeric columns
22:06:54 Using Annoy for neighbor search, n_neighbors = 20
22:06:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:06:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87218d9fea
22:06:54 Searching Annoy index using 1 thread, search_k = 2000
22:06:54 Annoy recall = 100%
22:06:56 Commencing smooth kNN distance calibration using 1 thread
22:06:59 Initializing from normalized Laplacian + noise
22:06:59 Commencing optimization for 500 epochs, with 30294 positive edges
22:07:04 Optimization finished
[1] "20 0.07"22:07:04 UMAP embedding parameters a = 1.68 b = 0.8631
22:07:04 Read 1203 rows and found 38 numeric columns
22:07:04 Using Annoy for neighbor search, n_neighbors = 20
22:07:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876347d289
22:07:04 Searching Annoy index using 1 thread, search_k = 2000
22:07:04 Annoy recall = 100%
22:07:06 Commencing smooth kNN distance calibration using 1 thread
22:07:09 Initializing from normalized Laplacian + noise
22:07:09 Commencing optimization for 500 epochs, with 30294 positive edges
22:07:13 Optimization finished
[1] "20 0.08"22:07:14 UMAP embedding parameters a = 1.645 b = 0.8737
22:07:14 Read 1203 rows and found 38 numeric columns
22:07:14 Using Annoy for neighbor search, n_neighbors = 20
22:07:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873176938e
22:07:14 Searching Annoy index using 1 thread, search_k = 2000
22:07:14 Annoy recall = 100%
22:07:16 Commencing smooth kNN distance calibration using 1 thread
22:07:19 Initializing from normalized Laplacian + noise
22:07:19 Commencing optimization for 500 epochs, with 30294 positive edges
22:07:23 Optimization finished
[1] "20 0.09"22:07:23 UMAP embedding parameters a = 1.611 b = 0.8844
22:07:23 Read 1203 rows and found 38 numeric columns
22:07:23 Using Annoy for neighbor search, n_neighbors = 20
22:07:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874472b0df
22:07:23 Searching Annoy index using 1 thread, search_k = 2000
22:07:23 Annoy recall = 100%
22:07:25 Commencing smooth kNN distance calibration using 1 thread
22:07:28 Initializing from normalized Laplacian + noise
22:07:28 Commencing optimization for 500 epochs, with 30294 positive edges
22:07:32 Optimization finished
[1] "20 0.1"22:07:32 UMAP embedding parameters a = 1.577 b = 0.8951
22:07:32 Read 1203 rows and found 38 numeric columns
22:07:32 Using Annoy for neighbor search, n_neighbors = 20
22:07:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877faf74c
22:07:33 Searching Annoy index using 1 thread, search_k = 2000
22:07:33 Annoy recall = 100%
22:07:34 Commencing smooth kNN distance calibration using 1 thread
22:07:38 Initializing from normalized Laplacian + noise
22:07:38 Commencing optimization for 500 epochs, with 30294 positive edges
22:07:42 Optimization finished
[1] "20 0.11"22:07:42 UMAP embedding parameters a = 1.544 b = 0.9058
22:07:42 Read 1203 rows and found 38 numeric columns
22:07:42 Using Annoy for neighbor search, n_neighbors = 20
22:07:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c5b197a
22:07:42 Searching Annoy index using 1 thread, search_k = 2000
22:07:42 Annoy recall = 100%
22:07:44 Commencing smooth kNN distance calibration using 1 thread
22:07:47 Initializing from normalized Laplacian + noise
22:07:47 Commencing optimization for 500 epochs, with 30294 positive edges
22:07:51 Optimization finished
[1] "20 0.12"22:07:51 UMAP embedding parameters a = 1.51 b = 0.9165
22:07:51 Read 1203 rows and found 38 numeric columns
22:07:51 Using Annoy for neighbor search, n_neighbors = 20
22:07:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753bb14fb
22:07:51 Searching Annoy index using 1 thread, search_k = 2000
22:07:52 Annoy recall = 100%
22:07:53 Commencing smooth kNN distance calibration using 1 thread
22:07:57 Initializing from normalized Laplacian + noise
22:07:57 Commencing optimization for 500 epochs, with 30294 positive edges
22:08:01 Optimization finished
[1] "20 0.13"22:08:01 UMAP embedding parameters a = 1.478 b = 0.9272
22:08:01 Read 1203 rows and found 38 numeric columns
22:08:01 Using Annoy for neighbor search, n_neighbors = 20
22:08:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d2fbbe8
22:08:01 Searching Annoy index using 1 thread, search_k = 2000
22:08:01 Annoy recall = 100%
22:08:03 Commencing smooth kNN distance calibration using 1 thread
22:08:06 Initializing from normalized Laplacian + noise
22:08:06 Commencing optimization for 500 epochs, with 30294 positive edges
22:08:10 Optimization finished
[1] "20 0.14"22:08:10 UMAP embedding parameters a = 1.446 b = 0.938
22:08:10 Read 1203 rows and found 38 numeric columns
22:08:10 Using Annoy for neighbor search, n_neighbors = 20
22:08:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876984a764
22:08:10 Searching Annoy index using 1 thread, search_k = 2000
22:08:11 Annoy recall = 100%
22:08:12 Commencing smooth kNN distance calibration using 1 thread
22:08:15 Initializing from normalized Laplacian + noise
22:08:15 Commencing optimization for 500 epochs, with 30294 positive edges
22:08:19 Optimization finished
[1] "20 0.15"22:08:20 UMAP embedding parameters a = 1.414 b = 0.9488
22:08:20 Read 1203 rows and found 38 numeric columns
22:08:20 Using Annoy for neighbor search, n_neighbors = 20
22:08:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716c43c8c
22:08:20 Searching Annoy index using 1 thread, search_k = 2000
22:08:20 Annoy recall = 100%
22:08:22 Commencing smooth kNN distance calibration using 1 thread
22:08:25 Initializing from normalized Laplacian + noise
22:08:25 Commencing optimization for 500 epochs, with 30294 positive edges
22:08:29 Optimization finished
[1] "20 0.16"22:08:29 UMAP embedding parameters a = 1.383 b = 0.9596
22:08:29 Read 1203 rows and found 38 numeric columns
22:08:29 Using Annoy for neighbor search, n_neighbors = 20
22:08:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87372612b9
22:08:29 Searching Annoy index using 1 thread, search_k = 2000
22:08:30 Annoy recall = 100%
22:08:32 Commencing smooth kNN distance calibration using 1 thread
22:08:35 Initializing from normalized Laplacian + noise
22:08:35 Commencing optimization for 500 epochs, with 30294 positive edges
22:08:40 Optimization finished
[1] "20 0.17"22:08:40 UMAP embedding parameters a = 1.352 b = 0.9704
22:08:40 Read 1203 rows and found 38 numeric columns
22:08:40 Using Annoy for neighbor search, n_neighbors = 20
22:08:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c9f26c8
22:08:40 Searching Annoy index using 1 thread, search_k = 2000
22:08:41 Annoy recall = 100%
22:08:42 Commencing smooth kNN distance calibration using 1 thread
22:08:46 Initializing from normalized Laplacian + noise
22:08:46 Commencing optimization for 500 epochs, with 30294 positive edges
22:08:50 Optimization finished
[1] "20 0.18"22:08:50 UMAP embedding parameters a = 1.321 b = 0.9813
22:08:50 Read 1203 rows and found 38 numeric columns
22:08:50 Using Annoy for neighbor search, n_neighbors = 20
22:08:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768d757f4
22:08:50 Searching Annoy index using 1 thread, search_k = 2000
22:08:50 Annoy recall = 100%
22:08:52 Commencing smooth kNN distance calibration using 1 thread
22:08:56 Initializing from normalized Laplacian + noise
22:08:56 Commencing optimization for 500 epochs, with 30294 positive edges
22:09:00 Optimization finished
[1] "20 0.19"22:09:00 UMAP embedding parameters a = 1.292 b = 0.9921
22:09:00 Read 1203 rows and found 38 numeric columns
22:09:00 Using Annoy for neighbor search, n_neighbors = 20
22:09:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:09:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757ff0f25
22:09:01 Searching Annoy index using 1 thread, search_k = 2000
22:09:01 Annoy recall = 100%
22:09:03 Commencing smooth kNN distance calibration using 1 thread
22:09:06 Initializing from normalized Laplacian + noise
22:09:06 Commencing optimization for 500 epochs, with 30294 positive edges
22:09:10 Optimization finished
[1] "20 0.2"22:09:10 UMAP embedding parameters a = 1.262 b = 1.003
22:09:10 Read 1203 rows and found 38 numeric columns
22:09:10 Using Annoy for neighbor search, n_neighbors = 20
22:09:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:09:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737c298f9
22:09:11 Searching Annoy index using 1 thread, search_k = 2000
22:09:11 Annoy recall = 100%
22:09:13 Commencing smooth kNN distance calibration using 1 thread
22:09:16 Initializing from normalized Laplacian + noise
22:09:16 Commencing optimization for 500 epochs, with 30294 positive edges
22:09:20 Optimization finished
[1] "21 0"22:09:21 UMAP embedding parameters a = 1.933 b = 0.7905
22:09:21 Read 1203 rows and found 38 numeric columns
22:09:21 Using Annoy for neighbor search, n_neighbors = 21
22:09:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:09:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873abdf4c1
22:09:21 Searching Annoy index using 1 thread, search_k = 2100
22:09:21 Annoy recall = 100%
22:09:23 Commencing smooth kNN distance calibration using 1 thread
22:09:26 Initializing from normalized Laplacian + noise
22:09:26 Commencing optimization for 500 epochs, with 31780 positive edges
22:09:31 Optimization finished
[1] "21 0.01"22:09:31 UMAP embedding parameters a = 1.896 b = 0.8006
22:09:31 Read 1203 rows and found 38 numeric columns
22:09:31 Using Annoy for neighbor search, n_neighbors = 21
22:09:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:09:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fc49b0c
22:09:31 Searching Annoy index using 1 thread, search_k = 2100
22:09:31 Annoy recall = 100%
22:09:33 Commencing smooth kNN distance calibration using 1 thread
22:09:37 Initializing from normalized Laplacian + noise
22:09:37 Commencing optimization for 500 epochs, with 31780 positive edges
22:09:41 Optimization finished
[1] "21 0.02"22:09:42 UMAP embedding parameters a = 1.859 b = 0.8109
22:09:42 Read 1203 rows and found 38 numeric columns
22:09:42 Using Annoy for neighbor search, n_neighbors = 21
22:09:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:09:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a4e2289
22:09:42 Searching Annoy index using 1 thread, search_k = 2100
22:09:42 Annoy recall = 100%
22:09:44 Commencing smooth kNN distance calibration using 1 thread
22:09:48 Initializing from normalized Laplacian + noise
22:09:48 Commencing optimization for 500 epochs, with 31780 positive edges
22:09:52 Optimization finished
[1] "21 0.03"22:09:52 UMAP embedding parameters a = 1.822 b = 0.8212
22:09:52 Read 1203 rows and found 38 numeric columns
22:09:52 Using Annoy for neighbor search, n_neighbors = 21
22:09:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:09:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87269e501e
22:09:52 Searching Annoy index using 1 thread, search_k = 2100
22:09:52 Annoy recall = 100%
22:09:54 Commencing smooth kNN distance calibration using 1 thread
22:09:57 Initializing from normalized Laplacian + noise
22:09:58 Commencing optimization for 500 epochs, with 31780 positive edges
22:10:01 Optimization finished
[1] "21 0.04"22:10:02 UMAP embedding parameters a = 1.786 b = 0.8316
22:10:02 Read 1203 rows and found 38 numeric columns
22:10:02 Using Annoy for neighbor search, n_neighbors = 21
22:10:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:10:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c5b3f55
22:10:02 Searching Annoy index using 1 thread, search_k = 2100
22:10:02 Annoy recall = 100%
22:10:04 Commencing smooth kNN distance calibration using 1 thread
22:10:08 Initializing from normalized Laplacian + noise
22:10:08 Commencing optimization for 500 epochs, with 31780 positive edges
22:10:12 Optimization finished
[1] "21 0.05"22:10:12 UMAP embedding parameters a = 1.75 b = 0.8421
22:10:12 Read 1203 rows and found 38 numeric columns
22:10:12 Using Annoy for neighbor search, n_neighbors = 21
22:10:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:10:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724b4fa96
22:10:13 Searching Annoy index using 1 thread, search_k = 2100
22:10:13 Annoy recall = 100%
22:10:15 Commencing smooth kNN distance calibration using 1 thread
22:10:18 Initializing from normalized Laplacian + noise
22:10:18 Commencing optimization for 500 epochs, with 31780 positive edges
22:10:22 Optimization finished
[1] "21 0.06"22:10:23 UMAP embedding parameters a = 1.715 b = 0.8526
22:10:23 Read 1203 rows and found 38 numeric columns
22:10:23 Using Annoy for neighbor search, n_neighbors = 21
22:10:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:10:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b87a7cb
22:10:23 Searching Annoy index using 1 thread, search_k = 2100
22:10:23 Annoy recall = 100%
22:10:25 Commencing smooth kNN distance calibration using 1 thread
22:10:28 Initializing from normalized Laplacian + noise
22:10:28 Commencing optimization for 500 epochs, with 31780 positive edges
22:10:33 Optimization finished
[1] "21 0.07"22:10:33 UMAP embedding parameters a = 1.68 b = 0.8631
22:10:33 Read 1203 rows and found 38 numeric columns
22:10:33 Using Annoy for neighbor search, n_neighbors = 21
22:10:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:10:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728e28ef1
22:10:33 Searching Annoy index using 1 thread, search_k = 2100
22:10:34 Annoy recall = 100%
22:10:35 Commencing smooth kNN distance calibration using 1 thread
22:10:39 Initializing from normalized Laplacian + noise
22:10:39 Commencing optimization for 500 epochs, with 31780 positive edges
22:10:43 Optimization finished
[1] "21 0.08"22:10:43 UMAP embedding parameters a = 1.645 b = 0.8737
22:10:43 Read 1203 rows and found 38 numeric columns
22:10:43 Using Annoy for neighbor search, n_neighbors = 21
22:10:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:10:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741e38e1b
22:10:44 Searching Annoy index using 1 thread, search_k = 2100
22:10:44 Annoy recall = 100%
22:10:46 Commencing smooth kNN distance calibration using 1 thread
22:10:51 Initializing from normalized Laplacian + noise
22:10:51 Commencing optimization for 500 epochs, with 31780 positive edges
22:10:55 Optimization finished
[1] "21 0.09"22:10:55 UMAP embedding parameters a = 1.611 b = 0.8844
22:10:55 Read 1203 rows and found 38 numeric columns
22:10:55 Using Annoy for neighbor search, n_neighbors = 21
22:10:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:10:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87396fcd09
22:10:55 Searching Annoy index using 1 thread, search_k = 2100
22:10:56 Annoy recall = 100%
22:10:57 Commencing smooth kNN distance calibration using 1 thread
22:11:01 Initializing from normalized Laplacian + noise
22:11:01 Commencing optimization for 500 epochs, with 31780 positive edges
22:11:05 Optimization finished
[1] "21 0.1"22:11:05 UMAP embedding parameters a = 1.577 b = 0.8951
22:11:05 Read 1203 rows and found 38 numeric columns
22:11:05 Using Annoy for neighbor search, n_neighbors = 21
22:11:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:11:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c10c8e5
22:11:06 Searching Annoy index using 1 thread, search_k = 2100
22:11:06 Annoy recall = 100%
22:11:07 Commencing smooth kNN distance calibration using 1 thread
22:11:11 Initializing from normalized Laplacian + noise
22:11:11 Commencing optimization for 500 epochs, with 31780 positive edges
22:11:15 Optimization finished
[1] "21 0.11"22:11:15 UMAP embedding parameters a = 1.544 b = 0.9058
22:11:15 Read 1203 rows and found 38 numeric columns
22:11:15 Using Annoy for neighbor search, n_neighbors = 21
22:11:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:11:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b1aaac9
22:11:15 Searching Annoy index using 1 thread, search_k = 2100
22:11:16 Annoy recall = 100%
22:11:17 Commencing smooth kNN distance calibration using 1 thread
22:11:21 Initializing from normalized Laplacian + noise
22:11:21 Commencing optimization for 500 epochs, with 31780 positive edges
22:11:25 Optimization finished
[1] "21 0.12"22:11:25 UMAP embedding parameters a = 1.51 b = 0.9165
22:11:25 Read 1203 rows and found 38 numeric columns
22:11:25 Using Annoy for neighbor search, n_neighbors = 21
22:11:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:11:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c53a5c4
22:11:25 Searching Annoy index using 1 thread, search_k = 2100
22:11:26 Annoy recall = 100%
22:11:27 Commencing smooth kNN distance calibration using 1 thread
22:11:31 Initializing from normalized Laplacian + noise
22:11:31 Commencing optimization for 500 epochs, with 31780 positive edges
22:11:35 Optimization finished
[1] "21 0.13"22:11:35 UMAP embedding parameters a = 1.478 b = 0.9272
22:11:35 Read 1203 rows and found 38 numeric columns
22:11:35 Using Annoy for neighbor search, n_neighbors = 21
22:11:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:11:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fe7b879
22:11:35 Searching Annoy index using 1 thread, search_k = 2100
22:11:35 Annoy recall = 100%
22:11:37 Commencing smooth kNN distance calibration using 1 thread
22:11:40 Initializing from normalized Laplacian + noise
22:11:40 Commencing optimization for 500 epochs, with 31780 positive edges
22:11:45 Optimization finished
[1] "21 0.14"22:11:45 UMAP embedding parameters a = 1.446 b = 0.938
22:11:45 Read 1203 rows and found 38 numeric columns
22:11:45 Using Annoy for neighbor search, n_neighbors = 21
22:11:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:11:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e63b098
22:11:45 Searching Annoy index using 1 thread, search_k = 2100
22:11:46 Annoy recall = 100%
22:11:47 Commencing smooth kNN distance calibration using 1 thread
22:11:51 Initializing from normalized Laplacian + noise
22:11:51 Commencing optimization for 500 epochs, with 31780 positive edges
22:11:55 Optimization finished
[1] "21 0.15"22:11:56 UMAP embedding parameters a = 1.414 b = 0.9488
22:11:56 Read 1203 rows and found 38 numeric columns
22:11:56 Using Annoy for neighbor search, n_neighbors = 21
22:11:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:11:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d097e43
22:11:56 Searching Annoy index using 1 thread, search_k = 2100
22:11:56 Annoy recall = 100%
22:11:58 Commencing smooth kNN distance calibration using 1 thread
22:12:01 Initializing from normalized Laplacian + noise
22:12:01 Commencing optimization for 500 epochs, with 31780 positive edges
22:12:05 Optimization finished
[1] "21 0.16"22:12:06 UMAP embedding parameters a = 1.383 b = 0.9596
22:12:06 Read 1203 rows and found 38 numeric columns
22:12:06 Using Annoy for neighbor search, n_neighbors = 21
22:12:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751755863
22:12:06 Searching Annoy index using 1 thread, search_k = 2100
22:12:06 Annoy recall = 100%
22:12:08 Commencing smooth kNN distance calibration using 1 thread
22:12:11 Initializing from normalized Laplacian + noise
22:12:11 Commencing optimization for 500 epochs, with 31780 positive edges
22:12:16 Optimization finished
[1] "21 0.17"22:12:16 UMAP embedding parameters a = 1.352 b = 0.9704
22:12:16 Read 1203 rows and found 38 numeric columns
22:12:16 Using Annoy for neighbor search, n_neighbors = 21
22:12:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741ab8321
22:12:16 Searching Annoy index using 1 thread, search_k = 2100
22:12:16 Annoy recall = 100%
22:12:18 Commencing smooth kNN distance calibration using 1 thread
22:12:21 Initializing from normalized Laplacian + noise
22:12:21 Commencing optimization for 500 epochs, with 31780 positive edges
22:12:26 Optimization finished
[1] "21 0.18"22:12:26 UMAP embedding parameters a = 1.321 b = 0.9813
22:12:26 Read 1203 rows and found 38 numeric columns
22:12:26 Using Annoy for neighbor search, n_neighbors = 21
22:12:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e8011d1
22:12:26 Searching Annoy index using 1 thread, search_k = 2100
22:12:26 Annoy recall = 100%
22:12:28 Commencing smooth kNN distance calibration using 1 thread
22:12:32 Initializing from normalized Laplacian + noise
22:12:32 Commencing optimization for 500 epochs, with 31780 positive edges
22:12:37 Optimization finished
[1] "21 0.19"22:12:37 UMAP embedding parameters a = 1.292 b = 0.9921
22:12:37 Read 1203 rows and found 38 numeric columns
22:12:37 Using Annoy for neighbor search, n_neighbors = 21
22:12:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715e80942
22:12:37 Searching Annoy index using 1 thread, search_k = 2100
22:12:37 Annoy recall = 100%
22:12:40 Commencing smooth kNN distance calibration using 1 thread
22:12:44 Initializing from normalized Laplacian + noise
22:12:44 Commencing optimization for 500 epochs, with 31780 positive edges
22:12:48 Optimization finished
[1] "21 0.2"22:12:49 UMAP embedding parameters a = 1.262 b = 1.003
22:12:49 Read 1203 rows and found 38 numeric columns
22:12:49 Using Annoy for neighbor search, n_neighbors = 21
22:12:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749a67a6d
22:12:49 Searching Annoy index using 1 thread, search_k = 2100
22:12:49 Annoy recall = 100%
22:12:51 Commencing smooth kNN distance calibration using 1 thread
22:12:55 Initializing from normalized Laplacian + noise
22:12:55 Commencing optimization for 500 epochs, with 31780 positive edges
22:12:59 Optimization finished
[1] "22 0"22:12:59 UMAP embedding parameters a = 1.933 b = 0.7905
22:12:59 Read 1203 rows and found 38 numeric columns
22:12:59 Using Annoy for neighbor search, n_neighbors = 22
22:12:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877adb2b4c
22:12:59 Searching Annoy index using 1 thread, search_k = 2200
22:13:00 Annoy recall = 100%
22:13:02 Commencing smooth kNN distance calibration using 1 thread
22:13:06 Initializing from normalized Laplacian + noise
22:13:06 Commencing optimization for 500 epochs, with 33290 positive edges
22:13:11 Optimization finished
[1] "22 0.01"22:13:11 UMAP embedding parameters a = 1.896 b = 0.8006
22:13:11 Read 1203 rows and found 38 numeric columns
22:13:11 Using Annoy for neighbor search, n_neighbors = 22
22:13:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:13:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769a31e3e
22:13:11 Searching Annoy index using 1 thread, search_k = 2200
22:13:11 Annoy recall = 100%
22:13:13 Commencing smooth kNN distance calibration using 1 thread
22:13:17 Initializing from normalized Laplacian + noise
22:13:17 Commencing optimization for 500 epochs, with 33290 positive edges
22:13:21 Optimization finished
[1] "22 0.02"22:13:22 UMAP embedding parameters a = 1.859 b = 0.8109
22:13:22 Read 1203 rows and found 38 numeric columns
22:13:22 Using Annoy for neighbor search, n_neighbors = 22
22:13:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:13:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d63655
22:13:22 Searching Annoy index using 1 thread, search_k = 2200
22:13:22 Annoy recall = 100%
22:13:24 Commencing smooth kNN distance calibration using 1 thread
22:13:29 Initializing from normalized Laplacian + noise
22:13:29 Commencing optimization for 500 epochs, with 33290 positive edges
22:13:33 Optimization finished
[1] "22 0.03"22:13:34 UMAP embedding parameters a = 1.822 b = 0.8212
22:13:34 Read 1203 rows and found 38 numeric columns
22:13:34 Using Annoy for neighbor search, n_neighbors = 22
22:13:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:13:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87645fd2b0
22:13:34 Searching Annoy index using 1 thread, search_k = 2200
22:13:34 Annoy recall = 100%
22:13:36 Commencing smooth kNN distance calibration using 1 thread
22:13:41 Initializing from normalized Laplacian + noise
22:13:41 Commencing optimization for 500 epochs, with 33290 positive edges
22:13:46 Optimization finished
[1] "22 0.04"22:13:46 UMAP embedding parameters a = 1.786 b = 0.8316
22:13:46 Read 1203 rows and found 38 numeric columns
22:13:46 Using Annoy for neighbor search, n_neighbors = 22
22:13:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:13:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87675aca
22:13:46 Searching Annoy index using 1 thread, search_k = 2200
22:13:47 Annoy recall = 100%
22:13:49 Commencing smooth kNN distance calibration using 1 thread
22:13:53 Initializing from normalized Laplacian + noise
22:13:53 Commencing optimization for 500 epochs, with 33290 positive edges
22:13:58 Optimization finished
[1] "22 0.05"22:13:58 UMAP embedding parameters a = 1.75 b = 0.8421
22:13:58 Read 1203 rows and found 38 numeric columns
22:13:58 Using Annoy for neighbor search, n_neighbors = 22
22:13:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:13:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873dfc490f
22:13:58 Searching Annoy index using 1 thread, search_k = 2200
22:13:58 Annoy recall = 100%
22:14:01 Commencing smooth kNN distance calibration using 1 thread
22:14:05 Initializing from normalized Laplacian + noise
22:14:05 Commencing optimization for 500 epochs, with 33290 positive edges
22:14:10 Optimization finished
[1] "22 0.06"22:14:10 UMAP embedding parameters a = 1.715 b = 0.8526
22:14:10 Read 1203 rows and found 38 numeric columns
22:14:10 Using Annoy for neighbor search, n_neighbors = 22
22:14:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:14:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fef978
22:14:10 Searching Annoy index using 1 thread, search_k = 2200
22:14:11 Annoy recall = 100%
22:14:12 Commencing smooth kNN distance calibration using 1 thread
22:14:17 Initializing from normalized Laplacian + noise
22:14:17 Commencing optimization for 500 epochs, with 33290 positive edges
22:14:21 Optimization finished
[1] "22 0.07"22:14:21 UMAP embedding parameters a = 1.68 b = 0.8631
22:14:21 Read 1203 rows and found 38 numeric columns
22:14:21 Using Annoy for neighbor search, n_neighbors = 22
22:14:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:14:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87693eb2be
22:14:22 Searching Annoy index using 1 thread, search_k = 2200
22:14:22 Annoy recall = 100%
22:14:24 Commencing smooth kNN distance calibration using 1 thread
22:14:28 Initializing from normalized Laplacian + noise
22:14:28 Commencing optimization for 500 epochs, with 33290 positive edges
22:14:33 Optimization finished
[1] "22 0.08"22:14:33 UMAP embedding parameters a = 1.645 b = 0.8737
22:14:33 Read 1203 rows and found 38 numeric columns
22:14:33 Using Annoy for neighbor search, n_neighbors = 22
22:14:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:14:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715fb5834
22:14:33 Searching Annoy index using 1 thread, search_k = 2200
22:14:34 Annoy recall = 100%
22:14:36 Commencing smooth kNN distance calibration using 1 thread
22:14:40 Initializing from normalized Laplacian + noise
22:14:40 Commencing optimization for 500 epochs, with 33290 positive edges
22:14:44 Optimization finished
[1] "22 0.09"22:14:45 UMAP embedding parameters a = 1.611 b = 0.8844
22:14:45 Read 1203 rows and found 38 numeric columns
22:14:45 Using Annoy for neighbor search, n_neighbors = 22
22:14:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:14:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738c19271
22:14:45 Searching Annoy index using 1 thread, search_k = 2200
22:14:45 Annoy recall = 100%
22:14:47 Commencing smooth kNN distance calibration using 1 thread
22:14:52 Initializing from normalized Laplacian + noise
22:14:52 Commencing optimization for 500 epochs, with 33290 positive edges
22:14:56 Optimization finished
[1] "22 0.1"22:14:56 UMAP embedding parameters a = 1.577 b = 0.8951
22:14:56 Read 1203 rows and found 38 numeric columns
22:14:56 Using Annoy for neighbor search, n_neighbors = 22
22:14:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:14:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723fca77f
22:14:57 Searching Annoy index using 1 thread, search_k = 2200
22:14:57 Annoy recall = 100%
22:14:59 Commencing smooth kNN distance calibration using 1 thread
22:15:03 Initializing from normalized Laplacian + noise
22:15:03 Commencing optimization for 500 epochs, with 33290 positive edges
22:15:07 Optimization finished
[1] "22 0.11"22:15:08 UMAP embedding parameters a = 1.544 b = 0.9058
22:15:08 Read 1203 rows and found 38 numeric columns
22:15:08 Using Annoy for neighbor search, n_neighbors = 22
22:15:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:15:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775bff340
22:15:08 Searching Annoy index using 1 thread, search_k = 2200
22:15:08 Annoy recall = 100%
22:15:10 Commencing smooth kNN distance calibration using 1 thread
22:15:14 Initializing from normalized Laplacian + noise
22:15:14 Commencing optimization for 500 epochs, with 33290 positive edges
22:15:18 Optimization finished
[1] "22 0.12"22:15:19 UMAP embedding parameters a = 1.51 b = 0.9165
22:15:19 Read 1203 rows and found 38 numeric columns
22:15:19 Using Annoy for neighbor search, n_neighbors = 22
22:15:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:15:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87530fb4fa
22:15:19 Searching Annoy index using 1 thread, search_k = 2200
22:15:19 Annoy recall = 100%
22:15:21 Commencing smooth kNN distance calibration using 1 thread
22:15:25 Initializing from normalized Laplacian + noise
22:15:25 Commencing optimization for 500 epochs, with 33290 positive edges
22:15:29 Optimization finished
[1] "22 0.13"22:15:30 UMAP embedding parameters a = 1.478 b = 0.9272
22:15:30 Read 1203 rows and found 38 numeric columns
22:15:30 Using Annoy for neighbor search, n_neighbors = 22
22:15:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:15:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a9af79e
22:15:30 Searching Annoy index using 1 thread, search_k = 2200
22:15:30 Annoy recall = 100%
22:15:32 Commencing smooth kNN distance calibration using 1 thread
22:15:36 Initializing from normalized Laplacian + noise
22:15:36 Commencing optimization for 500 epochs, with 33290 positive edges
22:15:41 Optimization finished
[1] "22 0.14"22:15:41 UMAP embedding parameters a = 1.446 b = 0.938
22:15:41 Read 1203 rows and found 38 numeric columns
22:15:41 Using Annoy for neighbor search, n_neighbors = 22
22:15:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:15:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87121b3295
22:15:41 Searching Annoy index using 1 thread, search_k = 2200
22:15:42 Annoy recall = 100%
22:15:44 Commencing smooth kNN distance calibration using 1 thread
22:15:48 Initializing from normalized Laplacian + noise
22:15:48 Commencing optimization for 500 epochs, with 33290 positive edges
22:15:52 Optimization finished
[1] "22 0.15"22:15:52 UMAP embedding parameters a = 1.414 b = 0.9488
22:15:52 Read 1203 rows and found 38 numeric columns
22:15:52 Using Annoy for neighbor search, n_neighbors = 22
22:15:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:15:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777c4af90
22:15:53 Searching Annoy index using 1 thread, search_k = 2200
22:15:53 Annoy recall = 100%
22:15:55 Commencing smooth kNN distance calibration using 1 thread
22:15:59 Initializing from normalized Laplacian + noise
22:15:59 Commencing optimization for 500 epochs, with 33290 positive edges
22:16:03 Optimization finished
[1] "22 0.16"22:16:03 UMAP embedding parameters a = 1.383 b = 0.9596
22:16:03 Read 1203 rows and found 38 numeric columns
22:16:03 Using Annoy for neighbor search, n_neighbors = 22
22:16:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:16:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776229f69
22:16:04 Searching Annoy index using 1 thread, search_k = 2200
22:16:04 Annoy recall = 100%
22:16:06 Commencing smooth kNN distance calibration using 1 thread
22:16:10 Initializing from normalized Laplacian + noise
22:16:10 Commencing optimization for 500 epochs, with 33290 positive edges
22:16:15 Optimization finished
[1] "22 0.17"22:16:15 UMAP embedding parameters a = 1.352 b = 0.9704
22:16:15 Read 1203 rows and found 38 numeric columns
22:16:15 Using Annoy for neighbor search, n_neighbors = 22
22:16:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:16:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873afdc186
22:16:15 Searching Annoy index using 1 thread, search_k = 2200
22:16:15 Annoy recall = 100%
22:16:17 Commencing smooth kNN distance calibration using 1 thread
22:16:21 Initializing from normalized Laplacian + noise
22:16:21 Commencing optimization for 500 epochs, with 33290 positive edges
22:16:26 Optimization finished
[1] "22 0.18"22:16:26 UMAP embedding parameters a = 1.321 b = 0.9813
22:16:26 Read 1203 rows and found 38 numeric columns
22:16:26 Using Annoy for neighbor search, n_neighbors = 22
22:16:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:16:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739a83dab
22:16:26 Searching Annoy index using 1 thread, search_k = 2200
22:16:26 Annoy recall = 100%
22:16:28 Commencing smooth kNN distance calibration using 1 thread
22:16:33 Initializing from normalized Laplacian + noise
22:16:33 Commencing optimization for 500 epochs, with 33290 positive edges
22:16:37 Optimization finished
[1] "22 0.19"22:16:38 UMAP embedding parameters a = 1.292 b = 0.9921
22:16:38 Read 1203 rows and found 38 numeric columns
22:16:38 Using Annoy for neighbor search, n_neighbors = 22
22:16:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:16:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f926c72
22:16:38 Searching Annoy index using 1 thread, search_k = 2200
22:16:38 Annoy recall = 100%
22:16:40 Commencing smooth kNN distance calibration using 1 thread
22:16:44 Initializing from normalized Laplacian + noise
22:16:44 Commencing optimization for 500 epochs, with 33290 positive edges
22:16:48 Optimization finished
[1] "22 0.2"22:16:49 UMAP embedding parameters a = 1.262 b = 1.003
22:16:49 Read 1203 rows and found 38 numeric columns
22:16:49 Using Annoy for neighbor search, n_neighbors = 22
22:16:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:16:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87570e8a6b
22:16:49 Searching Annoy index using 1 thread, search_k = 2200
22:16:49 Annoy recall = 100%
22:16:51 Commencing smooth kNN distance calibration using 1 thread
22:16:55 Initializing from normalized Laplacian + noise
22:16:55 Commencing optimization for 500 epochs, with 33290 positive edges
22:16:59 Optimization finished
[1] "23 0"22:16:59 UMAP embedding parameters a = 1.933 b = 0.7905
22:16:59 Read 1203 rows and found 38 numeric columns
22:16:59 Using Annoy for neighbor search, n_neighbors = 23
22:16:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c2e875
22:17:00 Searching Annoy index using 1 thread, search_k = 2300
22:17:00 Annoy recall = 100%
22:17:02 Commencing smooth kNN distance calibration using 1 thread
22:17:06 Initializing from normalized Laplacian + noise
22:17:06 Commencing optimization for 500 epochs, with 34814 positive edges
22:17:11 Optimization finished
[1] "23 0.01"22:17:11 UMAP embedding parameters a = 1.896 b = 0.8006
22:17:11 Read 1203 rows and found 38 numeric columns
22:17:11 Using Annoy for neighbor search, n_neighbors = 23
22:17:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87be61236
22:17:12 Searching Annoy index using 1 thread, search_k = 2300
22:17:12 Annoy recall = 100%
22:17:14 Commencing smooth kNN distance calibration using 1 thread
22:17:19 Initializing from normalized Laplacian + noise
22:17:19 Commencing optimization for 500 epochs, with 34814 positive edges
22:17:23 Optimization finished
[1] "23 0.02"22:17:24 UMAP embedding parameters a = 1.859 b = 0.8109
22:17:24 Read 1203 rows and found 38 numeric columns
22:17:24 Using Annoy for neighbor search, n_neighbors = 23
22:17:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f642e4
22:17:24 Searching Annoy index using 1 thread, search_k = 2300
22:17:24 Annoy recall = 100%
22:17:26 Commencing smooth kNN distance calibration using 1 thread
22:17:30 Initializing from normalized Laplacian + noise
22:17:30 Commencing optimization for 500 epochs, with 34814 positive edges
22:17:35 Optimization finished
[1] "23 0.03"22:17:35 UMAP embedding parameters a = 1.822 b = 0.8212
22:17:35 Read 1203 rows and found 38 numeric columns
22:17:35 Using Annoy for neighbor search, n_neighbors = 23
22:17:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876326990d
22:17:35 Searching Annoy index using 1 thread, search_k = 2300
22:17:36 Annoy recall = 100%
22:17:38 Commencing smooth kNN distance calibration using 1 thread
22:17:42 Initializing from normalized Laplacian + noise
22:17:42 Commencing optimization for 500 epochs, with 34814 positive edges
22:17:47 Optimization finished
[1] "23 0.04"22:17:47 UMAP embedding parameters a = 1.786 b = 0.8316
22:17:47 Read 1203 rows and found 38 numeric columns
22:17:47 Using Annoy for neighbor search, n_neighbors = 23
22:17:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738ef9079
22:17:47 Searching Annoy index using 1 thread, search_k = 2300
22:17:47 Annoy recall = 100%
22:17:49 Commencing smooth kNN distance calibration using 1 thread
22:17:53 Initializing from normalized Laplacian + noise
22:17:53 Commencing optimization for 500 epochs, with 34814 positive edges
22:17:58 Optimization finished
[1] "23 0.05"22:17:58 UMAP embedding parameters a = 1.75 b = 0.8421
22:17:58 Read 1203 rows and found 38 numeric columns
22:17:58 Using Annoy for neighbor search, n_neighbors = 23
22:17:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87586b9b47
22:17:59 Searching Annoy index using 1 thread, search_k = 2300
22:17:59 Annoy recall = 100%
22:18:01 Commencing smooth kNN distance calibration using 1 thread
22:18:05 Initializing from normalized Laplacian + noise
22:18:05 Commencing optimization for 500 epochs, with 34814 positive edges
22:18:09 Optimization finished
[1] "23 0.06"22:18:10 UMAP embedding parameters a = 1.715 b = 0.8526
22:18:10 Read 1203 rows and found 38 numeric columns
22:18:10 Using Annoy for neighbor search, n_neighbors = 23
22:18:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:18:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724d21c2f
22:18:10 Searching Annoy index using 1 thread, search_k = 2300
22:18:10 Annoy recall = 100%
22:18:12 Commencing smooth kNN distance calibration using 1 thread
22:18:16 Initializing from normalized Laplacian + noise
22:18:16 Commencing optimization for 500 epochs, with 34814 positive edges
22:18:21 Optimization finished
[1] "23 0.07"22:18:21 UMAP embedding parameters a = 1.68 b = 0.8631
22:18:21 Read 1203 rows and found 38 numeric columns
22:18:21 Using Annoy for neighbor search, n_neighbors = 23
22:18:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:18:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87176fa24b
22:18:21 Searching Annoy index using 1 thread, search_k = 2300
22:18:22 Annoy recall = 100%
22:18:23 Commencing smooth kNN distance calibration using 1 thread
22:18:27 Initializing from normalized Laplacian + noise
22:18:27 Commencing optimization for 500 epochs, with 34814 positive edges
22:18:31 Optimization finished
[1] "23 0.08"22:18:32 UMAP embedding parameters a = 1.645 b = 0.8737
22:18:32 Read 1203 rows and found 38 numeric columns
22:18:32 Using Annoy for neighbor search, n_neighbors = 23
22:18:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:18:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e53a48a
22:18:32 Searching Annoy index using 1 thread, search_k = 2300
22:18:32 Annoy recall = 100%
22:18:34 Commencing smooth kNN distance calibration using 1 thread
22:18:39 Initializing from normalized Laplacian + noise
22:18:39 Commencing optimization for 500 epochs, with 34814 positive edges
22:18:44 Optimization finished
[1] "23 0.09"22:18:44 UMAP embedding parameters a = 1.611 b = 0.8844
22:18:44 Read 1203 rows and found 38 numeric columns
22:18:44 Using Annoy for neighbor search, n_neighbors = 23
22:18:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:18:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e78969c
22:18:44 Searching Annoy index using 1 thread, search_k = 2300
22:18:44 Annoy recall = 100%
22:18:47 Commencing smooth kNN distance calibration using 1 thread
22:18:50 Initializing from normalized Laplacian + noise
22:18:50 Commencing optimization for 500 epochs, with 34814 positive edges
22:18:55 Optimization finished
[1] "23 0.1"22:18:55 UMAP embedding parameters a = 1.577 b = 0.8951
22:18:55 Read 1203 rows and found 38 numeric columns
22:18:55 Using Annoy for neighbor search, n_neighbors = 23
22:18:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:18:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87124acd97
22:18:55 Searching Annoy index using 1 thread, search_k = 2300
22:18:56 Annoy recall = 100%
22:18:57 Commencing smooth kNN distance calibration using 1 thread
22:19:01 Initializing from normalized Laplacian + noise
22:19:01 Commencing optimization for 500 epochs, with 34814 positive edges
22:19:06 Optimization finished
[1] "23 0.11"22:19:06 UMAP embedding parameters a = 1.544 b = 0.9058
22:19:06 Read 1203 rows and found 38 numeric columns
22:19:06 Using Annoy for neighbor search, n_neighbors = 23
22:19:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:19:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757f6c2c8
22:19:06 Searching Annoy index using 1 thread, search_k = 2300
22:19:07 Annoy recall = 100%
22:19:08 Commencing smooth kNN distance calibration using 1 thread
22:19:13 Initializing from normalized Laplacian + noise
22:19:13 Commencing optimization for 500 epochs, with 34814 positive edges
22:19:18 Optimization finished
[1] "23 0.12"22:19:18 UMAP embedding parameters a = 1.51 b = 0.9165
22:19:18 Read 1203 rows and found 38 numeric columns
22:19:18 Using Annoy for neighbor search, n_neighbors = 23
22:19:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:19:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87754eccf2
22:19:18 Searching Annoy index using 1 thread, search_k = 2300
22:19:18 Annoy recall = 100%
22:19:21 Commencing smooth kNN distance calibration using 1 thread
22:19:24 Initializing from normalized Laplacian + noise
22:19:24 Commencing optimization for 500 epochs, with 34814 positive edges
22:19:29 Optimization finished
[1] "23 0.13"22:19:29 UMAP embedding parameters a = 1.478 b = 0.9272
22:19:29 Read 1203 rows and found 38 numeric columns
22:19:29 Using Annoy for neighbor search, n_neighbors = 23
22:19:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:19:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776aaa047
22:19:29 Searching Annoy index using 1 thread, search_k = 2300
22:19:29 Annoy recall = 100%
22:19:32 Commencing smooth kNN distance calibration using 1 thread
22:19:36 Initializing from normalized Laplacian + noise
22:19:36 Commencing optimization for 500 epochs, with 34814 positive edges
22:19:41 Optimization finished
[1] "23 0.14"22:19:41 UMAP embedding parameters a = 1.446 b = 0.938
22:19:41 Read 1203 rows and found 38 numeric columns
22:19:41 Using Annoy for neighbor search, n_neighbors = 23
22:19:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:19:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87585e1d92
22:19:42 Searching Annoy index using 1 thread, search_k = 2300
22:19:42 Annoy recall = 100%
22:19:44 Commencing smooth kNN distance calibration using 1 thread
22:19:48 Initializing from normalized Laplacian + noise
22:19:48 Commencing optimization for 500 epochs, with 34814 positive edges
22:19:52 Optimization finished
[1] "23 0.15"22:19:53 UMAP embedding parameters a = 1.414 b = 0.9488
22:19:53 Read 1203 rows and found 38 numeric columns
22:19:53 Using Annoy for neighbor search, n_neighbors = 23
22:19:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:19:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87334b1601
22:19:53 Searching Annoy index using 1 thread, search_k = 2300
22:19:53 Annoy recall = 100%
22:19:55 Commencing smooth kNN distance calibration using 1 thread
22:19:59 Initializing from normalized Laplacian + noise
22:19:59 Commencing optimization for 500 epochs, with 34814 positive edges
22:20:03 Optimization finished
[1] "23 0.16"22:20:03 UMAP embedding parameters a = 1.383 b = 0.9596
22:20:03 Read 1203 rows and found 38 numeric columns
22:20:03 Using Annoy for neighbor search, n_neighbors = 23
22:20:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777a999c0
22:20:04 Searching Annoy index using 1 thread, search_k = 2300
22:20:04 Annoy recall = 100%
22:20:06 Commencing smooth kNN distance calibration using 1 thread
22:20:09 Initializing from normalized Laplacian + noise
22:20:09 Commencing optimization for 500 epochs, with 34814 positive edges
22:20:14 Optimization finished
[1] "23 0.17"22:20:14 UMAP embedding parameters a = 1.352 b = 0.9704
22:20:14 Read 1203 rows and found 38 numeric columns
22:20:14 Using Annoy for neighbor search, n_neighbors = 23
22:20:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87419cd050
22:20:14 Searching Annoy index using 1 thread, search_k = 2300
22:20:14 Annoy recall = 100%
22:20:16 Commencing smooth kNN distance calibration using 1 thread
22:20:20 Initializing from normalized Laplacian + noise
22:20:20 Commencing optimization for 500 epochs, with 34814 positive edges
22:20:24 Optimization finished
[1] "23 0.18"22:20:24 UMAP embedding parameters a = 1.321 b = 0.9813
22:20:24 Read 1203 rows and found 38 numeric columns
22:20:24 Using Annoy for neighbor search, n_neighbors = 23
22:20:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749466e35
22:20:24 Searching Annoy index using 1 thread, search_k = 2300
22:20:25 Annoy recall = 100%
22:20:26 Commencing smooth kNN distance calibration using 1 thread
22:20:30 Initializing from normalized Laplacian + noise
22:20:30 Commencing optimization for 500 epochs, with 34814 positive edges
22:20:34 Optimization finished
[1] "23 0.19"22:20:35 UMAP embedding parameters a = 1.292 b = 0.9921
22:20:35 Read 1203 rows and found 38 numeric columns
22:20:35 Using Annoy for neighbor search, n_neighbors = 23
22:20:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87306b2c31
22:20:35 Searching Annoy index using 1 thread, search_k = 2300
22:20:35 Annoy recall = 100%
22:20:37 Commencing smooth kNN distance calibration using 1 thread
22:20:41 Initializing from normalized Laplacian + noise
22:20:41 Commencing optimization for 500 epochs, with 34814 positive edges
22:20:45 Optimization finished
[1] "23 0.2"22:20:45 UMAP embedding parameters a = 1.262 b = 1.003
22:20:45 Read 1203 rows and found 38 numeric columns
22:20:45 Using Annoy for neighbor search, n_neighbors = 23
22:20:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87659977cf
22:20:45 Searching Annoy index using 1 thread, search_k = 2300
22:20:46 Annoy recall = 100%
22:20:47 Commencing smooth kNN distance calibration using 1 thread
22:20:51 Initializing from normalized Laplacian + noise
22:20:51 Commencing optimization for 500 epochs, with 34814 positive edges
22:20:55 Optimization finished
[1] "24 0"22:20:55 UMAP embedding parameters a = 1.933 b = 0.7905
22:20:55 Read 1203 rows and found 38 numeric columns
22:20:55 Using Annoy for neighbor search, n_neighbors = 24
22:20:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f066175
22:20:56 Searching Annoy index using 1 thread, search_k = 2400
22:20:56 Annoy recall = 100%
22:20:58 Commencing smooth kNN distance calibration using 1 thread
22:21:01 Initializing from normalized Laplacian + noise
22:21:01 Commencing optimization for 500 epochs, with 36294 positive edges
22:21:06 Optimization finished
[1] "24 0.01"22:21:06 UMAP embedding parameters a = 1.896 b = 0.8006
22:21:06 Read 1203 rows and found 38 numeric columns
22:21:06 Using Annoy for neighbor search, n_neighbors = 24
22:21:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:21:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737ae12c
22:21:06 Searching Annoy index using 1 thread, search_k = 2400
22:21:06 Annoy recall = 100%
22:21:08 Commencing smooth kNN distance calibration using 1 thread
22:21:12 Initializing from normalized Laplacian + noise
22:21:12 Commencing optimization for 500 epochs, with 36294 positive edges
22:21:16 Optimization finished
[1] "24 0.02"22:21:16 UMAP embedding parameters a = 1.859 b = 0.8109
22:21:16 Read 1203 rows and found 38 numeric columns
22:21:16 Using Annoy for neighbor search, n_neighbors = 24
22:21:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:21:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730346f6d
22:21:17 Searching Annoy index using 1 thread, search_k = 2400
22:21:17 Annoy recall = 100%
22:21:19 Commencing smooth kNN distance calibration using 1 thread
22:21:22 Initializing from normalized Laplacian + noise
22:21:22 Commencing optimization for 500 epochs, with 36294 positive edges
22:21:27 Optimization finished
[1] "24 0.03"22:21:27 UMAP embedding parameters a = 1.822 b = 0.8212
22:21:27 Read 1203 rows and found 38 numeric columns
22:21:27 Using Annoy for neighbor search, n_neighbors = 24
22:21:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:21:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875121940a
22:21:27 Searching Annoy index using 1 thread, search_k = 2400
22:21:27 Annoy recall = 100%
22:21:29 Commencing smooth kNN distance calibration using 1 thread
22:21:33 Initializing from normalized Laplacian + noise
22:21:33 Commencing optimization for 500 epochs, with 36294 positive edges
22:21:37 Optimization finished
[1] "24 0.04"22:21:37 UMAP embedding parameters a = 1.786 b = 0.8316
22:21:37 Read 1203 rows and found 38 numeric columns
22:21:37 Using Annoy for neighbor search, n_neighbors = 24
22:21:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:21:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b3f90bc
22:21:38 Searching Annoy index using 1 thread, search_k = 2400
22:21:38 Annoy recall = 100%
22:21:40 Commencing smooth kNN distance calibration using 1 thread
22:21:43 Initializing from normalized Laplacian + noise
22:21:43 Commencing optimization for 500 epochs, with 36294 positive edges
22:21:48 Optimization finished
[1] "24 0.05"22:21:48 UMAP embedding parameters a = 1.75 b = 0.8421
22:21:48 Read 1203 rows and found 38 numeric columns
22:21:48 Using Annoy for neighbor search, n_neighbors = 24
22:21:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:21:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726570ed7
22:21:48 Searching Annoy index using 1 thread, search_k = 2400
22:21:48 Annoy recall = 100%
22:21:50 Commencing smooth kNN distance calibration using 1 thread
22:21:54 Initializing from normalized Laplacian + noise
22:21:54 Commencing optimization for 500 epochs, with 36294 positive edges
22:21:58 Optimization finished
[1] "24 0.06"22:21:58 UMAP embedding parameters a = 1.715 b = 0.8526
22:21:58 Read 1203 rows and found 38 numeric columns
22:21:58 Using Annoy for neighbor search, n_neighbors = 24
22:21:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:21:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c1f5590
22:21:58 Searching Annoy index using 1 thread, search_k = 2400
22:21:59 Annoy recall = 100%
22:22:00 Commencing smooth kNN distance calibration using 1 thread
22:22:04 Initializing from normalized Laplacian + noise
22:22:04 Commencing optimization for 500 epochs, with 36294 positive edges
22:22:08 Optimization finished
[1] "24 0.07"22:22:08 UMAP embedding parameters a = 1.68 b = 0.8631
22:22:08 Read 1203 rows and found 38 numeric columns
22:22:08 Using Annoy for neighbor search, n_neighbors = 24
22:22:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:22:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734e7ce68
22:22:09 Searching Annoy index using 1 thread, search_k = 2400
22:22:09 Annoy recall = 100%
22:22:11 Commencing smooth kNN distance calibration using 1 thread
22:22:14 Initializing from normalized Laplacian + noise
22:22:14 Commencing optimization for 500 epochs, with 36294 positive edges
22:22:19 Optimization finished
[1] "24 0.08"22:22:19 UMAP embedding parameters a = 1.645 b = 0.8737
22:22:19 Read 1203 rows and found 38 numeric columns
22:22:19 Using Annoy for neighbor search, n_neighbors = 24
22:22:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:22:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755e97b49
22:22:19 Searching Annoy index using 1 thread, search_k = 2400
22:22:19 Annoy recall = 100%
22:22:21 Commencing smooth kNN distance calibration using 1 thread
22:22:25 Initializing from normalized Laplacian + noise
22:22:25 Commencing optimization for 500 epochs, with 36294 positive edges
22:22:29 Optimization finished
[1] "24 0.09"22:22:29 UMAP embedding parameters a = 1.611 b = 0.8844
22:22:29 Read 1203 rows and found 38 numeric columns
22:22:29 Using Annoy for neighbor search, n_neighbors = 24
22:22:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:22:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87632ddffc
22:22:30 Searching Annoy index using 1 thread, search_k = 2400
22:22:30 Annoy recall = 100%
22:22:32 Commencing smooth kNN distance calibration using 1 thread
22:22:35 Initializing from normalized Laplacian + noise
22:22:35 Commencing optimization for 500 epochs, with 36294 positive edges
22:22:39 Optimization finished
[1] "24 0.1"22:22:40 UMAP embedding parameters a = 1.577 b = 0.8951
22:22:40 Read 1203 rows and found 38 numeric columns
22:22:40 Using Annoy for neighbor search, n_neighbors = 24
22:22:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:22:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739aab6dd
22:22:40 Searching Annoy index using 1 thread, search_k = 2400
22:22:40 Annoy recall = 100%
22:22:42 Commencing smooth kNN distance calibration using 1 thread
22:22:46 Initializing from normalized Laplacian + noise
22:22:46 Commencing optimization for 500 epochs, with 36294 positive edges
22:22:50 Optimization finished
[1] "24 0.11"22:22:50 UMAP embedding parameters a = 1.544 b = 0.9058
22:22:50 Read 1203 rows and found 38 numeric columns
22:22:50 Using Annoy for neighbor search, n_neighbors = 24
22:22:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:22:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761cf8d80
22:22:50 Searching Annoy index using 1 thread, search_k = 2400
22:22:51 Annoy recall = 100%
22:22:52 Commencing smooth kNN distance calibration using 1 thread
22:22:56 Initializing from normalized Laplacian + noise
22:22:56 Commencing optimization for 500 epochs, with 36294 positive edges
22:23:00 Optimization finished
[1] "24 0.12"22:23:01 UMAP embedding parameters a = 1.51 b = 0.9165
22:23:01 Read 1203 rows and found 38 numeric columns
22:23:01 Using Annoy for neighbor search, n_neighbors = 24
22:23:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a2422e0
22:23:01 Searching Annoy index using 1 thread, search_k = 2400
22:23:01 Annoy recall = 100%
22:23:03 Commencing smooth kNN distance calibration using 1 thread
22:23:07 Initializing from normalized Laplacian + noise
22:23:07 Commencing optimization for 500 epochs, with 36294 positive edges
22:23:11 Optimization finished
[1] "24 0.13"22:23:11 UMAP embedding parameters a = 1.478 b = 0.9272
22:23:11 Read 1203 rows and found 38 numeric columns
22:23:11 Using Annoy for neighbor search, n_neighbors = 24
22:23:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871cd14fea
22:23:11 Searching Annoy index using 1 thread, search_k = 2400
22:23:12 Annoy recall = 100%
22:23:13 Commencing smooth kNN distance calibration using 1 thread
22:23:17 Initializing from normalized Laplacian + noise
22:23:17 Commencing optimization for 500 epochs, with 36294 positive edges
22:23:21 Optimization finished
[1] "24 0.14"22:23:22 UMAP embedding parameters a = 1.446 b = 0.938
22:23:22 Read 1203 rows and found 38 numeric columns
22:23:22 Using Annoy for neighbor search, n_neighbors = 24
22:23:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871abf1df9
22:23:22 Searching Annoy index using 1 thread, search_k = 2400
22:23:22 Annoy recall = 100%
22:23:24 Commencing smooth kNN distance calibration using 1 thread
22:23:28 Initializing from normalized Laplacian + noise
22:23:28 Commencing optimization for 500 epochs, with 36294 positive edges
22:23:32 Optimization finished
[1] "24 0.15"22:23:32 UMAP embedding parameters a = 1.414 b = 0.9488
22:23:32 Read 1203 rows and found 38 numeric columns
22:23:32 Using Annoy for neighbor search, n_neighbors = 24
22:23:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87428fbe28
22:23:33 Searching Annoy index using 1 thread, search_k = 2400
22:23:33 Annoy recall = 100%
22:23:35 Commencing smooth kNN distance calibration using 1 thread
22:23:39 Initializing from normalized Laplacian + noise
22:23:39 Commencing optimization for 500 epochs, with 36294 positive edges
22:23:44 Optimization finished
[1] "24 0.16"22:23:44 UMAP embedding parameters a = 1.383 b = 0.9596
22:23:44 Read 1203 rows and found 38 numeric columns
22:23:44 Using Annoy for neighbor search, n_neighbors = 24
22:23:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741a36c19
22:23:44 Searching Annoy index using 1 thread, search_k = 2400
22:23:44 Annoy recall = 100%
22:23:46 Commencing smooth kNN distance calibration using 1 thread
22:23:50 Initializing from normalized Laplacian + noise
22:23:50 Commencing optimization for 500 epochs, with 36294 positive edges
22:23:55 Optimization finished
[1] "24 0.17"22:23:55 UMAP embedding parameters a = 1.352 b = 0.9704
22:23:55 Read 1203 rows and found 38 numeric columns
22:23:55 Using Annoy for neighbor search, n_neighbors = 24
22:23:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87322ec044
22:23:55 Searching Annoy index using 1 thread, search_k = 2400
22:23:56 Annoy recall = 100%
22:23:58 Commencing smooth kNN distance calibration using 1 thread
22:24:02 Initializing from normalized Laplacian + noise
22:24:02 Commencing optimization for 500 epochs, with 36294 positive edges
22:24:06 Optimization finished
[1] "24 0.18"22:24:07 UMAP embedding parameters a = 1.321 b = 0.9813
22:24:07 Read 1203 rows and found 38 numeric columns
22:24:07 Using Annoy for neighbor search, n_neighbors = 24
22:24:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:24:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730e362b2
22:24:07 Searching Annoy index using 1 thread, search_k = 2400
22:24:07 Annoy recall = 100%
22:24:09 Commencing smooth kNN distance calibration using 1 thread
22:24:13 Initializing from normalized Laplacian + noise
22:24:13 Commencing optimization for 500 epochs, with 36294 positive edges
22:24:18 Optimization finished
[1] "24 0.19"22:24:18 UMAP embedding parameters a = 1.292 b = 0.9921
22:24:18 Read 1203 rows and found 38 numeric columns
22:24:18 Using Annoy for neighbor search, n_neighbors = 24
22:24:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:24:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87301c02b6
22:24:18 Searching Annoy index using 1 thread, search_k = 2400
22:24:18 Annoy recall = 100%
22:24:20 Commencing smooth kNN distance calibration using 1 thread
22:24:24 Initializing from normalized Laplacian + noise
22:24:24 Commencing optimization for 500 epochs, with 36294 positive edges
22:24:29 Optimization finished
[1] "24 0.2"22:24:29 UMAP embedding parameters a = 1.262 b = 1.003
22:24:29 Read 1203 rows and found 38 numeric columns
22:24:29 Using Annoy for neighbor search, n_neighbors = 24
22:24:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:24:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744798ddb
22:24:29 Searching Annoy index using 1 thread, search_k = 2400
22:24:30 Annoy recall = 100%
22:24:32 Commencing smooth kNN distance calibration using 1 thread
22:24:36 Initializing from normalized Laplacian + noise
22:24:36 Commencing optimization for 500 epochs, with 36294 positive edges
22:24:40 Optimization finished
[1] "25 0"22:24:40 UMAP embedding parameters a = 1.933 b = 0.7905
22:24:40 Read 1203 rows and found 38 numeric columns
22:24:40 Using Annoy for neighbor search, n_neighbors = 25
22:24:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:24:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878da257a
22:24:40 Searching Annoy index using 1 thread, search_k = 2500
22:24:41 Annoy recall = 100%
22:24:43 Commencing smooth kNN distance calibration using 1 thread
22:24:47 Initializing from normalized Laplacian + noise
22:24:47 Commencing optimization for 500 epochs, with 37764 positive edges
22:24:51 Optimization finished
[1] "25 0.01"22:24:52 UMAP embedding parameters a = 1.896 b = 0.8006
22:24:52 Read 1203 rows and found 38 numeric columns
22:24:52 Using Annoy for neighbor search, n_neighbors = 25
22:24:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:24:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87256acfa8
22:24:52 Searching Annoy index using 1 thread, search_k = 2500
22:24:52 Annoy recall = 100%
22:24:54 Commencing smooth kNN distance calibration using 1 thread
22:24:59 Initializing from normalized Laplacian + noise
22:24:59 Commencing optimization for 500 epochs, with 37764 positive edges
22:25:04 Optimization finished
[1] "25 0.02"22:25:04 UMAP embedding parameters a = 1.859 b = 0.8109
22:25:04 Read 1203 rows and found 38 numeric columns
22:25:04 Using Annoy for neighbor search, n_neighbors = 25
22:25:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:25:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b242e23
22:25:04 Searching Annoy index using 1 thread, search_k = 2500
22:25:05 Annoy recall = 100%
22:25:07 Commencing smooth kNN distance calibration using 1 thread
22:25:11 Initializing from normalized Laplacian + noise
22:25:11 Commencing optimization for 500 epochs, with 37764 positive edges
22:25:16 Optimization finished
[1] "25 0.03"22:25:17 UMAP embedding parameters a = 1.822 b = 0.8212
22:25:17 Read 1203 rows and found 38 numeric columns
22:25:17 Using Annoy for neighbor search, n_neighbors = 25
22:25:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:25:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876138430c
22:25:17 Searching Annoy index using 1 thread, search_k = 2500
22:25:17 Annoy recall = 100%
22:25:19 Commencing smooth kNN distance calibration using 1 thread
22:25:23 Initializing from normalized Laplacian + noise
22:25:23 Commencing optimization for 500 epochs, with 37764 positive edges
22:25:28 Optimization finished
[1] "25 0.04"22:25:28 UMAP embedding parameters a = 1.786 b = 0.8316
22:25:28 Read 1203 rows and found 38 numeric columns
22:25:28 Using Annoy for neighbor search, n_neighbors = 25
22:25:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:25:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758b5e5a9
22:25:28 Searching Annoy index using 1 thread, search_k = 2500
22:25:29 Annoy recall = 100%
22:25:31 Commencing smooth kNN distance calibration using 1 thread
22:25:35 Initializing from normalized Laplacian + noise
22:25:35 Commencing optimization for 500 epochs, with 37764 positive edges
22:25:39 Optimization finished
[1] "25 0.05"22:25:40 UMAP embedding parameters a = 1.75 b = 0.8421
22:25:40 Read 1203 rows and found 38 numeric columns
22:25:40 Using Annoy for neighbor search, n_neighbors = 25
22:25:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:25:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732cdc7e3
22:25:40 Searching Annoy index using 1 thread, search_k = 2500
22:25:40 Annoy recall = 100%
22:25:43 Commencing smooth kNN distance calibration using 1 thread
22:25:47 Initializing from normalized Laplacian + noise
22:25:47 Commencing optimization for 500 epochs, with 37764 positive edges
22:25:52 Optimization finished
[1] "25 0.06"22:25:52 UMAP embedding parameters a = 1.715 b = 0.8526
22:25:52 Read 1203 rows and found 38 numeric columns
22:25:52 Using Annoy for neighbor search, n_neighbors = 25
22:25:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:25:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722d5135c
22:25:52 Searching Annoy index using 1 thread, search_k = 2500
22:25:53 Annoy recall = 100%
22:25:55 Commencing smooth kNN distance calibration using 1 thread
22:25:59 Initializing from normalized Laplacian + noise
22:25:59 Commencing optimization for 500 epochs, with 37764 positive edges
22:26:04 Optimization finished
[1] "25 0.07"22:26:04 UMAP embedding parameters a = 1.68 b = 0.8631
22:26:04 Read 1203 rows and found 38 numeric columns
22:26:04 Using Annoy for neighbor search, n_neighbors = 25
22:26:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:26:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721fc53de
22:26:04 Searching Annoy index using 1 thread, search_k = 2500
22:26:04 Annoy recall = 100%
22:26:06 Commencing smooth kNN distance calibration using 1 thread
22:26:10 Initializing from normalized Laplacian + noise
22:26:10 Commencing optimization for 500 epochs, with 37764 positive edges
22:26:15 Optimization finished
[1] "25 0.08"22:26:15 UMAP embedding parameters a = 1.645 b = 0.8737
22:26:15 Read 1203 rows and found 38 numeric columns
22:26:15 Using Annoy for neighbor search, n_neighbors = 25
22:26:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:26:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876338f414
22:26:15 Searching Annoy index using 1 thread, search_k = 2500
22:26:16 Annoy recall = 100%
22:26:17 Commencing smooth kNN distance calibration using 1 thread
22:26:21 Initializing from normalized Laplacian + noise
22:26:21 Commencing optimization for 500 epochs, with 37764 positive edges
22:26:26 Optimization finished
[1] "25 0.09"22:26:26 UMAP embedding parameters a = 1.611 b = 0.8844
22:26:26 Read 1203 rows and found 38 numeric columns
22:26:26 Using Annoy for neighbor search, n_neighbors = 25
22:26:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:26:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8786e8b2b
22:26:26 Searching Annoy index using 1 thread, search_k = 2500
22:26:27 Annoy recall = 100%
22:26:29 Commencing smooth kNN distance calibration using 1 thread
22:26:32 Initializing from normalized Laplacian + noise
22:26:32 Commencing optimization for 500 epochs, with 37764 positive edges
22:26:37 Optimization finished
[1] "25 0.1"22:26:37 UMAP embedding parameters a = 1.577 b = 0.8951
22:26:37 Read 1203 rows and found 38 numeric columns
22:26:37 Using Annoy for neighbor search, n_neighbors = 25
22:26:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:26:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876102b553
22:26:37 Searching Annoy index using 1 thread, search_k = 2500
22:26:38 Annoy recall = 100%
22:26:40 Commencing smooth kNN distance calibration using 1 thread
22:26:43 Initializing from normalized Laplacian + noise
22:26:43 Commencing optimization for 500 epochs, with 37764 positive edges
22:26:48 Optimization finished
[1] "25 0.11"22:26:48 UMAP embedding parameters a = 1.544 b = 0.9058
22:26:48 Read 1203 rows and found 38 numeric columns
22:26:48 Using Annoy for neighbor search, n_neighbors = 25
22:26:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:26:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766b3d540
22:26:48 Searching Annoy index using 1 thread, search_k = 2500
22:26:49 Annoy recall = 100%
22:26:51 Commencing smooth kNN distance calibration using 1 thread
22:26:54 Initializing from normalized Laplacian + noise
22:26:55 Commencing optimization for 500 epochs, with 37764 positive edges
22:26:59 Optimization finished
[1] "25 0.12"22:26:59 UMAP embedding parameters a = 1.51 b = 0.9165
22:26:59 Read 1203 rows and found 38 numeric columns
22:26:59 Using Annoy for neighbor search, n_neighbors = 25
22:26:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:26:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738a2fa99
22:26:59 Searching Annoy index using 1 thread, search_k = 2500
22:27:00 Annoy recall = 100%
22:27:02 Commencing smooth kNN distance calibration using 1 thread
22:27:05 Initializing from normalized Laplacian + noise
22:27:05 Commencing optimization for 500 epochs, with 37764 positive edges
22:27:10 Optimization finished
[1] "25 0.13"22:27:10 UMAP embedding parameters a = 1.478 b = 0.9272
22:27:10 Read 1203 rows and found 38 numeric columns
22:27:10 Using Annoy for neighbor search, n_neighbors = 25
22:27:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:27:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873224495d
22:27:10 Searching Annoy index using 1 thread, search_k = 2500
22:27:11 Annoy recall = 100%
22:27:12 Commencing smooth kNN distance calibration using 1 thread
22:27:16 Initializing from normalized Laplacian + noise
22:27:16 Commencing optimization for 500 epochs, with 37764 positive edges
22:27:21 Optimization finished
[1] "25 0.14"22:27:21 UMAP embedding parameters a = 1.446 b = 0.938
22:27:21 Read 1203 rows and found 38 numeric columns
22:27:21 Using Annoy for neighbor search, n_neighbors = 25
22:27:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:27:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761f365fd
22:27:21 Searching Annoy index using 1 thread, search_k = 2500
22:27:22 Annoy recall = 100%
22:27:24 Commencing smooth kNN distance calibration using 1 thread
22:27:27 Initializing from normalized Laplacian + noise
22:27:27 Commencing optimization for 500 epochs, with 37764 positive edges
22:27:32 Optimization finished
[1] "25 0.15"22:27:32 UMAP embedding parameters a = 1.414 b = 0.9488
22:27:32 Read 1203 rows and found 38 numeric columns
22:27:32 Using Annoy for neighbor search, n_neighbors = 25
22:27:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:27:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875efa0970
22:27:32 Searching Annoy index using 1 thread, search_k = 2500
22:27:33 Annoy recall = 100%
22:27:34 Commencing smooth kNN distance calibration using 1 thread
22:27:38 Initializing from normalized Laplacian + noise
22:27:38 Commencing optimization for 500 epochs, with 37764 positive edges
22:27:43 Optimization finished
[1] "25 0.16"22:27:43 UMAP embedding parameters a = 1.383 b = 0.9596
22:27:43 Read 1203 rows and found 38 numeric columns
22:27:43 Using Annoy for neighbor search, n_neighbors = 25
22:27:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:27:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e439eed
22:27:43 Searching Annoy index using 1 thread, search_k = 2500
22:27:44 Annoy recall = 100%
22:27:45 Commencing smooth kNN distance calibration using 1 thread
22:27:49 Initializing from normalized Laplacian + noise
22:27:49 Commencing optimization for 500 epochs, with 37764 positive edges
22:27:54 Optimization finished
[1] "25 0.17"22:27:54 UMAP embedding parameters a = 1.352 b = 0.9704
22:27:54 Read 1203 rows and found 38 numeric columns
22:27:54 Using Annoy for neighbor search, n_neighbors = 25
22:27:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:27:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716db3465
22:27:54 Searching Annoy index using 1 thread, search_k = 2500
22:27:55 Annoy recall = 100%
22:27:56 Commencing smooth kNN distance calibration using 1 thread
22:28:00 Initializing from normalized Laplacian + noise
22:28:00 Commencing optimization for 500 epochs, with 37764 positive edges
22:28:05 Optimization finished
[1] "25 0.18"22:28:05 UMAP embedding parameters a = 1.321 b = 0.9813
22:28:05 Read 1203 rows and found 38 numeric columns
22:28:05 Using Annoy for neighbor search, n_neighbors = 25
22:28:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:28:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734e384b9
22:28:05 Searching Annoy index using 1 thread, search_k = 2500
22:28:05 Annoy recall = 100%
22:28:07 Commencing smooth kNN distance calibration using 1 thread
22:28:11 Initializing from normalized Laplacian + noise
22:28:11 Commencing optimization for 500 epochs, with 37764 positive edges
22:28:16 Optimization finished
[1] "25 0.19"22:28:16 UMAP embedding parameters a = 1.292 b = 0.9921
22:28:16 Read 1203 rows and found 38 numeric columns
22:28:16 Using Annoy for neighbor search, n_neighbors = 25
22:28:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:28:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721717ee9
22:28:16 Searching Annoy index using 1 thread, search_k = 2500
22:28:17 Annoy recall = 100%
22:28:18 Commencing smooth kNN distance calibration using 1 thread
22:28:22 Initializing from normalized Laplacian + noise
22:28:22 Commencing optimization for 500 epochs, with 37764 positive edges
22:28:27 Optimization finished
[1] "25 0.2"22:28:27 UMAP embedding parameters a = 1.262 b = 1.003
22:28:27 Read 1203 rows and found 38 numeric columns
22:28:27 Using Annoy for neighbor search, n_neighbors = 25
22:28:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:28:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875085eb42
22:28:27 Searching Annoy index using 1 thread, search_k = 2500
22:28:28 Annoy recall = 100%
22:28:30 Commencing smooth kNN distance calibration using 1 thread
22:28:33 Initializing from normalized Laplacian + noise
22:28:33 Commencing optimization for 500 epochs, with 37764 positive edges
22:28:38 Optimization finished
[1] "26 0"22:28:38 UMAP embedding parameters a = 1.933 b = 0.7905
22:28:38 Read 1203 rows and found 38 numeric columns
22:28:38 Using Annoy for neighbor search, n_neighbors = 26
22:28:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:28:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716b31239
22:28:38 Searching Annoy index using 1 thread, search_k = 2600
22:28:39 Annoy recall = 100%
22:28:41 Commencing smooth kNN distance calibration using 1 thread
22:28:44 Initializing from normalized Laplacian + noise
22:28:44 Commencing optimization for 500 epochs, with 39224 positive edges
22:28:49 Optimization finished
[1] "26 0.01"22:28:49 UMAP embedding parameters a = 1.896 b = 0.8006
22:28:49 Read 1203 rows and found 38 numeric columns
22:28:49 Using Annoy for neighbor search, n_neighbors = 26
22:28:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:28:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b95a1ca
22:28:49 Searching Annoy index using 1 thread, search_k = 2600
22:28:50 Annoy recall = 100%
22:28:52 Commencing smooth kNN distance calibration using 1 thread
22:28:56 Initializing from normalized Laplacian + noise
22:28:56 Commencing optimization for 500 epochs, with 39224 positive edges
22:29:00 Optimization finished
[1] "26 0.02"22:29:00 UMAP embedding parameters a = 1.859 b = 0.8109
22:29:00 Read 1203 rows and found 38 numeric columns
22:29:00 Using Annoy for neighbor search, n_neighbors = 26
22:29:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:29:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d573b2c
22:29:01 Searching Annoy index using 1 thread, search_k = 2600
22:29:01 Annoy recall = 100%
22:29:03 Commencing smooth kNN distance calibration using 1 thread
22:29:07 Initializing from normalized Laplacian + noise
22:29:07 Commencing optimization for 500 epochs, with 39224 positive edges
22:29:11 Optimization finished
[1] "26 0.03"22:29:11 UMAP embedding parameters a = 1.822 b = 0.8212
22:29:11 Read 1203 rows and found 38 numeric columns
22:29:11 Using Annoy for neighbor search, n_neighbors = 26
22:29:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:29:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731723033
22:29:12 Searching Annoy index using 1 thread, search_k = 2600
22:29:12 Annoy recall = 100%
22:29:14 Commencing smooth kNN distance calibration using 1 thread
22:29:18 Initializing from normalized Laplacian + noise
22:29:18 Commencing optimization for 500 epochs, with 39224 positive edges
22:29:22 Optimization finished
[1] "26 0.04"22:29:23 UMAP embedding parameters a = 1.786 b = 0.8316
22:29:23 Read 1203 rows and found 38 numeric columns
22:29:23 Using Annoy for neighbor search, n_neighbors = 26
22:29:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:29:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e255ff2
22:29:23 Searching Annoy index using 1 thread, search_k = 2600
22:29:23 Annoy recall = 100%
22:29:25 Commencing smooth kNN distance calibration using 1 thread
22:29:29 Initializing from normalized Laplacian + noise
22:29:29 Commencing optimization for 500 epochs, with 39224 positive edges
22:29:34 Optimization finished
[1] "26 0.05"22:29:34 UMAP embedding parameters a = 1.75 b = 0.8421
22:29:34 Read 1203 rows and found 38 numeric columns
22:29:34 Using Annoy for neighbor search, n_neighbors = 26
22:29:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:29:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872efaa746
22:29:34 Searching Annoy index using 1 thread, search_k = 2600
22:29:34 Annoy recall = 100%
22:29:36 Commencing smooth kNN distance calibration using 1 thread
22:29:40 Initializing from normalized Laplacian + noise
22:29:40 Commencing optimization for 500 epochs, with 39224 positive edges
22:29:45 Optimization finished
[1] "26 0.06"22:29:45 UMAP embedding parameters a = 1.715 b = 0.8526
22:29:45 Read 1203 rows and found 38 numeric columns
22:29:45 Using Annoy for neighbor search, n_neighbors = 26
22:29:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:29:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763a0f077
22:29:45 Searching Annoy index using 1 thread, search_k = 2600
22:29:45 Annoy recall = 100%
22:29:47 Commencing smooth kNN distance calibration using 1 thread
22:29:51 Initializing from normalized Laplacian + noise
22:29:51 Commencing optimization for 500 epochs, with 39224 positive edges
22:29:56 Optimization finished
[1] "26 0.07"22:29:56 UMAP embedding parameters a = 1.68 b = 0.8631
22:29:56 Read 1203 rows and found 38 numeric columns
22:29:56 Using Annoy for neighbor search, n_neighbors = 26
22:29:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:29:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f08c2a4
22:29:56 Searching Annoy index using 1 thread, search_k = 2600
22:29:57 Annoy recall = 100%
22:29:59 Commencing smooth kNN distance calibration using 1 thread
22:30:03 Initializing from normalized Laplacian + noise
22:30:03 Commencing optimization for 500 epochs, with 39224 positive edges
22:30:07 Optimization finished
[1] "26 0.08"22:30:07 UMAP embedding parameters a = 1.645 b = 0.8737
22:30:07 Read 1203 rows and found 38 numeric columns
22:30:07 Using Annoy for neighbor search, n_neighbors = 26
22:30:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:30:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f16a9fc
22:30:08 Searching Annoy index using 1 thread, search_k = 2600
22:30:08 Annoy recall = 100%
22:30:10 Commencing smooth kNN distance calibration using 1 thread
22:30:14 Initializing from normalized Laplacian + noise
22:30:14 Commencing optimization for 500 epochs, with 39224 positive edges
22:30:18 Optimization finished
[1] "26 0.09"22:30:19 UMAP embedding parameters a = 1.611 b = 0.8844
22:30:19 Read 1203 rows and found 38 numeric columns
22:30:19 Using Annoy for neighbor search, n_neighbors = 26
22:30:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:30:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87281a7e53
22:30:19 Searching Annoy index using 1 thread, search_k = 2600
22:30:19 Annoy recall = 100%
22:30:21 Commencing smooth kNN distance calibration using 1 thread
22:30:25 Initializing from normalized Laplacian + noise
22:30:25 Commencing optimization for 500 epochs, with 39224 positive edges
22:30:29 Optimization finished
[1] "26 0.1"22:30:30 UMAP embedding parameters a = 1.577 b = 0.8951
22:30:30 Read 1203 rows and found 38 numeric columns
22:30:30 Using Annoy for neighbor search, n_neighbors = 26
22:30:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:30:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e2e81e
22:30:30 Searching Annoy index using 1 thread, search_k = 2600
22:30:30 Annoy recall = 100%
22:30:32 Commencing smooth kNN distance calibration using 1 thread
22:30:36 Initializing from normalized Laplacian + noise
22:30:36 Commencing optimization for 500 epochs, with 39224 positive edges
22:30:41 Optimization finished
[1] "26 0.11"22:30:41 UMAP embedding parameters a = 1.544 b = 0.9058
22:30:41 Read 1203 rows and found 38 numeric columns
22:30:41 Using Annoy for neighbor search, n_neighbors = 26
22:30:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:30:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748179a4
22:30:41 Searching Annoy index using 1 thread, search_k = 2600
22:30:41 Annoy recall = 100%
22:30:43 Commencing smooth kNN distance calibration using 1 thread
22:30:47 Initializing from normalized Laplacian + noise
22:30:47 Commencing optimization for 500 epochs, with 39224 positive edges
22:30:52 Optimization finished
[1] "26 0.12"22:30:52 UMAP embedding parameters a = 1.51 b = 0.9165
22:30:52 Read 1203 rows and found 38 numeric columns
22:30:52 Using Annoy for neighbor search, n_neighbors = 26
22:30:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:30:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87633eac76
22:30:52 Searching Annoy index using 1 thread, search_k = 2600
22:30:53 Annoy recall = 100%
22:30:55 Commencing smooth kNN distance calibration using 1 thread
22:30:59 Initializing from normalized Laplacian + noise
22:30:59 Commencing optimization for 500 epochs, with 39224 positive edges
22:31:03 Optimization finished
[1] "26 0.13"22:31:03 UMAP embedding parameters a = 1.478 b = 0.9272
22:31:03 Read 1203 rows and found 38 numeric columns
22:31:03 Using Annoy for neighbor search, n_neighbors = 26
22:31:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:31:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87691b2b2a
22:31:04 Searching Annoy index using 1 thread, search_k = 2600
22:31:04 Annoy recall = 100%
22:31:06 Commencing smooth kNN distance calibration using 1 thread
22:31:10 Initializing from normalized Laplacian + noise
22:31:10 Commencing optimization for 500 epochs, with 39224 positive edges
22:31:15 Optimization finished
[1] "26 0.14"22:31:15 UMAP embedding parameters a = 1.446 b = 0.938
22:31:15 Read 1203 rows and found 38 numeric columns
22:31:15 Using Annoy for neighbor search, n_neighbors = 26
22:31:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:31:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d375f4d
22:31:15 Searching Annoy index using 1 thread, search_k = 2600
22:31:15 Annoy recall = 100%
22:31:17 Commencing smooth kNN distance calibration using 1 thread
22:31:21 Initializing from normalized Laplacian + noise
22:31:21 Commencing optimization for 500 epochs, with 39224 positive edges
22:31:26 Optimization finished
[1] "26 0.15"22:31:26 UMAP embedding parameters a = 1.414 b = 0.9488
22:31:26 Read 1203 rows and found 38 numeric columns
22:31:26 Using Annoy for neighbor search, n_neighbors = 26
22:31:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:31:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87160c7459
22:31:26 Searching Annoy index using 1 thread, search_k = 2600
22:31:27 Annoy recall = 100%
22:31:29 Commencing smooth kNN distance calibration using 1 thread
22:31:33 Initializing from normalized Laplacian + noise
22:31:33 Commencing optimization for 500 epochs, with 39224 positive edges
22:31:37 Optimization finished
[1] "26 0.16"22:31:37 UMAP embedding parameters a = 1.383 b = 0.9596
22:31:37 Read 1203 rows and found 38 numeric columns
22:31:37 Using Annoy for neighbor search, n_neighbors = 26
22:31:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:31:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bf03e86
22:31:38 Searching Annoy index using 1 thread, search_k = 2600
22:31:38 Annoy recall = 100%
22:31:40 Commencing smooth kNN distance calibration using 1 thread
22:31:44 Initializing from normalized Laplacian + noise
22:31:44 Commencing optimization for 500 epochs, with 39224 positive edges
22:31:48 Optimization finished
[1] "26 0.17"22:31:49 UMAP embedding parameters a = 1.352 b = 0.9704
22:31:49 Read 1203 rows and found 38 numeric columns
22:31:49 Using Annoy for neighbor search, n_neighbors = 26
22:31:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:31:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f33b32b
22:31:49 Searching Annoy index using 1 thread, search_k = 2600
22:31:49 Annoy recall = 100%
22:31:51 Commencing smooth kNN distance calibration using 1 thread
22:31:55 Initializing from normalized Laplacian + noise
22:31:55 Commencing optimization for 500 epochs, with 39224 positive edges
22:32:00 Optimization finished
[1] "26 0.18"22:32:00 UMAP embedding parameters a = 1.321 b = 0.9813
22:32:00 Read 1203 rows and found 38 numeric columns
22:32:00 Using Annoy for neighbor search, n_neighbors = 26
22:32:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:32:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877945686d
22:32:00 Searching Annoy index using 1 thread, search_k = 2600
22:32:00 Annoy recall = 100%
22:32:02 Commencing smooth kNN distance calibration using 1 thread
22:32:07 Initializing from normalized Laplacian + noise
22:32:07 Commencing optimization for 500 epochs, with 39224 positive edges
22:32:11 Optimization finished
[1] "26 0.19"22:32:11 UMAP embedding parameters a = 1.292 b = 0.9921
22:32:11 Read 1203 rows and found 38 numeric columns
22:32:11 Using Annoy for neighbor search, n_neighbors = 26
22:32:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:32:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87145ec9b1
22:32:12 Searching Annoy index using 1 thread, search_k = 2600
22:32:12 Annoy recall = 100%
22:32:14 Commencing smooth kNN distance calibration using 1 thread
22:32:18 Initializing from normalized Laplacian + noise
22:32:18 Commencing optimization for 500 epochs, with 39224 positive edges
22:32:22 Optimization finished
[1] "26 0.2"22:32:23 UMAP embedding parameters a = 1.262 b = 1.003
22:32:23 Read 1203 rows and found 38 numeric columns
22:32:23 Using Annoy for neighbor search, n_neighbors = 26
22:32:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:32:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876036687e
22:32:23 Searching Annoy index using 1 thread, search_k = 2600
22:32:23 Annoy recall = 100%
22:32:25 Commencing smooth kNN distance calibration using 1 thread
22:32:29 Initializing from normalized Laplacian + noise
22:32:29 Commencing optimization for 500 epochs, with 39224 positive edges
22:32:34 Optimization finished
[1] "27 0"22:32:34 UMAP embedding parameters a = 1.933 b = 0.7905
22:32:34 Read 1203 rows and found 38 numeric columns
22:32:34 Using Annoy for neighbor search, n_neighbors = 27
22:32:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:32:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ff93dae
22:32:34 Searching Annoy index using 1 thread, search_k = 2700
22:32:35 Annoy recall = 100%
22:32:37 Commencing smooth kNN distance calibration using 1 thread
22:32:41 Initializing from normalized Laplacian + noise
22:32:41 Commencing optimization for 500 epochs, with 40700 positive edges
22:32:45 Optimization finished
[1] "27 0.01"22:32:45 UMAP embedding parameters a = 1.896 b = 0.8006
22:32:45 Read 1203 rows and found 38 numeric columns
22:32:45 Using Annoy for neighbor search, n_neighbors = 27
22:32:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:32:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d01c44a
22:32:46 Searching Annoy index using 1 thread, search_k = 2700
22:32:46 Annoy recall = 100%
22:32:48 Commencing smooth kNN distance calibration using 1 thread
22:32:52 Initializing from normalized Laplacian + noise
22:32:52 Commencing optimization for 500 epochs, with 40700 positive edges
22:32:57 Optimization finished
[1] "27 0.02"22:32:57 UMAP embedding parameters a = 1.859 b = 0.8109
22:32:57 Read 1203 rows and found 38 numeric columns
22:32:57 Using Annoy for neighbor search, n_neighbors = 27
22:32:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:32:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87125ab1db
22:32:57 Searching Annoy index using 1 thread, search_k = 2700
22:32:57 Annoy recall = 100%
22:32:59 Commencing smooth kNN distance calibration using 1 thread
22:33:04 Initializing from normalized Laplacian + noise
22:33:04 Commencing optimization for 500 epochs, with 40700 positive edges
22:33:08 Optimization finished
[1] "27 0.03"22:33:08 UMAP embedding parameters a = 1.822 b = 0.8212
22:33:08 Read 1203 rows and found 38 numeric columns
22:33:08 Using Annoy for neighbor search, n_neighbors = 27
22:33:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:33:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741eca3ab
22:33:09 Searching Annoy index using 1 thread, search_k = 2700
22:33:09 Annoy recall = 100%
22:33:11 Commencing smooth kNN distance calibration using 1 thread
22:33:15 Initializing from normalized Laplacian + noise
22:33:15 Commencing optimization for 500 epochs, with 40700 positive edges
22:33:20 Optimization finished
[1] "27 0.04"22:33:20 UMAP embedding parameters a = 1.786 b = 0.8316
22:33:20 Read 1203 rows and found 38 numeric columns
22:33:20 Using Annoy for neighbor search, n_neighbors = 27
22:33:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:33:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872bfbcdba
22:33:20 Searching Annoy index using 1 thread, search_k = 2700
22:33:20 Annoy recall = 100%
22:33:22 Commencing smooth kNN distance calibration using 1 thread
22:33:27 Initializing from normalized Laplacian + noise
22:33:27 Commencing optimization for 500 epochs, with 40700 positive edges
22:33:31 Optimization finished
[1] "27 0.05"22:33:31 UMAP embedding parameters a = 1.75 b = 0.8421
22:33:31 Read 1203 rows and found 38 numeric columns
22:33:31 Using Annoy for neighbor search, n_neighbors = 27
22:33:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:33:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87509e50c8
22:33:32 Searching Annoy index using 1 thread, search_k = 2700
22:33:32 Annoy recall = 100%
22:33:34 Commencing smooth kNN distance calibration using 1 thread
22:33:38 Initializing from normalized Laplacian + noise
22:33:38 Commencing optimization for 500 epochs, with 40700 positive edges
22:33:43 Optimization finished
[1] "27 0.06"22:33:43 UMAP embedding parameters a = 1.715 b = 0.8526
22:33:43 Read 1203 rows and found 38 numeric columns
22:33:43 Using Annoy for neighbor search, n_neighbors = 27
22:33:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:33:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758c7d810
22:33:43 Searching Annoy index using 1 thread, search_k = 2700
22:33:43 Annoy recall = 100%
22:33:45 Commencing smooth kNN distance calibration using 1 thread
22:33:49 Initializing from normalized Laplacian + noise
22:33:49 Commencing optimization for 500 epochs, with 40700 positive edges
22:33:54 Optimization finished
[1] "27 0.07"22:33:54 UMAP embedding parameters a = 1.68 b = 0.8631
22:33:54 Read 1203 rows and found 38 numeric columns
22:33:54 Using Annoy for neighbor search, n_neighbors = 27
22:33:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:33:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760df5274
22:33:55 Searching Annoy index using 1 thread, search_k = 2700
22:33:55 Annoy recall = 100%
22:33:57 Commencing smooth kNN distance calibration using 1 thread
22:34:01 Initializing from normalized Laplacian + noise
22:34:01 Commencing optimization for 500 epochs, with 40700 positive edges
22:34:06 Optimization finished
[1] "27 0.08"22:34:06 UMAP embedding parameters a = 1.645 b = 0.8737
22:34:06 Read 1203 rows and found 38 numeric columns
22:34:06 Using Annoy for neighbor search, n_neighbors = 27
22:34:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:34:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87720fcfb2
22:34:06 Searching Annoy index using 1 thread, search_k = 2700
22:34:06 Annoy recall = 100%
22:34:08 Commencing smooth kNN distance calibration using 1 thread
22:34:12 Initializing from normalized Laplacian + noise
22:34:12 Commencing optimization for 500 epochs, with 40700 positive edges
22:34:17 Optimization finished
[1] "27 0.09"22:34:17 UMAP embedding parameters a = 1.611 b = 0.8844
22:34:17 Read 1203 rows and found 38 numeric columns
22:34:17 Using Annoy for neighbor search, n_neighbors = 27
22:34:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:34:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87294dc352
22:34:18 Searching Annoy index using 1 thread, search_k = 2700
22:34:18 Annoy recall = 100%
22:34:20 Commencing smooth kNN distance calibration using 1 thread
22:34:24 Initializing from normalized Laplacian + noise
22:34:24 Commencing optimization for 500 epochs, with 40700 positive edges
22:34:29 Optimization finished
[1] "27 0.1"22:34:29 UMAP embedding parameters a = 1.577 b = 0.8951
22:34:29 Read 1203 rows and found 38 numeric columns
22:34:29 Using Annoy for neighbor search, n_neighbors = 27
22:34:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:34:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87779264ad
22:34:29 Searching Annoy index using 1 thread, search_k = 2700
22:34:29 Annoy recall = 100%
22:34:32 Commencing smooth kNN distance calibration using 1 thread
22:34:36 Initializing from normalized Laplacian + noise
22:34:36 Commencing optimization for 500 epochs, with 40700 positive edges
22:34:40 Optimization finished
[1] "27 0.11"22:34:40 UMAP embedding parameters a = 1.544 b = 0.9058
22:34:40 Read 1203 rows and found 38 numeric columns
22:34:40 Using Annoy for neighbor search, n_neighbors = 27
22:34:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:34:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877da5717c
22:34:41 Searching Annoy index using 1 thread, search_k = 2700
22:34:41 Annoy recall = 100%
22:34:43 Commencing smooth kNN distance calibration using 1 thread
22:34:47 Initializing from normalized Laplacian + noise
22:34:47 Commencing optimization for 500 epochs, with 40700 positive edges
22:34:52 Optimization finished
[1] "27 0.12"22:34:52 UMAP embedding parameters a = 1.51 b = 0.9165
22:34:52 Read 1203 rows and found 38 numeric columns
22:34:52 Using Annoy for neighbor search, n_neighbors = 27
22:34:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:34:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716a4fe7e
22:34:52 Searching Annoy index using 1 thread, search_k = 2700
22:34:52 Annoy recall = 100%
22:34:55 Commencing smooth kNN distance calibration using 1 thread
22:34:59 Initializing from normalized Laplacian + noise
22:34:59 Commencing optimization for 500 epochs, with 40700 positive edges
22:35:03 Optimization finished
[1] "27 0.13"22:35:04 UMAP embedding parameters a = 1.478 b = 0.9272
22:35:04 Read 1203 rows and found 38 numeric columns
22:35:04 Using Annoy for neighbor search, n_neighbors = 27
22:35:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:35:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87290494e0
22:35:04 Searching Annoy index using 1 thread, search_k = 2700
22:35:04 Annoy recall = 100%
22:35:06 Commencing smooth kNN distance calibration using 1 thread
22:35:10 Initializing from normalized Laplacian + noise
22:35:10 Commencing optimization for 500 epochs, with 40700 positive edges
22:35:15 Optimization finished
[1] "27 0.14"22:35:15 UMAP embedding parameters a = 1.446 b = 0.938
22:35:15 Read 1203 rows and found 38 numeric columns
22:35:15 Using Annoy for neighbor search, n_neighbors = 27
22:35:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:35:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bcad16e
22:35:15 Searching Annoy index using 1 thread, search_k = 2700
22:35:16 Annoy recall = 100%
22:35:18 Commencing smooth kNN distance calibration using 1 thread
22:35:22 Initializing from normalized Laplacian + noise
22:35:22 Commencing optimization for 500 epochs, with 40700 positive edges
22:35:27 Optimization finished
[1] "27 0.15"22:35:27 UMAP embedding parameters a = 1.414 b = 0.9488
22:35:27 Read 1203 rows and found 38 numeric columns
22:35:27 Using Annoy for neighbor search, n_neighbors = 27
22:35:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:35:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87459fa5c4
22:35:27 Searching Annoy index using 1 thread, search_k = 2700
22:35:27 Annoy recall = 100%
22:35:29 Commencing smooth kNN distance calibration using 1 thread
22:35:34 Initializing from normalized Laplacian + noise
22:35:34 Commencing optimization for 500 epochs, with 40700 positive edges
22:35:38 Optimization finished
[1] "27 0.16"22:35:38 UMAP embedding parameters a = 1.383 b = 0.9596
22:35:38 Read 1203 rows and found 38 numeric columns
22:35:38 Using Annoy for neighbor search, n_neighbors = 27
22:35:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:35:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ca58558
22:35:39 Searching Annoy index using 1 thread, search_k = 2700
22:35:39 Annoy recall = 100%
22:35:41 Commencing smooth kNN distance calibration using 1 thread
22:35:45 Initializing from normalized Laplacian + noise
22:35:45 Commencing optimization for 500 epochs, with 40700 positive edges
22:35:50 Optimization finished
[1] "27 0.17"22:35:50 UMAP embedding parameters a = 1.352 b = 0.9704
22:35:50 Read 1203 rows and found 38 numeric columns
22:35:50 Using Annoy for neighbor search, n_neighbors = 27
22:35:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:35:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ad39412
22:35:50 Searching Annoy index using 1 thread, search_k = 2700
22:35:51 Annoy recall = 100%
22:35:53 Commencing smooth kNN distance calibration using 1 thread
22:35:57 Initializing from normalized Laplacian + noise
22:35:57 Commencing optimization for 500 epochs, with 40700 positive edges
22:36:01 Optimization finished
[1] "27 0.18"22:36:02 UMAP embedding parameters a = 1.321 b = 0.9813
22:36:02 Read 1203 rows and found 38 numeric columns
22:36:02 Using Annoy for neighbor search, n_neighbors = 27
22:36:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:36:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724b64fc0
22:36:02 Searching Annoy index using 1 thread, search_k = 2700
22:36:02 Annoy recall = 100%
22:36:04 Commencing smooth kNN distance calibration using 1 thread
22:36:08 Initializing from normalized Laplacian + noise
22:36:08 Commencing optimization for 500 epochs, with 40700 positive edges
22:36:13 Optimization finished
[1] "27 0.19"22:36:13 UMAP embedding parameters a = 1.292 b = 0.9921
22:36:13 Read 1203 rows and found 38 numeric columns
22:36:13 Using Annoy for neighbor search, n_neighbors = 27
22:36:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:36:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734c003ab
22:36:13 Searching Annoy index using 1 thread, search_k = 2700
22:36:14 Annoy recall = 100%
22:36:16 Commencing smooth kNN distance calibration using 1 thread
22:36:20 Initializing from normalized Laplacian + noise
22:36:20 Commencing optimization for 500 epochs, with 40700 positive edges
22:36:24 Optimization finished
[1] "27 0.2"22:36:25 UMAP embedding parameters a = 1.262 b = 1.003
22:36:25 Read 1203 rows and found 38 numeric columns
22:36:25 Using Annoy for neighbor search, n_neighbors = 27
22:36:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:36:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752b67c30
22:36:25 Searching Annoy index using 1 thread, search_k = 2700
22:36:25 Annoy recall = 100%
22:36:27 Commencing smooth kNN distance calibration using 1 thread
22:36:31 Initializing from normalized Laplacian + noise
22:36:31 Commencing optimization for 500 epochs, with 40700 positive edges
22:36:36 Optimization finished
[1] "28 0"22:36:36 UMAP embedding parameters a = 1.933 b = 0.7905
22:36:36 Read 1203 rows and found 38 numeric columns
22:36:36 Using Annoy for neighbor search, n_neighbors = 28
22:36:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:36:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872937c964
22:36:36 Searching Annoy index using 1 thread, search_k = 2800
22:36:37 Annoy recall = 100%
22:36:39 Commencing smooth kNN distance calibration using 1 thread
22:36:43 Initializing from normalized Laplacian + noise
22:36:43 Commencing optimization for 500 epochs, with 42138 positive edges
22:36:47 Optimization finished
[1] "28 0.01"22:36:47 UMAP embedding parameters a = 1.896 b = 0.8006
22:36:47 Read 1203 rows and found 38 numeric columns
22:36:47 Using Annoy for neighbor search, n_neighbors = 28
22:36:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:36:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717feb021
22:36:48 Searching Annoy index using 1 thread, search_k = 2800
22:36:48 Annoy recall = 100%
22:36:50 Commencing smooth kNN distance calibration using 1 thread
22:36:54 Initializing from normalized Laplacian + noise
22:36:54 Commencing optimization for 500 epochs, with 42138 positive edges
22:36:59 Optimization finished
[1] "28 0.02"22:36:59 UMAP embedding parameters a = 1.859 b = 0.8109
22:36:59 Read 1203 rows and found 38 numeric columns
22:36:59 Using Annoy for neighbor search, n_neighbors = 28
22:36:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:36:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bd1a75a
22:36:59 Searching Annoy index using 1 thread, search_k = 2800
22:37:00 Annoy recall = 100%
22:37:02 Commencing smooth kNN distance calibration using 1 thread
22:37:06 Initializing from normalized Laplacian + noise
22:37:06 Commencing optimization for 500 epochs, with 42138 positive edges
22:37:10 Optimization finished
[1] "28 0.03"22:37:10 UMAP embedding parameters a = 1.822 b = 0.8212
22:37:10 Read 1203 rows and found 38 numeric columns
22:37:10 Using Annoy for neighbor search, n_neighbors = 28
22:37:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:37:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766f28b1
22:37:11 Searching Annoy index using 1 thread, search_k = 2800
22:37:11 Annoy recall = 100%
22:37:13 Commencing smooth kNN distance calibration using 1 thread
22:37:17 Initializing from normalized Laplacian + noise
22:37:17 Commencing optimization for 500 epochs, with 42138 positive edges
22:37:22 Optimization finished
[1] "28 0.04"22:37:22 UMAP embedding parameters a = 1.786 b = 0.8316
22:37:22 Read 1203 rows and found 38 numeric columns
22:37:22 Using Annoy for neighbor search, n_neighbors = 28
22:37:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:37:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e0b247a
22:37:22 Searching Annoy index using 1 thread, search_k = 2800
22:37:23 Annoy recall = 100%
22:37:25 Commencing smooth kNN distance calibration using 1 thread
22:37:29 Initializing from normalized Laplacian + noise
22:37:29 Commencing optimization for 500 epochs, with 42138 positive edges
22:37:33 Optimization finished
[1] "28 0.05"22:37:34 UMAP embedding parameters a = 1.75 b = 0.8421
22:37:34 Read 1203 rows and found 38 numeric columns
22:37:34 Using Annoy for neighbor search, n_neighbors = 28
22:37:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:37:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747c1e5e0
22:37:34 Searching Annoy index using 1 thread, search_k = 2800
22:37:34 Annoy recall = 100%
22:37:36 Commencing smooth kNN distance calibration using 1 thread
22:37:40 Initializing from normalized Laplacian + noise
22:37:40 Commencing optimization for 500 epochs, with 42138 positive edges
22:37:45 Optimization finished
[1] "28 0.06"22:37:45 UMAP embedding parameters a = 1.715 b = 0.8526
22:37:45 Read 1203 rows and found 38 numeric columns
22:37:45 Using Annoy for neighbor search, n_neighbors = 28
22:37:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:37:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a2dbdc
22:37:45 Searching Annoy index using 1 thread, search_k = 2800
22:37:46 Annoy recall = 100%
22:37:48 Commencing smooth kNN distance calibration using 1 thread
22:37:52 Initializing from normalized Laplacian + noise
22:37:52 Commencing optimization for 500 epochs, with 42138 positive edges
22:37:56 Optimization finished
[1] "28 0.07"22:37:57 UMAP embedding parameters a = 1.68 b = 0.8631
22:37:57 Read 1203 rows and found 38 numeric columns
22:37:57 Using Annoy for neighbor search, n_neighbors = 28
22:37:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:37:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727508ce7
22:37:57 Searching Annoy index using 1 thread, search_k = 2800
22:37:57 Annoy recall = 100%
22:37:59 Commencing smooth kNN distance calibration using 1 thread
22:38:03 Initializing from normalized Laplacian + noise
22:38:03 Commencing optimization for 500 epochs, with 42138 positive edges
22:38:08 Optimization finished
[1] "28 0.08"22:38:08 UMAP embedding parameters a = 1.645 b = 0.8737
22:38:08 Read 1203 rows and found 38 numeric columns
22:38:08 Using Annoy for neighbor search, n_neighbors = 28
22:38:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:38:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c20af91
22:38:09 Searching Annoy index using 1 thread, search_k = 2800
22:38:09 Annoy recall = 100%
22:38:11 Commencing smooth kNN distance calibration using 1 thread
22:38:15 Initializing from normalized Laplacian + noise
22:38:15 Commencing optimization for 500 epochs, with 42138 positive edges
22:38:20 Optimization finished
[1] "28 0.09"22:38:20 UMAP embedding parameters a = 1.611 b = 0.8844
22:38:20 Read 1203 rows and found 38 numeric columns
22:38:20 Using Annoy for neighbor search, n_neighbors = 28
22:38:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:38:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765d9445a
22:38:20 Searching Annoy index using 1 thread, search_k = 2800
22:38:21 Annoy recall = 100%
22:38:23 Commencing smooth kNN distance calibration using 1 thread
22:38:27 Initializing from normalized Laplacian + noise
22:38:27 Commencing optimization for 500 epochs, with 42138 positive edges
22:38:31 Optimization finished
[1] "28 0.1"22:38:32 UMAP embedding parameters a = 1.577 b = 0.8951
22:38:32 Read 1203 rows and found 38 numeric columns
22:38:32 Using Annoy for neighbor search, n_neighbors = 28
22:38:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:38:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87749ca95
22:38:32 Searching Annoy index using 1 thread, search_k = 2800
22:38:32 Annoy recall = 100%
22:38:34 Commencing smooth kNN distance calibration using 1 thread
22:38:38 Initializing from normalized Laplacian + noise
22:38:38 Commencing optimization for 500 epochs, with 42138 positive edges
22:38:43 Optimization finished
[1] "28 0.11"22:38:43 UMAP embedding parameters a = 1.544 b = 0.9058
22:38:43 Read 1203 rows and found 38 numeric columns
22:38:43 Using Annoy for neighbor search, n_neighbors = 28
22:38:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:38:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87292273dc
22:38:43 Searching Annoy index using 1 thread, search_k = 2800
22:38:44 Annoy recall = 100%
22:38:46 Commencing smooth kNN distance calibration using 1 thread
22:38:50 Initializing from normalized Laplacian + noise
22:38:50 Commencing optimization for 500 epochs, with 42138 positive edges
22:38:55 Optimization finished
[1] "28 0.12"22:38:55 UMAP embedding parameters a = 1.51 b = 0.9165
22:38:55 Read 1203 rows and found 38 numeric columns
22:38:55 Using Annoy for neighbor search, n_neighbors = 28
22:38:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:38:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877833f635
22:38:55 Searching Annoy index using 1 thread, search_k = 2800
22:38:55 Annoy recall = 100%
22:38:57 Commencing smooth kNN distance calibration using 1 thread
22:39:02 Initializing from normalized Laplacian + noise
22:39:02 Commencing optimization for 500 epochs, with 42138 positive edges
22:39:06 Optimization finished
[1] "28 0.13"22:39:06 UMAP embedding parameters a = 1.478 b = 0.9272
22:39:06 Read 1203 rows and found 38 numeric columns
22:39:06 Using Annoy for neighbor search, n_neighbors = 28
22:39:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:39:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749366e40
22:39:07 Searching Annoy index using 1 thread, search_k = 2800
22:39:07 Annoy recall = 100%
22:39:09 Commencing smooth kNN distance calibration using 1 thread
22:39:13 Initializing from normalized Laplacian + noise
22:39:13 Commencing optimization for 500 epochs, with 42138 positive edges
22:39:18 Optimization finished
[1] "28 0.14"22:39:18 UMAP embedding parameters a = 1.446 b = 0.938
22:39:18 Read 1203 rows and found 38 numeric columns
22:39:18 Using Annoy for neighbor search, n_neighbors = 28
22:39:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:39:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87551e4196
22:39:18 Searching Annoy index using 1 thread, search_k = 2800
22:39:19 Annoy recall = 100%
22:39:21 Commencing smooth kNN distance calibration using 1 thread
22:39:25 Initializing from normalized Laplacian + noise
22:39:25 Commencing optimization for 500 epochs, with 42138 positive edges
22:39:30 Optimization finished
[1] "28 0.15"22:39:30 UMAP embedding parameters a = 1.414 b = 0.9488
22:39:30 Read 1203 rows and found 38 numeric columns
22:39:30 Using Annoy for neighbor search, n_neighbors = 28
22:39:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:39:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748d246fe
22:39:30 Searching Annoy index using 1 thread, search_k = 2800
22:39:30 Annoy recall = 100%
22:39:33 Commencing smooth kNN distance calibration using 1 thread
22:39:37 Initializing from normalized Laplacian + noise
22:39:37 Commencing optimization for 500 epochs, with 42138 positive edges
22:39:41 Optimization finished
[1] "28 0.16"22:39:42 UMAP embedding parameters a = 1.383 b = 0.9596
22:39:42 Read 1203 rows and found 38 numeric columns
22:39:42 Using Annoy for neighbor search, n_neighbors = 28
22:39:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:39:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721fe4650
22:39:42 Searching Annoy index using 1 thread, search_k = 2800
22:39:42 Annoy recall = 100%
22:39:44 Commencing smooth kNN distance calibration using 1 thread
22:39:48 Initializing from normalized Laplacian + noise
22:39:48 Commencing optimization for 500 epochs, with 42138 positive edges
22:39:53 Optimization finished
[1] "28 0.17"22:39:53 UMAP embedding parameters a = 1.352 b = 0.9704
22:39:53 Read 1203 rows and found 38 numeric columns
22:39:53 Using Annoy for neighbor search, n_neighbors = 28
22:39:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:39:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735fd940a
22:39:53 Searching Annoy index using 1 thread, search_k = 2800
22:39:54 Annoy recall = 100%
22:39:56 Commencing smooth kNN distance calibration using 1 thread
22:40:00 Initializing from normalized Laplacian + noise
22:40:00 Commencing optimization for 500 epochs, with 42138 positive edges
22:40:05 Optimization finished
[1] "28 0.18"22:40:05 UMAP embedding parameters a = 1.321 b = 0.9813
22:40:05 Read 1203 rows and found 38 numeric columns
22:40:05 Using Annoy for neighbor search, n_neighbors = 28
22:40:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:40:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ae216b0
22:40:05 Searching Annoy index using 1 thread, search_k = 2800
22:40:05 Annoy recall = 100%
22:40:08 Commencing smooth kNN distance calibration using 1 thread
22:40:12 Initializing from normalized Laplacian + noise
22:40:12 Commencing optimization for 500 epochs, with 42138 positive edges
22:40:16 Optimization finished
[1] "28 0.19"22:40:17 UMAP embedding parameters a = 1.292 b = 0.9921
22:40:17 Read 1203 rows and found 38 numeric columns
22:40:17 Using Annoy for neighbor search, n_neighbors = 28
22:40:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:40:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b4c09a2
22:40:17 Searching Annoy index using 1 thread, search_k = 2800
22:40:17 Annoy recall = 100%
22:40:19 Commencing smooth kNN distance calibration using 1 thread
22:40:23 Initializing from normalized Laplacian + noise
22:40:24 Commencing optimization for 500 epochs, with 42138 positive edges
22:40:28 Optimization finished
[1] "28 0.2"22:40:28 UMAP embedding parameters a = 1.262 b = 1.003
22:40:28 Read 1203 rows and found 38 numeric columns
22:40:28 Using Annoy for neighbor search, n_neighbors = 28
22:40:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:40:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d8ff8b8
22:40:29 Searching Annoy index using 1 thread, search_k = 2800
22:40:29 Annoy recall = 100%
22:40:31 Commencing smooth kNN distance calibration using 1 thread
22:40:35 Initializing from normalized Laplacian + noise
22:40:35 Commencing optimization for 500 epochs, with 42138 positive edges
22:40:40 Optimization finished
[1] "29 0"22:40:40 UMAP embedding parameters a = 1.933 b = 0.7905
22:40:40 Read 1203 rows and found 38 numeric columns
22:40:40 Using Annoy for neighbor search, n_neighbors = 29
22:40:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:40:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873887882c
22:40:40 Searching Annoy index using 1 thread, search_k = 2900
22:40:41 Annoy recall = 100%
22:40:43 Commencing smooth kNN distance calibration using 1 thread
22:40:47 Initializing from normalized Laplacian + noise
22:40:47 Commencing optimization for 500 epochs, with 43618 positive edges
22:40:52 Optimization finished
[1] "29 0.01"22:40:52 UMAP embedding parameters a = 1.896 b = 0.8006
22:40:52 Read 1203 rows and found 38 numeric columns
22:40:52 Using Annoy for neighbor search, n_neighbors = 29
22:40:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:40:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761f10821
22:40:52 Searching Annoy index using 1 thread, search_k = 2900
22:40:52 Annoy recall = 100%
22:40:55 Commencing smooth kNN distance calibration using 1 thread
22:40:59 Initializing from normalized Laplacian + noise
22:40:59 Commencing optimization for 500 epochs, with 43618 positive edges
22:41:04 Optimization finished
[1] "29 0.02"22:41:04 UMAP embedding parameters a = 1.859 b = 0.8109
22:41:04 Read 1203 rows and found 38 numeric columns
22:41:04 Using Annoy for neighbor search, n_neighbors = 29
22:41:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:41:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756948d98
22:41:04 Searching Annoy index using 1 thread, search_k = 2900
22:41:04 Annoy recall = 100%
22:41:06 Commencing smooth kNN distance calibration using 1 thread
22:41:11 Initializing from normalized Laplacian + noise
22:41:11 Commencing optimization for 500 epochs, with 43618 positive edges
22:41:15 Optimization finished
[1] "29 0.03"22:41:16 UMAP embedding parameters a = 1.822 b = 0.8212
22:41:16 Read 1203 rows and found 38 numeric columns
22:41:16 Using Annoy for neighbor search, n_neighbors = 29
22:41:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:41:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87452599a
22:41:16 Searching Annoy index using 1 thread, search_k = 2900
22:41:16 Annoy recall = 100%
22:41:18 Commencing smooth kNN distance calibration using 1 thread
22:41:22 Initializing from normalized Laplacian + noise
22:41:22 Commencing optimization for 500 epochs, with 43618 positive edges
22:41:27 Optimization finished
[1] "29 0.04"22:41:27 UMAP embedding parameters a = 1.786 b = 0.8316
22:41:27 Read 1203 rows and found 38 numeric columns
22:41:27 Using Annoy for neighbor search, n_neighbors = 29
22:41:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:41:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872790ade5
22:41:28 Searching Annoy index using 1 thread, search_k = 2900
22:41:28 Annoy recall = 100%
22:41:30 Commencing smooth kNN distance calibration using 1 thread
22:41:34 Initializing from normalized Laplacian + noise
22:41:34 Commencing optimization for 500 epochs, with 43618 positive edges
22:41:39 Optimization finished
[1] "29 0.05"22:41:39 UMAP embedding parameters a = 1.75 b = 0.8421
22:41:39 Read 1203 rows and found 38 numeric columns
22:41:39 Using Annoy for neighbor search, n_neighbors = 29
22:41:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:41:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87633a12f0
22:41:39 Searching Annoy index using 1 thread, search_k = 2900
22:41:40 Annoy recall = 100%
22:41:42 Commencing smooth kNN distance calibration using 1 thread
22:41:46 Initializing from normalized Laplacian + noise
22:41:46 Commencing optimization for 500 epochs, with 43618 positive edges
22:41:51 Optimization finished
[1] "29 0.06"22:41:51 UMAP embedding parameters a = 1.715 b = 0.8526
22:41:51 Read 1203 rows and found 38 numeric columns
22:41:51 Using Annoy for neighbor search, n_neighbors = 29
22:41:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:41:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f25edac
22:41:51 Searching Annoy index using 1 thread, search_k = 2900
22:41:52 Annoy recall = 100%
22:41:54 Commencing smooth kNN distance calibration using 1 thread
22:41:58 Initializing from normalized Laplacian + noise
22:41:58 Commencing optimization for 500 epochs, with 43618 positive edges
22:42:03 Optimization finished
[1] "29 0.07"22:42:03 UMAP embedding parameters a = 1.68 b = 0.8631
22:42:03 Read 1203 rows and found 38 numeric columns
22:42:03 Using Annoy for neighbor search, n_neighbors = 29
22:42:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:42:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c46fda6
22:42:03 Searching Annoy index using 1 thread, search_k = 2900
22:42:03 Annoy recall = 100%
22:42:05 Commencing smooth kNN distance calibration using 1 thread
22:42:10 Initializing from normalized Laplacian + noise
22:42:10 Commencing optimization for 500 epochs, with 43618 positive edges
22:42:14 Optimization finished
[1] "29 0.08"22:42:15 UMAP embedding parameters a = 1.645 b = 0.8737
22:42:15 Read 1203 rows and found 38 numeric columns
22:42:15 Using Annoy for neighbor search, n_neighbors = 29
22:42:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:42:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717fa169b
22:42:15 Searching Annoy index using 1 thread, search_k = 2900
22:42:15 Annoy recall = 100%
22:42:17 Commencing smooth kNN distance calibration using 1 thread
22:42:22 Initializing from normalized Laplacian + noise
22:42:22 Commencing optimization for 500 epochs, with 43618 positive edges
22:42:26 Optimization finished
[1] "29 0.09"22:42:27 UMAP embedding parameters a = 1.611 b = 0.8844
22:42:27 Read 1203 rows and found 38 numeric columns
22:42:27 Using Annoy for neighbor search, n_neighbors = 29
22:42:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:42:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721dc69dc
22:42:27 Searching Annoy index using 1 thread, search_k = 2900
22:42:27 Annoy recall = 100%
22:42:29 Commencing smooth kNN distance calibration using 1 thread
22:42:34 Initializing from normalized Laplacian + noise
22:42:34 Commencing optimization for 500 epochs, with 43618 positive edges
22:42:38 Optimization finished
[1] "29 0.1"22:42:39 UMAP embedding parameters a = 1.577 b = 0.8951
22:42:39 Read 1203 rows and found 38 numeric columns
22:42:39 Using Annoy for neighbor search, n_neighbors = 29
22:42:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:42:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87757ec70a
22:42:39 Searching Annoy index using 1 thread, search_k = 2900
22:42:39 Annoy recall = 100%
22:42:41 Commencing smooth kNN distance calibration using 1 thread
22:42:45 Initializing from normalized Laplacian + noise
22:42:45 Commencing optimization for 500 epochs, with 43618 positive edges
22:42:50 Optimization finished
[1] "29 0.11"22:42:50 UMAP embedding parameters a = 1.544 b = 0.9058
22:42:50 Read 1203 rows and found 38 numeric columns
22:42:50 Using Annoy for neighbor search, n_neighbors = 29
22:42:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:42:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ff8c6bc
22:42:51 Searching Annoy index using 1 thread, search_k = 2900
22:42:51 Annoy recall = 100%
22:42:53 Commencing smooth kNN distance calibration using 1 thread
22:42:57 Initializing from normalized Laplacian + noise
22:42:57 Commencing optimization for 500 epochs, with 43618 positive edges
22:43:02 Optimization finished
[1] "29 0.12"22:43:02 UMAP embedding parameters a = 1.51 b = 0.9165
22:43:02 Read 1203 rows and found 38 numeric columns
22:43:02 Using Annoy for neighbor search, n_neighbors = 29
22:43:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:43:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875dae1136
22:43:03 Searching Annoy index using 1 thread, search_k = 2900
22:43:03 Annoy recall = 100%
22:43:05 Commencing smooth kNN distance calibration using 1 thread
22:43:09 Initializing from normalized Laplacian + noise
22:43:09 Commencing optimization for 500 epochs, with 43618 positive edges
22:43:14 Optimization finished
[1] "29 0.13"22:43:14 UMAP embedding parameters a = 1.478 b = 0.9272
22:43:14 Read 1203 rows and found 38 numeric columns
22:43:14 Using Annoy for neighbor search, n_neighbors = 29
22:43:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:43:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bedefbc
22:43:15 Searching Annoy index using 1 thread, search_k = 2900
22:43:15 Annoy recall = 100%
22:43:17 Commencing smooth kNN distance calibration using 1 thread
22:43:21 Initializing from normalized Laplacian + noise
22:43:21 Commencing optimization for 500 epochs, with 43618 positive edges
22:43:26 Optimization finished
[1] "29 0.14"22:43:26 UMAP embedding parameters a = 1.446 b = 0.938
22:43:26 Read 1203 rows and found 38 numeric columns
22:43:26 Using Annoy for neighbor search, n_neighbors = 29
22:43:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:43:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e03eb36
22:43:27 Searching Annoy index using 1 thread, search_k = 2900
22:43:27 Annoy recall = 100%
22:43:29 Commencing smooth kNN distance calibration using 1 thread
22:43:33 Initializing from normalized Laplacian + noise
22:43:33 Commencing optimization for 500 epochs, with 43618 positive edges
22:43:38 Optimization finished
[1] "29 0.15"22:43:38 UMAP embedding parameters a = 1.414 b = 0.9488
22:43:38 Read 1203 rows and found 38 numeric columns
22:43:38 Using Annoy for neighbor search, n_neighbors = 29
22:43:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:43:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87256ff716
22:43:38 Searching Annoy index using 1 thread, search_k = 2900
22:43:39 Annoy recall = 100%
22:43:41 Commencing smooth kNN distance calibration using 1 thread
22:43:45 Initializing from normalized Laplacian + noise
22:43:45 Commencing optimization for 500 epochs, with 43618 positive edges
22:43:50 Optimization finished
[1] "29 0.16"22:43:50 UMAP embedding parameters a = 1.383 b = 0.9596
22:43:50 Read 1203 rows and found 38 numeric columns
22:43:50 Using Annoy for neighbor search, n_neighbors = 29
22:43:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:43:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87190cb98
22:43:51 Searching Annoy index using 1 thread, search_k = 2900
22:43:51 Annoy recall = 100%
22:43:53 Commencing smooth kNN distance calibration using 1 thread
22:43:57 Initializing from normalized Laplacian + noise
22:43:57 Commencing optimization for 500 epochs, with 43618 positive edges
22:44:02 Optimization finished
[1] "29 0.17"22:44:02 UMAP embedding parameters a = 1.352 b = 0.9704
22:44:02 Read 1203 rows and found 38 numeric columns
22:44:02 Using Annoy for neighbor search, n_neighbors = 29
22:44:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:44:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87554781e
22:44:02 Searching Annoy index using 1 thread, search_k = 2900
22:44:03 Annoy recall = 100%
22:44:05 Commencing smooth kNN distance calibration using 1 thread
22:44:09 Initializing from normalized Laplacian + noise
22:44:09 Commencing optimization for 500 epochs, with 43618 positive edges
22:44:14 Optimization finished
[1] "29 0.18"22:44:14 UMAP embedding parameters a = 1.321 b = 0.9813
22:44:14 Read 1203 rows and found 38 numeric columns
22:44:14 Using Annoy for neighbor search, n_neighbors = 29
22:44:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:44:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87190a6a7
22:44:14 Searching Annoy index using 1 thread, search_k = 2900
22:44:15 Annoy recall = 100%
22:44:17 Commencing smooth kNN distance calibration using 1 thread
22:44:21 Initializing from normalized Laplacian + noise
22:44:21 Commencing optimization for 500 epochs, with 43618 positive edges
22:44:26 Optimization finished
[1] "29 0.19"22:44:26 UMAP embedding parameters a = 1.292 b = 0.9921
22:44:26 Read 1203 rows and found 38 numeric columns
22:44:26 Using Annoy for neighbor search, n_neighbors = 29
22:44:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:44:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87676a0ff3
22:44:26 Searching Annoy index using 1 thread, search_k = 2900
22:44:27 Annoy recall = 100%
22:44:29 Commencing smooth kNN distance calibration using 1 thread
22:44:33 Initializing from normalized Laplacian + noise
22:44:33 Commencing optimization for 500 epochs, with 43618 positive edges
22:44:38 Optimization finished
[1] "29 0.2"22:44:38 UMAP embedding parameters a = 1.262 b = 1.003
22:44:38 Read 1203 rows and found 38 numeric columns
22:44:38 Using Annoy for neighbor search, n_neighbors = 29
22:44:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:44:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c9e42b3
22:44:38 Searching Annoy index using 1 thread, search_k = 2900
22:44:39 Annoy recall = 100%
22:44:41 Commencing smooth kNN distance calibration using 1 thread
22:44:45 Initializing from normalized Laplacian + noise
22:44:45 Commencing optimization for 500 epochs, with 43618 positive edges
22:44:50 Optimization finished
[1] "30 0"22:44:50 UMAP embedding parameters a = 1.933 b = 0.7905
22:44:50 Read 1203 rows and found 38 numeric columns
22:44:50 Using Annoy for neighbor search, n_neighbors = 30
22:44:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:44:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ab31a83
22:44:51 Searching Annoy index using 1 thread, search_k = 3000
22:44:51 Annoy recall = 100%
22:44:53 Commencing smooth kNN distance calibration using 1 thread
22:44:58 Initializing from normalized Laplacian + noise
22:44:58 Commencing optimization for 500 epochs, with 45072 positive edges
22:45:03 Optimization finished
[1] "30 0.01"22:45:03 UMAP embedding parameters a = 1.896 b = 0.8006
22:45:03 Read 1203 rows and found 38 numeric columns
22:45:03 Using Annoy for neighbor search, n_neighbors = 30
22:45:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:45:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f9e0628
22:45:03 Searching Annoy index using 1 thread, search_k = 3000
22:45:03 Annoy recall = 100%
22:45:06 Commencing smooth kNN distance calibration using 1 thread
22:45:11 Initializing from normalized Laplacian + noise
22:45:11 Commencing optimization for 500 epochs, with 45072 positive edges
22:45:16 Optimization finished
[1] "30 0.02"22:45:17 UMAP embedding parameters a = 1.859 b = 0.8109
22:45:17 Read 1203 rows and found 38 numeric columns
22:45:17 Using Annoy for neighbor search, n_neighbors = 30
22:45:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:45:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755d4b0f4
22:45:17 Searching Annoy index using 1 thread, search_k = 3000
22:45:17 Annoy recall = 100%
22:45:20 Commencing smooth kNN distance calibration using 1 thread
22:45:25 Initializing from normalized Laplacian + noise
22:45:25 Commencing optimization for 500 epochs, with 45072 positive edges
22:45:30 Optimization finished
[1] "30 0.03"22:45:30 UMAP embedding parameters a = 1.822 b = 0.8212
22:45:30 Read 1203 rows and found 38 numeric columns
22:45:30 Using Annoy for neighbor search, n_neighbors = 30
22:45:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:45:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fd15c1a
22:45:30 Searching Annoy index using 1 thread, search_k = 3000
22:45:31 Annoy recall = 100%
22:45:33 Commencing smooth kNN distance calibration using 1 thread
22:45:38 Initializing from normalized Laplacian + noise
22:45:38 Commencing optimization for 500 epochs, with 45072 positive edges
22:45:43 Optimization finished
[1] "30 0.04"22:45:43 UMAP embedding parameters a = 1.786 b = 0.8316
22:45:43 Read 1203 rows and found 38 numeric columns
22:45:43 Using Annoy for neighbor search, n_neighbors = 30
22:45:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:45:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728704d26
22:45:43 Searching Annoy index using 1 thread, search_k = 3000
22:45:44 Annoy recall = 100%
22:45:46 Commencing smooth kNN distance calibration using 1 thread
22:45:51 Initializing from normalized Laplacian + noise
22:45:51 Commencing optimization for 500 epochs, with 45072 positive edges
22:45:56 Optimization finished
[1] "30 0.05"22:45:56 UMAP embedding parameters a = 1.75 b = 0.8421
22:45:56 Read 1203 rows and found 38 numeric columns
22:45:56 Using Annoy for neighbor search, n_neighbors = 30
22:45:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:45:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777d2f744
22:45:57 Searching Annoy index using 1 thread, search_k = 3000
22:45:57 Annoy recall = 100%
22:46:00 Commencing smooth kNN distance calibration using 1 thread
22:46:04 Initializing from normalized Laplacian + noise
22:46:04 Commencing optimization for 500 epochs, with 45072 positive edges
22:46:10 Optimization finished
[1] "30 0.06"22:46:10 UMAP embedding parameters a = 1.715 b = 0.8526
22:46:10 Read 1203 rows and found 38 numeric columns
22:46:10 Using Annoy for neighbor search, n_neighbors = 30
22:46:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:46:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735cef024
22:46:10 Searching Annoy index using 1 thread, search_k = 3000
22:46:10 Annoy recall = 100%
22:46:13 Commencing smooth kNN distance calibration using 1 thread
22:46:18 Initializing from normalized Laplacian + noise
22:46:18 Commencing optimization for 500 epochs, with 45072 positive edges
22:46:23 Optimization finished
[1] "30 0.07"22:46:23 UMAP embedding parameters a = 1.68 b = 0.8631
22:46:23 Read 1203 rows and found 38 numeric columns
22:46:23 Using Annoy for neighbor search, n_neighbors = 30
22:46:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:46:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87635263d6
22:46:23 Searching Annoy index using 1 thread, search_k = 3000
22:46:24 Annoy recall = 100%
22:46:26 Commencing smooth kNN distance calibration using 1 thread
22:46:32 Initializing from normalized Laplacian + noise
22:46:32 Commencing optimization for 500 epochs, with 45072 positive edges
22:46:37 Optimization finished
[1] "30 0.08"22:46:37 UMAP embedding parameters a = 1.645 b = 0.8737
22:46:37 Read 1203 rows and found 38 numeric columns
22:46:37 Using Annoy for neighbor search, n_neighbors = 30
22:46:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:46:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87431f00e7
22:46:37 Searching Annoy index using 1 thread, search_k = 3000
22:46:38 Annoy recall = 100%
22:46:40 Commencing smooth kNN distance calibration using 1 thread
22:46:45 Initializing from normalized Laplacian + noise
22:46:45 Commencing optimization for 500 epochs, with 45072 positive edges
22:46:50 Optimization finished
[1] "30 0.09"22:46:50 UMAP embedding parameters a = 1.611 b = 0.8844
22:46:50 Read 1203 rows and found 38 numeric columns
22:46:50 Using Annoy for neighbor search, n_neighbors = 30
22:46:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:46:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87635ee8dc
22:46:51 Searching Annoy index using 1 thread, search_k = 3000
22:46:51 Annoy recall = 100%
22:46:53 Commencing smooth kNN distance calibration using 1 thread
22:46:58 Initializing from normalized Laplacian + noise
22:46:58 Commencing optimization for 500 epochs, with 45072 positive edges
22:47:03 Optimization finished
[1] "30 0.1"22:47:03 UMAP embedding parameters a = 1.577 b = 0.8951
22:47:03 Read 1203 rows and found 38 numeric columns
22:47:03 Using Annoy for neighbor search, n_neighbors = 30
22:47:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:47:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871bd9ec02
22:47:03 Searching Annoy index using 1 thread, search_k = 3000
22:47:04 Annoy recall = 100%
22:47:06 Commencing smooth kNN distance calibration using 1 thread
22:47:11 Initializing from normalized Laplacian + noise
22:47:11 Commencing optimization for 500 epochs, with 45072 positive edges
22:47:17 Optimization finished
[1] "30 0.11"22:47:17 UMAP embedding parameters a = 1.544 b = 0.9058
22:47:17 Read 1203 rows and found 38 numeric columns
22:47:17 Using Annoy for neighbor search, n_neighbors = 30
22:47:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:47:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725100908
22:47:17 Searching Annoy index using 1 thread, search_k = 3000
22:47:17 Annoy recall = 100%
22:47:20 Commencing smooth kNN distance calibration using 1 thread
22:47:24 Initializing from normalized Laplacian + noise
22:47:24 Commencing optimization for 500 epochs, with 45072 positive edges
22:47:30 Optimization finished
[1] "30 0.12"22:47:30 UMAP embedding parameters a = 1.51 b = 0.9165
22:47:30 Read 1203 rows and found 38 numeric columns
22:47:30 Using Annoy for neighbor search, n_neighbors = 30
22:47:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:47:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739f37675
22:47:30 Searching Annoy index using 1 thread, search_k = 3000
22:47:30 Annoy recall = 100%
22:47:33 Commencing smooth kNN distance calibration using 1 thread
22:47:39 Initializing from normalized Laplacian + noise
22:47:39 Commencing optimization for 500 epochs, with 45072 positive edges
22:47:44 Optimization finished
[1] "30 0.13"22:47:44 UMAP embedding parameters a = 1.478 b = 0.9272
22:47:44 Read 1203 rows and found 38 numeric columns
22:47:44 Using Annoy for neighbor search, n_neighbors = 30
22:47:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:47:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87202c459c
22:47:45 Searching Annoy index using 1 thread, search_k = 3000
22:47:45 Annoy recall = 100%
22:47:47 Commencing smooth kNN distance calibration using 1 thread
22:47:52 Initializing from normalized Laplacian + noise
22:47:52 Commencing optimization for 500 epochs, with 45072 positive edges
22:47:58 Optimization finished
[1] "30 0.14"22:47:58 UMAP embedding parameters a = 1.446 b = 0.938
22:47:58 Read 1203 rows and found 38 numeric columns
22:47:58 Using Annoy for neighbor search, n_neighbors = 30
22:47:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:47:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ca0b6ed
22:47:58 Searching Annoy index using 1 thread, search_k = 3000
22:47:58 Annoy recall = 100%
22:48:01 Commencing smooth kNN distance calibration using 1 thread
22:48:07 Initializing from normalized Laplacian + noise
22:48:07 Commencing optimization for 500 epochs, with 45072 positive edges
22:48:12 Optimization finished
[1] "30 0.15"22:48:13 UMAP embedding parameters a = 1.414 b = 0.9488
22:48:13 Read 1203 rows and found 38 numeric columns
22:48:13 Using Annoy for neighbor search, n_neighbors = 30
22:48:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:48:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d2d8965
22:48:13 Searching Annoy index using 1 thread, search_k = 3000
22:48:13 Annoy recall = 100%
22:48:16 Commencing smooth kNN distance calibration using 1 thread
22:48:22 Initializing from normalized Laplacian + noise
22:48:22 Commencing optimization for 500 epochs, with 45072 positive edges
22:48:27 Optimization finished
[1] "30 0.16"22:48:28 UMAP embedding parameters a = 1.383 b = 0.9596
22:48:28 Read 1203 rows and found 38 numeric columns
22:48:28 Using Annoy for neighbor search, n_neighbors = 30
22:48:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:48:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f523348
22:48:28 Searching Annoy index using 1 thread, search_k = 3000
22:48:28 Annoy recall = 100%
22:48:31 Commencing smooth kNN distance calibration using 1 thread
22:48:37 Initializing from normalized Laplacian + noise
22:48:37 Commencing optimization for 500 epochs, with 45072 positive edges
22:48:42 Optimization finished
[1] "30 0.17"22:48:43 UMAP embedding parameters a = 1.352 b = 0.9704
22:48:43 Read 1203 rows and found 38 numeric columns
22:48:43 Using Annoy for neighbor search, n_neighbors = 30
22:48:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:48:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718e7b493
22:48:43 Searching Annoy index using 1 thread, search_k = 3000
22:48:43 Annoy recall = 100%
22:48:46 Commencing smooth kNN distance calibration using 1 thread
22:48:52 Initializing from normalized Laplacian + noise
22:48:52 Commencing optimization for 500 epochs, with 45072 positive edges
22:48:57 Optimization finished
[1] "30 0.18"22:48:57 UMAP embedding parameters a = 1.321 b = 0.9813
22:48:57 Read 1203 rows and found 38 numeric columns
22:48:57 Using Annoy for neighbor search, n_neighbors = 30
22:48:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:48:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873527a001
22:48:58 Searching Annoy index using 1 thread, search_k = 3000
22:48:58 Annoy recall = 100%
22:49:01 Commencing smooth kNN distance calibration using 1 thread
22:49:06 Initializing from normalized Laplacian + noise
22:49:06 Commencing optimization for 500 epochs, with 45072 positive edges
22:49:11 Optimization finished
[1] "30 0.19"22:49:11 UMAP embedding parameters a = 1.292 b = 0.9921
22:49:11 Read 1203 rows and found 38 numeric columns
22:49:11 Using Annoy for neighbor search, n_neighbors = 30
22:49:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:49:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87112e9d24
22:49:11 Searching Annoy index using 1 thread, search_k = 3000
22:49:12 Annoy recall = 100%
22:49:14 Commencing smooth kNN distance calibration using 1 thread
22:49:20 Initializing from normalized Laplacian + noise
22:49:20 Commencing optimization for 500 epochs, with 45072 positive edges
22:49:25 Optimization finished
[1] "30 0.2"22:49:26 UMAP embedding parameters a = 1.262 b = 1.003
22:49:26 Read 1203 rows and found 38 numeric columns
22:49:26 Using Annoy for neighbor search, n_neighbors = 30
22:49:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:49:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e667b9e
22:49:26 Searching Annoy index using 1 thread, search_k = 3000
22:49:26 Annoy recall = 100%
22:49:29 Commencing smooth kNN distance calibration using 1 thread
22:49:33 Initializing from normalized Laplacian + noise
22:49:33 Commencing optimization for 500 epochs, with 45072 positive edges
22:49:38 Optimization finished
[1] "31 0"22:49:39 UMAP embedding parameters a = 1.933 b = 0.7905
22:49:39 Read 1203 rows and found 38 numeric columns
22:49:39 Using Annoy for neighbor search, n_neighbors = 31
22:49:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:49:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87652066bd
22:49:39 Searching Annoy index using 1 thread, search_k = 3100
22:49:39 Annoy recall = 100%
22:49:41 Commencing smooth kNN distance calibration using 1 thread
22:49:46 Initializing from normalized Laplacian + noise
22:49:46 Commencing optimization for 500 epochs, with 46500 positive edges
22:49:51 Optimization finished
[1] "31 0.01"22:49:51 UMAP embedding parameters a = 1.896 b = 0.8006
22:49:51 Read 1203 rows and found 38 numeric columns
22:49:51 Using Annoy for neighbor search, n_neighbors = 31
22:49:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:49:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876edcae5a
22:49:51 Searching Annoy index using 1 thread, search_k = 3100
22:49:52 Annoy recall = 100%
22:49:54 Commencing smooth kNN distance calibration using 1 thread
22:49:58 Initializing from normalized Laplacian + noise
22:49:58 Commencing optimization for 500 epochs, with 46500 positive edges
22:50:03 Optimization finished
[1] "31 0.02"22:50:04 UMAP embedding parameters a = 1.859 b = 0.8109
22:50:04 Read 1203 rows and found 38 numeric columns
22:50:04 Using Annoy for neighbor search, n_neighbors = 31
22:50:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:50:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a546b5a
22:50:04 Searching Annoy index using 1 thread, search_k = 3100
22:50:04 Annoy recall = 100%
22:50:06 Commencing smooth kNN distance calibration using 1 thread
22:50:11 Initializing from normalized Laplacian + noise
22:50:11 Commencing optimization for 500 epochs, with 46500 positive edges
22:50:16 Optimization finished
[1] "31 0.03"22:50:16 UMAP embedding parameters a = 1.822 b = 0.8212
22:50:16 Read 1203 rows and found 38 numeric columns
22:50:16 Using Annoy for neighbor search, n_neighbors = 31
22:50:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:50:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87432451f4
22:50:16 Searching Annoy index using 1 thread, search_k = 3100
22:50:17 Annoy recall = 100%
22:50:19 Commencing smooth kNN distance calibration using 1 thread
22:50:23 Initializing from normalized Laplacian + noise
22:50:23 Commencing optimization for 500 epochs, with 46500 positive edges
22:50:28 Optimization finished
[1] "31 0.04"22:50:28 UMAP embedding parameters a = 1.786 b = 0.8316
22:50:28 Read 1203 rows and found 38 numeric columns
22:50:28 Using Annoy for neighbor search, n_neighbors = 31
22:50:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:50:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87144ca570
22:50:29 Searching Annoy index using 1 thread, search_k = 3100
22:50:29 Annoy recall = 100%
22:50:31 Commencing smooth kNN distance calibration using 1 thread
22:50:36 Initializing from normalized Laplacian + noise
22:50:36 Commencing optimization for 500 epochs, with 46500 positive edges
22:50:41 Optimization finished
[1] "31 0.05"22:50:41 UMAP embedding parameters a = 1.75 b = 0.8421
22:50:41 Read 1203 rows and found 38 numeric columns
22:50:41 Using Annoy for neighbor search, n_neighbors = 31
22:50:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:50:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87be536f2
22:50:42 Searching Annoy index using 1 thread, search_k = 3100
22:50:42 Annoy recall = 100%
22:50:44 Commencing smooth kNN distance calibration using 1 thread
22:50:49 Initializing from normalized Laplacian + noise
22:50:49 Commencing optimization for 500 epochs, with 46500 positive edges
22:50:54 Optimization finished
[1] "31 0.06"22:50:54 UMAP embedding parameters a = 1.715 b = 0.8526
22:50:54 Read 1203 rows and found 38 numeric columns
22:50:54 Using Annoy for neighbor search, n_neighbors = 31
22:50:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:50:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874878ca12
22:50:54 Searching Annoy index using 1 thread, search_k = 3100
22:50:55 Annoy recall = 100%
22:50:57 Commencing smooth kNN distance calibration using 1 thread
22:51:02 Initializing from normalized Laplacian + noise
22:51:02 Commencing optimization for 500 epochs, with 46500 positive edges
22:51:07 Optimization finished
[1] "31 0.07"22:51:07 UMAP embedding parameters a = 1.68 b = 0.8631
22:51:07 Read 1203 rows and found 38 numeric columns
22:51:07 Using Annoy for neighbor search, n_neighbors = 31
22:51:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:51:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715dd4c18
22:51:07 Searching Annoy index using 1 thread, search_k = 3100
22:51:08 Annoy recall = 100%
22:51:10 Commencing smooth kNN distance calibration using 1 thread
22:51:15 Initializing from normalized Laplacian + noise
22:51:15 Commencing optimization for 500 epochs, with 46500 positive edges
22:51:20 Optimization finished
[1] "31 0.08"22:51:20 UMAP embedding parameters a = 1.645 b = 0.8737
22:51:20 Read 1203 rows and found 38 numeric columns
22:51:20 Using Annoy for neighbor search, n_neighbors = 31
22:51:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:51:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87734f46e5
22:51:20 Searching Annoy index using 1 thread, search_k = 3100
22:51:21 Annoy recall = 100%
22:51:23 Commencing smooth kNN distance calibration using 1 thread
22:51:27 Initializing from normalized Laplacian + noise
22:51:28 Commencing optimization for 500 epochs, with 46500 positive edges
22:51:32 Optimization finished
[1] "31 0.09"22:51:33 UMAP embedding parameters a = 1.611 b = 0.8844
22:51:33 Read 1203 rows and found 38 numeric columns
22:51:33 Using Annoy for neighbor search, n_neighbors = 31
22:51:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:51:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755170cc5
22:51:33 Searching Annoy index using 1 thread, search_k = 3100
22:51:33 Annoy recall = 100%
22:51:35 Commencing smooth kNN distance calibration using 1 thread
22:51:40 Initializing from normalized Laplacian + noise
22:51:40 Commencing optimization for 500 epochs, with 46500 positive edges
22:51:45 Optimization finished
[1] "31 0.1"22:51:45 UMAP embedding parameters a = 1.577 b = 0.8951
22:51:45 Read 1203 rows and found 38 numeric columns
22:51:45 Using Annoy for neighbor search, n_neighbors = 31
22:51:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:51:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874090669b
22:51:45 Searching Annoy index using 1 thread, search_k = 3100
22:51:46 Annoy recall = 100%
22:51:48 Commencing smooth kNN distance calibration using 1 thread
22:51:52 Initializing from normalized Laplacian + noise
22:51:52 Commencing optimization for 500 epochs, with 46500 positive edges
22:51:57 Optimization finished
[1] "31 0.11"22:51:57 UMAP embedding parameters a = 1.544 b = 0.9058
22:51:57 Read 1203 rows and found 38 numeric columns
22:51:57 Using Annoy for neighbor search, n_neighbors = 31
22:51:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:51:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752ed4d0e
22:51:58 Searching Annoy index using 1 thread, search_k = 3100
22:51:58 Annoy recall = 100%
22:52:00 Commencing smooth kNN distance calibration using 1 thread
22:52:05 Initializing from normalized Laplacian + noise
22:52:05 Commencing optimization for 500 epochs, with 46500 positive edges
22:52:10 Optimization finished
[1] "31 0.12"22:52:10 UMAP embedding parameters a = 1.51 b = 0.9165
22:52:10 Read 1203 rows and found 38 numeric columns
22:52:10 Using Annoy for neighbor search, n_neighbors = 31
22:52:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:52:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872aebbdb9
22:52:10 Searching Annoy index using 1 thread, search_k = 3100
22:52:10 Annoy recall = 100%
22:52:13 Commencing smooth kNN distance calibration using 1 thread
22:52:17 Initializing from normalized Laplacian + noise
22:52:17 Commencing optimization for 500 epochs, with 46500 positive edges
22:52:22 Optimization finished
[1] "31 0.13"22:52:22 UMAP embedding parameters a = 1.478 b = 0.9272
22:52:22 Read 1203 rows and found 38 numeric columns
22:52:22 Using Annoy for neighbor search, n_neighbors = 31
22:52:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:52:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874061c2b5
22:52:23 Searching Annoy index using 1 thread, search_k = 3100
22:52:23 Annoy recall = 100%
22:52:25 Commencing smooth kNN distance calibration using 1 thread
22:52:30 Initializing from normalized Laplacian + noise
22:52:30 Commencing optimization for 500 epochs, with 46500 positive edges
22:52:35 Optimization finished
[1] "31 0.14"22:52:35 UMAP embedding parameters a = 1.446 b = 0.938
22:52:35 Read 1203 rows and found 38 numeric columns
22:52:35 Using Annoy for neighbor search, n_neighbors = 31
22:52:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:52:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b5d9a34
22:52:35 Searching Annoy index using 1 thread, search_k = 3100
22:52:36 Annoy recall = 100%
22:52:38 Commencing smooth kNN distance calibration using 1 thread
22:52:43 Initializing from normalized Laplacian + noise
22:52:43 Commencing optimization for 500 epochs, with 46500 positive edges
22:52:47 Optimization finished
[1] "31 0.15"22:52:48 UMAP embedding parameters a = 1.414 b = 0.9488
22:52:48 Read 1203 rows and found 38 numeric columns
22:52:48 Using Annoy for neighbor search, n_neighbors = 31
22:52:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:52:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722beb4fe
22:52:48 Searching Annoy index using 1 thread, search_k = 3100
22:52:48 Annoy recall = 100%
22:52:51 Commencing smooth kNN distance calibration using 1 thread
22:52:55 Initializing from normalized Laplacian + noise
22:52:55 Commencing optimization for 500 epochs, with 46500 positive edges
22:53:00 Optimization finished
[1] "31 0.16"22:53:00 UMAP embedding parameters a = 1.383 b = 0.9596
22:53:00 Read 1203 rows and found 38 numeric columns
22:53:00 Using Annoy for neighbor search, n_neighbors = 31
22:53:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:53:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877630b2da
22:53:01 Searching Annoy index using 1 thread, search_k = 3100
22:53:01 Annoy recall = 100%
22:53:03 Commencing smooth kNN distance calibration using 1 thread
22:53:08 Initializing from normalized Laplacian + noise
22:53:08 Commencing optimization for 500 epochs, with 46500 positive edges
22:53:13 Optimization finished
[1] "31 0.17"22:53:13 UMAP embedding parameters a = 1.352 b = 0.9704
22:53:13 Read 1203 rows and found 38 numeric columns
22:53:13 Using Annoy for neighbor search, n_neighbors = 31
22:53:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:53:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875eaffe0b
22:53:13 Searching Annoy index using 1 thread, search_k = 3100
22:53:14 Annoy recall = 100%
22:53:16 Commencing smooth kNN distance calibration using 1 thread
22:53:20 Initializing from normalized Laplacian + noise
22:53:20 Commencing optimization for 500 epochs, with 46500 positive edges
22:53:25 Optimization finished
[1] "31 0.18"22:53:26 UMAP embedding parameters a = 1.321 b = 0.9813
22:53:26 Read 1203 rows and found 38 numeric columns
22:53:26 Using Annoy for neighbor search, n_neighbors = 31
22:53:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:53:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765ddb5e5
22:53:26 Searching Annoy index using 1 thread, search_k = 3100
22:53:26 Annoy recall = 100%
22:53:29 Commencing smooth kNN distance calibration using 1 thread
22:53:33 Initializing from normalized Laplacian + noise
22:53:33 Commencing optimization for 500 epochs, with 46500 positive edges
22:53:38 Optimization finished
[1] "31 0.19"22:53:39 UMAP embedding parameters a = 1.292 b = 0.9921
22:53:39 Read 1203 rows and found 38 numeric columns
22:53:39 Using Annoy for neighbor search, n_neighbors = 31
22:53:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:53:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87598f9bb6
22:53:39 Searching Annoy index using 1 thread, search_k = 3100
22:53:39 Annoy recall = 100%
22:53:41 Commencing smooth kNN distance calibration using 1 thread
22:53:46 Initializing from normalized Laplacian + noise
22:53:46 Commencing optimization for 500 epochs, with 46500 positive edges
22:53:51 Optimization finished
[1] "31 0.2"22:53:51 UMAP embedding parameters a = 1.262 b = 1.003
22:53:51 Read 1203 rows and found 38 numeric columns
22:53:51 Using Annoy for neighbor search, n_neighbors = 31
22:53:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:53:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a89ea0d
22:53:52 Searching Annoy index using 1 thread, search_k = 3100
22:53:52 Annoy recall = 100%
22:53:54 Commencing smooth kNN distance calibration using 1 thread
22:53:59 Initializing from normalized Laplacian + noise
22:53:59 Commencing optimization for 500 epochs, with 46500 positive edges
22:54:04 Optimization finished
[1] "32 0"22:54:04 UMAP embedding parameters a = 1.933 b = 0.7905
22:54:04 Read 1203 rows and found 38 numeric columns
22:54:04 Using Annoy for neighbor search, n_neighbors = 32
22:54:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:54:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87aedbeed
22:54:04 Searching Annoy index using 1 thread, search_k = 3200
22:54:05 Annoy recall = 100%
22:54:07 Commencing smooth kNN distance calibration using 1 thread
22:54:12 Initializing from normalized Laplacian + noise
22:54:12 Commencing optimization for 500 epochs, with 47960 positive edges
22:54:17 Optimization finished
[1] "32 0.01"22:54:17 UMAP embedding parameters a = 1.896 b = 0.8006
22:54:17 Read 1203 rows and found 38 numeric columns
22:54:17 Using Annoy for neighbor search, n_neighbors = 32
22:54:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:54:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871383122b
22:54:17 Searching Annoy index using 1 thread, search_k = 3200
22:54:17 Annoy recall = 100%
22:54:20 Commencing smooth kNN distance calibration using 1 thread
22:54:25 Initializing from normalized Laplacian + noise
22:54:25 Commencing optimization for 500 epochs, with 47960 positive edges
22:54:29 Optimization finished
[1] "32 0.02"22:54:30 UMAP embedding parameters a = 1.859 b = 0.8109
22:54:30 Read 1203 rows and found 38 numeric columns
22:54:30 Using Annoy for neighbor search, n_neighbors = 32
22:54:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:54:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ab62faa
22:54:30 Searching Annoy index using 1 thread, search_k = 3200
22:54:30 Annoy recall = 100%
22:54:33 Commencing smooth kNN distance calibration using 1 thread
22:54:38 Initializing from normalized Laplacian + noise
22:54:38 Commencing optimization for 500 epochs, with 47960 positive edges
22:54:43 Optimization finished
[1] "32 0.03"22:54:43 UMAP embedding parameters a = 1.822 b = 0.8212
22:54:43 Read 1203 rows and found 38 numeric columns
22:54:43 Using Annoy for neighbor search, n_neighbors = 32
22:54:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:54:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87578e75da
22:54:43 Searching Annoy index using 1 thread, search_k = 3200
22:54:43 Annoy recall = 100%
22:54:46 Commencing smooth kNN distance calibration using 1 thread
22:54:50 Initializing from normalized Laplacian + noise
22:54:50 Commencing optimization for 500 epochs, with 47960 positive edges
22:54:55 Optimization finished
[1] "32 0.04"22:54:55 UMAP embedding parameters a = 1.786 b = 0.8316
22:54:55 Read 1203 rows and found 38 numeric columns
22:54:55 Using Annoy for neighbor search, n_neighbors = 32
22:54:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:54:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730b09b91
22:54:56 Searching Annoy index using 1 thread, search_k = 3200
22:54:56 Annoy recall = 100%
22:54:58 Commencing smooth kNN distance calibration using 1 thread
22:55:03 Initializing from normalized Laplacian + noise
22:55:03 Commencing optimization for 500 epochs, with 47960 positive edges
22:55:08 Optimization finished
[1] "32 0.05"22:55:08 UMAP embedding parameters a = 1.75 b = 0.8421
22:55:08 Read 1203 rows and found 38 numeric columns
22:55:08 Using Annoy for neighbor search, n_neighbors = 32
22:55:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:55:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a0862f2
22:55:09 Searching Annoy index using 1 thread, search_k = 3200
22:55:09 Annoy recall = 100%
22:55:11 Commencing smooth kNN distance calibration using 1 thread
22:55:16 Initializing from normalized Laplacian + noise
22:55:16 Commencing optimization for 500 epochs, with 47960 positive edges
22:55:21 Optimization finished
[1] "32 0.06"22:55:21 UMAP embedding parameters a = 1.715 b = 0.8526
22:55:21 Read 1203 rows and found 38 numeric columns
22:55:21 Using Annoy for neighbor search, n_neighbors = 32
22:55:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:55:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770762a6e
22:55:22 Searching Annoy index using 1 thread, search_k = 3200
22:55:22 Annoy recall = 100%
22:55:24 Commencing smooth kNN distance calibration using 1 thread
22:55:29 Initializing from normalized Laplacian + noise
22:55:29 Commencing optimization for 500 epochs, with 47960 positive edges
22:55:34 Optimization finished
[1] "32 0.07"22:55:34 UMAP embedding parameters a = 1.68 b = 0.8631
22:55:34 Read 1203 rows and found 38 numeric columns
22:55:34 Using Annoy for neighbor search, n_neighbors = 32
22:55:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:55:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765d83b92
22:55:34 Searching Annoy index using 1 thread, search_k = 3200
22:55:35 Annoy recall = 100%
22:55:37 Commencing smooth kNN distance calibration using 1 thread
22:55:42 Initializing from normalized Laplacian + noise
22:55:42 Commencing optimization for 500 epochs, with 47960 positive edges
22:55:47 Optimization finished
[1] "32 0.08"22:55:47 UMAP embedding parameters a = 1.645 b = 0.8737
22:55:47 Read 1203 rows and found 38 numeric columns
22:55:47 Using Annoy for neighbor search, n_neighbors = 32
22:55:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:55:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b370017
22:55:47 Searching Annoy index using 1 thread, search_k = 3200
22:55:47 Annoy recall = 100%
22:55:50 Commencing smooth kNN distance calibration using 1 thread
22:55:54 Initializing from normalized Laplacian + noise
22:55:54 Commencing optimization for 500 epochs, with 47960 positive edges
22:55:59 Optimization finished
[1] "32 0.09"22:56:00 UMAP embedding parameters a = 1.611 b = 0.8844
22:56:00 Read 1203 rows and found 38 numeric columns
22:56:00 Using Annoy for neighbor search, n_neighbors = 32
22:56:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:56:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877edca60c
22:56:00 Searching Annoy index using 1 thread, search_k = 3200
22:56:00 Annoy recall = 100%
22:56:02 Commencing smooth kNN distance calibration using 1 thread
22:56:07 Initializing from normalized Laplacian + noise
22:56:07 Commencing optimization for 500 epochs, with 47960 positive edges
22:56:12 Optimization finished
[1] "32 0.1"22:56:12 UMAP embedding parameters a = 1.577 b = 0.8951
22:56:12 Read 1203 rows and found 38 numeric columns
22:56:12 Using Annoy for neighbor search, n_neighbors = 32
22:56:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:56:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874af8a24f
22:56:13 Searching Annoy index using 1 thread, search_k = 3200
22:56:13 Annoy recall = 100%
22:56:15 Commencing smooth kNN distance calibration using 1 thread
22:56:20 Initializing from normalized Laplacian + noise
22:56:20 Commencing optimization for 500 epochs, with 47960 positive edges
22:56:25 Optimization finished
[1] "32 0.11"22:56:25 UMAP embedding parameters a = 1.544 b = 0.9058
22:56:25 Read 1203 rows and found 38 numeric columns
22:56:25 Using Annoy for neighbor search, n_neighbors = 32
22:56:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:56:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a13ae71
22:56:25 Searching Annoy index using 1 thread, search_k = 3200
22:56:26 Annoy recall = 100%
22:56:28 Commencing smooth kNN distance calibration using 1 thread
22:56:33 Initializing from normalized Laplacian + noise
22:56:33 Commencing optimization for 500 epochs, with 47960 positive edges
22:56:38 Optimization finished
[1] "32 0.12"22:56:38 UMAP embedding parameters a = 1.51 b = 0.9165
22:56:38 Read 1203 rows and found 38 numeric columns
22:56:38 Using Annoy for neighbor search, n_neighbors = 32
22:56:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:56:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879311166
22:56:38 Searching Annoy index using 1 thread, search_k = 3200
22:56:38 Annoy recall = 100%
22:56:41 Commencing smooth kNN distance calibration using 1 thread
22:56:45 Initializing from normalized Laplacian + noise
22:56:45 Commencing optimization for 500 epochs, with 47960 positive edges
22:56:51 Optimization finished
[1] "32 0.13"22:56:51 UMAP embedding parameters a = 1.478 b = 0.9272
22:56:51 Read 1203 rows and found 38 numeric columns
22:56:51 Using Annoy for neighbor search, n_neighbors = 32
22:56:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:56:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e1cf443
22:56:51 Searching Annoy index using 1 thread, search_k = 3200
22:56:51 Annoy recall = 100%
22:56:54 Commencing smooth kNN distance calibration using 1 thread
22:56:58 Initializing from normalized Laplacian + noise
22:56:58 Commencing optimization for 500 epochs, with 47960 positive edges
22:57:03 Optimization finished
[1] "32 0.14"22:57:04 UMAP embedding parameters a = 1.446 b = 0.938
22:57:04 Read 1203 rows and found 38 numeric columns
22:57:04 Using Annoy for neighbor search, n_neighbors = 32
22:57:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:57:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e6053e2
22:57:04 Searching Annoy index using 1 thread, search_k = 3200
22:57:04 Annoy recall = 100%
22:57:07 Commencing smooth kNN distance calibration using 1 thread
22:57:11 Initializing from normalized Laplacian + noise
22:57:11 Commencing optimization for 500 epochs, with 47960 positive edges
22:57:16 Optimization finished
[1] "32 0.15"22:57:17 UMAP embedding parameters a = 1.414 b = 0.9488
22:57:17 Read 1203 rows and found 38 numeric columns
22:57:17 Using Annoy for neighbor search, n_neighbors = 32
22:57:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:57:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715164858
22:57:17 Searching Annoy index using 1 thread, search_k = 3200
22:57:17 Annoy recall = 100%
22:57:19 Commencing smooth kNN distance calibration using 1 thread
22:57:24 Initializing from normalized Laplacian + noise
22:57:24 Commencing optimization for 500 epochs, with 47960 positive edges
22:57:29 Optimization finished
[1] "32 0.16"22:57:29 UMAP embedding parameters a = 1.383 b = 0.9596
22:57:29 Read 1203 rows and found 38 numeric columns
22:57:29 Using Annoy for neighbor search, n_neighbors = 32
22:57:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:57:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875695be55
22:57:29 Searching Annoy index using 1 thread, search_k = 3200
22:57:30 Annoy recall = 100%
22:57:32 Commencing smooth kNN distance calibration using 1 thread
22:57:37 Initializing from normalized Laplacian + noise
22:57:37 Commencing optimization for 500 epochs, with 47960 positive edges
22:57:42 Optimization finished
[1] "32 0.17"22:57:42 UMAP embedding parameters a = 1.352 b = 0.9704
22:57:42 Read 1203 rows and found 38 numeric columns
22:57:42 Using Annoy for neighbor search, n_neighbors = 32
22:57:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:57:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87343d9ffa
22:57:42 Searching Annoy index using 1 thread, search_k = 3200
22:57:43 Annoy recall = 100%
22:57:45 Commencing smooth kNN distance calibration using 1 thread
22:57:50 Initializing from normalized Laplacian + noise
22:57:50 Commencing optimization for 500 epochs, with 47960 positive edges
22:57:55 Optimization finished
[1] "32 0.18"22:57:55 UMAP embedding parameters a = 1.321 b = 0.9813
22:57:55 Read 1203 rows and found 38 numeric columns
22:57:55 Using Annoy for neighbor search, n_neighbors = 32
22:57:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:57:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878658f3e
22:57:55 Searching Annoy index using 1 thread, search_k = 3200
22:57:56 Annoy recall = 100%
22:57:58 Commencing smooth kNN distance calibration using 1 thread
22:58:03 Initializing from normalized Laplacian + noise
22:58:03 Commencing optimization for 500 epochs, with 47960 positive edges
22:58:08 Optimization finished
[1] "32 0.19"22:58:08 UMAP embedding parameters a = 1.292 b = 0.9921
22:58:08 Read 1203 rows and found 38 numeric columns
22:58:08 Using Annoy for neighbor search, n_neighbors = 32
22:58:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872baccb1b
22:58:08 Searching Annoy index using 1 thread, search_k = 3200
22:58:08 Annoy recall = 100%
22:58:11 Commencing smooth kNN distance calibration using 1 thread
22:58:15 Initializing from normalized Laplacian + noise
22:58:15 Commencing optimization for 500 epochs, with 47960 positive edges
22:58:20 Optimization finished
[1] "32 0.2"22:58:21 UMAP embedding parameters a = 1.262 b = 1.003
22:58:21 Read 1203 rows and found 38 numeric columns
22:58:21 Using Annoy for neighbor search, n_neighbors = 32
22:58:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774ce0695
22:58:21 Searching Annoy index using 1 thread, search_k = 3200
22:58:21 Annoy recall = 100%
22:58:24 Commencing smooth kNN distance calibration using 1 thread
22:58:28 Initializing from normalized Laplacian + noise
22:58:28 Commencing optimization for 500 epochs, with 47960 positive edges
22:58:33 Optimization finished
[1] "33 0"22:58:34 UMAP embedding parameters a = 1.933 b = 0.7905
22:58:34 Read 1203 rows and found 38 numeric columns
22:58:34 Using Annoy for neighbor search, n_neighbors = 33
22:58:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b52dc4c
22:58:34 Searching Annoy index using 1 thread, search_k = 3300
22:58:34 Annoy recall = 100%
22:58:36 Commencing smooth kNN distance calibration using 1 thread
22:58:41 Initializing from normalized Laplacian + noise
22:58:41 Commencing optimization for 500 epochs, with 49344 positive edges
22:58:46 Optimization finished
[1] "33 0.01"22:58:47 UMAP embedding parameters a = 1.896 b = 0.8006
22:58:47 Read 1203 rows and found 38 numeric columns
22:58:47 Using Annoy for neighbor search, n_neighbors = 33
22:58:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87569888d4
22:58:47 Searching Annoy index using 1 thread, search_k = 3300
22:58:47 Annoy recall = 100%
22:58:50 Commencing smooth kNN distance calibration using 1 thread
22:58:54 Initializing from normalized Laplacian + noise
22:58:54 Commencing optimization for 500 epochs, with 49344 positive edges
22:58:59 Optimization finished
[1] "33 0.02"22:59:00 UMAP embedding parameters a = 1.859 b = 0.8109
22:59:00 Read 1203 rows and found 38 numeric columns
22:59:00 Using Annoy for neighbor search, n_neighbors = 33
22:59:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:59:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87352fc94b
22:59:00 Searching Annoy index using 1 thread, search_k = 3300
22:59:00 Annoy recall = 100%
22:59:02 Commencing smooth kNN distance calibration using 1 thread
22:59:07 Initializing from normalized Laplacian + noise
22:59:07 Commencing optimization for 500 epochs, with 49344 positive edges
22:59:12 Optimization finished
[1] "33 0.03"22:59:13 UMAP embedding parameters a = 1.822 b = 0.8212
22:59:13 Read 1203 rows and found 38 numeric columns
22:59:13 Using Annoy for neighbor search, n_neighbors = 33
22:59:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:59:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756b07680
22:59:13 Searching Annoy index using 1 thread, search_k = 3300
22:59:13 Annoy recall = 100%
22:59:16 Commencing smooth kNN distance calibration using 1 thread
22:59:20 Initializing from normalized Laplacian + noise
22:59:20 Commencing optimization for 500 epochs, with 49344 positive edges
22:59:26 Optimization finished
[1] "33 0.04"22:59:26 UMAP embedding parameters a = 1.786 b = 0.8316
22:59:26 Read 1203 rows and found 38 numeric columns
22:59:26 Using Annoy for neighbor search, n_neighbors = 33
22:59:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:59:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779573dd2
22:59:26 Searching Annoy index using 1 thread, search_k = 3300
22:59:26 Annoy recall = 100%
22:59:29 Commencing smooth kNN distance calibration using 1 thread
22:59:33 Initializing from normalized Laplacian + noise
22:59:33 Commencing optimization for 500 epochs, with 49344 positive edges
22:59:38 Optimization finished
[1] "33 0.05"22:59:39 UMAP embedding parameters a = 1.75 b = 0.8421
22:59:39 Read 1203 rows and found 38 numeric columns
22:59:39 Using Annoy for neighbor search, n_neighbors = 33
22:59:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:59:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b607c25
22:59:39 Searching Annoy index using 1 thread, search_k = 3300
22:59:39 Annoy recall = 100%
22:59:42 Commencing smooth kNN distance calibration using 1 thread
22:59:46 Initializing from normalized Laplacian + noise
22:59:46 Commencing optimization for 500 epochs, with 49344 positive edges
22:59:51 Optimization finished
[1] "33 0.06"22:59:52 UMAP embedding parameters a = 1.715 b = 0.8526
22:59:52 Read 1203 rows and found 38 numeric columns
22:59:52 Using Annoy for neighbor search, n_neighbors = 33
22:59:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:59:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873560748b
22:59:52 Searching Annoy index using 1 thread, search_k = 3300
22:59:52 Annoy recall = 100%
22:59:55 Commencing smooth kNN distance calibration using 1 thread
22:59:59 Initializing from normalized Laplacian + noise
22:59:59 Commencing optimization for 500 epochs, with 49344 positive edges
23:00:04 Optimization finished
[1] "33 0.07"23:00:05 UMAP embedding parameters a = 1.68 b = 0.8631
23:00:05 Read 1203 rows and found 38 numeric columns
23:00:05 Using Annoy for neighbor search, n_neighbors = 33
23:00:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:00:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f34f3b7
23:00:05 Searching Annoy index using 1 thread, search_k = 3300
23:00:05 Annoy recall = 100%
23:00:08 Commencing smooth kNN distance calibration using 1 thread
23:00:12 Initializing from normalized Laplacian + noise
23:00:12 Commencing optimization for 500 epochs, with 49344 positive edges
23:00:18 Optimization finished
[1] "33 0.08"23:00:18 UMAP embedding parameters a = 1.645 b = 0.8737
23:00:18 Read 1203 rows and found 38 numeric columns
23:00:18 Using Annoy for neighbor search, n_neighbors = 33
23:00:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:00:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f017db
23:00:18 Searching Annoy index using 1 thread, search_k = 3300
23:00:18 Annoy recall = 100%
23:00:21 Commencing smooth kNN distance calibration using 1 thread
23:00:26 Initializing from normalized Laplacian + noise
23:00:26 Commencing optimization for 500 epochs, with 49344 positive edges
23:00:31 Optimization finished
[1] "33 0.09"23:00:31 UMAP embedding parameters a = 1.611 b = 0.8844
23:00:31 Read 1203 rows and found 38 numeric columns
23:00:31 Using Annoy for neighbor search, n_neighbors = 33
23:00:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:00:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fea5e99
23:00:31 Searching Annoy index using 1 thread, search_k = 3300
23:00:31 Annoy recall = 100%
23:00:34 Commencing smooth kNN distance calibration using 1 thread
23:00:38 Initializing from normalized Laplacian + noise
23:00:38 Commencing optimization for 500 epochs, with 49344 positive edges
23:00:43 Optimization finished
[1] "33 0.1"23:00:44 UMAP embedding parameters a = 1.577 b = 0.8951
23:00:44 Read 1203 rows and found 38 numeric columns
23:00:44 Using Annoy for neighbor search, n_neighbors = 33
23:00:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:00:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a22b2a4
23:00:44 Searching Annoy index using 1 thread, search_k = 3300
23:00:44 Annoy recall = 100%
23:00:47 Commencing smooth kNN distance calibration using 1 thread
23:00:51 Initializing from normalized Laplacian + noise
23:00:51 Commencing optimization for 500 epochs, with 49344 positive edges
23:00:57 Optimization finished
[1] "33 0.11"23:00:57 UMAP embedding parameters a = 1.544 b = 0.9058
23:00:57 Read 1203 rows and found 38 numeric columns
23:00:57 Using Annoy for neighbor search, n_neighbors = 33
23:00:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:00:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718732a07
23:00:57 Searching Annoy index using 1 thread, search_k = 3300
23:00:57 Annoy recall = 100%
23:01:00 Commencing smooth kNN distance calibration using 1 thread
23:01:05 Initializing from normalized Laplacian + noise
23:01:05 Commencing optimization for 500 epochs, with 49344 positive edges
23:01:10 Optimization finished
[1] "33 0.12"23:01:10 UMAP embedding parameters a = 1.51 b = 0.9165
23:01:10 Read 1203 rows and found 38 numeric columns
23:01:10 Using Annoy for neighbor search, n_neighbors = 33
23:01:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:01:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874aa08e43
23:01:10 Searching Annoy index using 1 thread, search_k = 3300
23:01:11 Annoy recall = 100%
23:01:13 Commencing smooth kNN distance calibration using 1 thread
23:01:18 Initializing from normalized Laplacian + noise
23:01:18 Commencing optimization for 500 epochs, with 49344 positive edges
23:01:23 Optimization finished
[1] "33 0.13"23:01:23 UMAP embedding parameters a = 1.478 b = 0.9272
23:01:23 Read 1203 rows and found 38 numeric columns
23:01:23 Using Annoy for neighbor search, n_neighbors = 33
23:01:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:01:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741b1287f
23:01:24 Searching Annoy index using 1 thread, search_k = 3300
23:01:24 Annoy recall = 100%
23:01:26 Commencing smooth kNN distance calibration using 1 thread
23:01:31 Initializing from normalized Laplacian + noise
23:01:31 Commencing optimization for 500 epochs, with 49344 positive edges
23:01:36 Optimization finished
[1] "33 0.14"23:01:36 UMAP embedding parameters a = 1.446 b = 0.938
23:01:36 Read 1203 rows and found 38 numeric columns
23:01:36 Using Annoy for neighbor search, n_neighbors = 33
23:01:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:01:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874923c598
23:01:37 Searching Annoy index using 1 thread, search_k = 3300
23:01:37 Annoy recall = 100%
23:01:39 Commencing smooth kNN distance calibration using 1 thread
23:01:44 Initializing from normalized Laplacian + noise
23:01:44 Commencing optimization for 500 epochs, with 49344 positive edges
23:01:49 Optimization finished
[1] "33 0.15"23:01:50 UMAP embedding parameters a = 1.414 b = 0.9488
23:01:50 Read 1203 rows and found 38 numeric columns
23:01:50 Using Annoy for neighbor search, n_neighbors = 33
23:01:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:01:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754a8f135
23:01:50 Searching Annoy index using 1 thread, search_k = 3300
23:01:50 Annoy recall = 100%
23:01:53 Commencing smooth kNN distance calibration using 1 thread
23:01:57 Initializing from normalized Laplacian + noise
23:01:57 Commencing optimization for 500 epochs, with 49344 positive edges
23:02:02 Optimization finished
[1] "33 0.16"23:02:03 UMAP embedding parameters a = 1.383 b = 0.9596
23:02:03 Read 1203 rows and found 38 numeric columns
23:02:03 Using Annoy for neighbor search, n_neighbors = 33
23:02:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:02:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87322752ed
23:02:03 Searching Annoy index using 1 thread, search_k = 3300
23:02:03 Annoy recall = 100%
23:02:06 Commencing smooth kNN distance calibration using 1 thread
23:02:10 Initializing from normalized Laplacian + noise
23:02:10 Commencing optimization for 500 epochs, with 49344 positive edges
23:02:15 Optimization finished
[1] "33 0.17"23:02:16 UMAP embedding parameters a = 1.352 b = 0.9704
23:02:16 Read 1203 rows and found 38 numeric columns
23:02:16 Using Annoy for neighbor search, n_neighbors = 33
23:02:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:02:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872efc012a
23:02:16 Searching Annoy index using 1 thread, search_k = 3300
23:02:16 Annoy recall = 100%
23:02:19 Commencing smooth kNN distance calibration using 1 thread
23:02:24 Initializing from normalized Laplacian + noise
23:02:24 Commencing optimization for 500 epochs, with 49344 positive edges
23:02:29 Optimization finished
[1] "33 0.18"23:02:29 UMAP embedding parameters a = 1.321 b = 0.9813
23:02:29 Read 1203 rows and found 38 numeric columns
23:02:29 Using Annoy for neighbor search, n_neighbors = 33
23:02:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:02:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876fdff14c
23:02:29 Searching Annoy index using 1 thread, search_k = 3300
23:02:30 Annoy recall = 100%
23:02:32 Commencing smooth kNN distance calibration using 1 thread
23:02:37 Initializing from normalized Laplacian + noise
23:02:37 Commencing optimization for 500 epochs, with 49344 positive edges
23:02:42 Optimization finished
[1] "33 0.19"23:02:42 UMAP embedding parameters a = 1.292 b = 0.9921
23:02:42 Read 1203 rows and found 38 numeric columns
23:02:42 Using Annoy for neighbor search, n_neighbors = 33
23:02:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:02:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873103f8f9
23:02:43 Searching Annoy index using 1 thread, search_k = 3300
23:02:43 Annoy recall = 100%
23:02:45 Commencing smooth kNN distance calibration using 1 thread
23:02:50 Initializing from normalized Laplacian + noise
23:02:50 Commencing optimization for 500 epochs, with 49344 positive edges
23:02:55 Optimization finished
[1] "33 0.2"23:02:56 UMAP embedding parameters a = 1.262 b = 1.003
23:02:56 Read 1203 rows and found 38 numeric columns
23:02:56 Using Annoy for neighbor search, n_neighbors = 33
23:02:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:02:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779f4a379
23:02:56 Searching Annoy index using 1 thread, search_k = 3300
23:02:56 Annoy recall = 100%
23:02:59 Commencing smooth kNN distance calibration using 1 thread
23:03:03 Initializing from normalized Laplacian + noise
23:03:03 Commencing optimization for 500 epochs, with 49344 positive edges
23:03:08 Optimization finished
[1] "34 0"23:03:09 UMAP embedding parameters a = 1.933 b = 0.7905
23:03:09 Read 1203 rows and found 38 numeric columns
23:03:09 Using Annoy for neighbor search, n_neighbors = 34
23:03:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:03:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779f39fbe
23:03:09 Searching Annoy index using 1 thread, search_k = 3400
23:03:09 Annoy recall = 100%
23:03:12 Commencing smooth kNN distance calibration using 1 thread
23:03:16 Initializing from normalized Laplacian + noise
23:03:16 Commencing optimization for 500 epochs, with 50752 positive edges
23:03:22 Optimization finished
[1] "34 0.01"23:03:22 UMAP embedding parameters a = 1.896 b = 0.8006
23:03:22 Read 1203 rows and found 38 numeric columns
23:03:22 Using Annoy for neighbor search, n_neighbors = 34
23:03:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:03:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a350a5f
23:03:22 Searching Annoy index using 1 thread, search_k = 3400
23:03:22 Annoy recall = 100%
23:03:25 Commencing smooth kNN distance calibration using 1 thread
23:03:30 Initializing from normalized Laplacian + noise
23:03:30 Commencing optimization for 500 epochs, with 50752 positive edges
23:03:35 Optimization finished
[1] "34 0.02"23:03:35 UMAP embedding parameters a = 1.859 b = 0.8109
23:03:35 Read 1203 rows and found 38 numeric columns
23:03:35 Using Annoy for neighbor search, n_neighbors = 34
23:03:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:03:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8781197bd
23:03:35 Searching Annoy index using 1 thread, search_k = 3400
23:03:36 Annoy recall = 100%
23:03:38 Commencing smooth kNN distance calibration using 1 thread
23:03:43 Initializing from normalized Laplacian + noise
23:03:43 Commencing optimization for 500 epochs, with 50752 positive edges
23:03:48 Optimization finished
[1] "34 0.03"23:03:48 UMAP embedding parameters a = 1.822 b = 0.8212
23:03:48 Read 1203 rows and found 38 numeric columns
23:03:48 Using Annoy for neighbor search, n_neighbors = 34
23:03:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:03:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871853f3a0
23:03:48 Searching Annoy index using 1 thread, search_k = 3400
23:03:49 Annoy recall = 100%
23:03:51 Commencing smooth kNN distance calibration using 1 thread
23:03:56 Initializing from normalized Laplacian + noise
23:03:56 Commencing optimization for 500 epochs, with 50752 positive edges
23:04:01 Optimization finished
[1] "34 0.04"23:04:01 UMAP embedding parameters a = 1.786 b = 0.8316
23:04:01 Read 1203 rows and found 38 numeric columns
23:04:01 Using Annoy for neighbor search, n_neighbors = 34
23:04:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:04:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f4b52b7
23:04:02 Searching Annoy index using 1 thread, search_k = 3400
23:04:02 Annoy recall = 100%
23:04:04 Commencing smooth kNN distance calibration using 1 thread
23:04:09 Initializing from normalized Laplacian + noise
23:04:09 Commencing optimization for 500 epochs, with 50752 positive edges
23:04:15 Optimization finished
[1] "34 0.05"23:04:15 UMAP embedding parameters a = 1.75 b = 0.8421
23:04:15 Read 1203 rows and found 38 numeric columns
23:04:15 Using Annoy for neighbor search, n_neighbors = 34
23:04:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:04:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ea75612
23:04:15 Searching Annoy index using 1 thread, search_k = 3400
23:04:15 Annoy recall = 100%
23:04:18 Commencing smooth kNN distance calibration using 1 thread
23:04:23 Initializing from normalized Laplacian + noise
23:04:23 Commencing optimization for 500 epochs, with 50752 positive edges
23:04:28 Optimization finished
[1] "34 0.06"23:04:28 UMAP embedding parameters a = 1.715 b = 0.8526
23:04:28 Read 1203 rows and found 38 numeric columns
23:04:28 Using Annoy for neighbor search, n_neighbors = 34
23:04:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:04:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c91939a
23:04:28 Searching Annoy index using 1 thread, search_k = 3400
23:04:29 Annoy recall = 100%
23:04:31 Commencing smooth kNN distance calibration using 1 thread
23:04:36 Initializing from normalized Laplacian + noise
23:04:36 Commencing optimization for 500 epochs, with 50752 positive edges
23:04:41 Optimization finished
[1] "34 0.07"23:04:41 UMAP embedding parameters a = 1.68 b = 0.8631
23:04:41 Read 1203 rows and found 38 numeric columns
23:04:41 Using Annoy for neighbor search, n_neighbors = 34
23:04:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:04:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757b0e1f5
23:04:42 Searching Annoy index using 1 thread, search_k = 3400
23:04:42 Annoy recall = 100%
23:04:44 Commencing smooth kNN distance calibration using 1 thread
23:04:49 Initializing from normalized Laplacian + noise
23:04:49 Commencing optimization for 500 epochs, with 50752 positive edges
23:04:55 Optimization finished
[1] "34 0.08"23:04:55 UMAP embedding parameters a = 1.645 b = 0.8737
23:04:55 Read 1203 rows and found 38 numeric columns
23:04:55 Using Annoy for neighbor search, n_neighbors = 34
23:04:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:04:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a54212d
23:04:55 Searching Annoy index using 1 thread, search_k = 3400
23:04:55 Annoy recall = 100%
23:04:58 Commencing smooth kNN distance calibration using 1 thread
23:05:03 Initializing from normalized Laplacian + noise
23:05:03 Commencing optimization for 500 epochs, with 50752 positive edges
23:05:08 Optimization finished
[1] "34 0.09"23:05:08 UMAP embedding parameters a = 1.611 b = 0.8844
23:05:08 Read 1203 rows and found 38 numeric columns
23:05:08 Using Annoy for neighbor search, n_neighbors = 34
23:05:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:05:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87415f9a2f
23:05:09 Searching Annoy index using 1 thread, search_k = 3400
23:05:09 Annoy recall = 100%
23:05:11 Commencing smooth kNN distance calibration using 1 thread
23:05:16 Initializing from normalized Laplacian + noise
23:05:16 Commencing optimization for 500 epochs, with 50752 positive edges
23:05:21 Optimization finished
[1] "34 0.1"23:05:22 UMAP embedding parameters a = 1.577 b = 0.8951
23:05:22 Read 1203 rows and found 38 numeric columns
23:05:22 Using Annoy for neighbor search, n_neighbors = 34
23:05:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:05:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873303be41
23:05:22 Searching Annoy index using 1 thread, search_k = 3400
23:05:22 Annoy recall = 100%
23:05:25 Commencing smooth kNN distance calibration using 1 thread
23:05:30 Initializing from normalized Laplacian + noise
23:05:30 Commencing optimization for 500 epochs, with 50752 positive edges
23:05:35 Optimization finished
[1] "34 0.11"23:05:35 UMAP embedding parameters a = 1.544 b = 0.9058
23:05:35 Read 1203 rows and found 38 numeric columns
23:05:35 Using Annoy for neighbor search, n_neighbors = 34
23:05:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:05:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760ecaa02
23:05:35 Searching Annoy index using 1 thread, search_k = 3400
23:05:36 Annoy recall = 100%
23:05:39 Commencing smooth kNN distance calibration using 1 thread
23:05:44 Initializing from normalized Laplacian + noise
23:05:44 Commencing optimization for 500 epochs, with 50752 positive edges
23:05:49 Optimization finished
[1] "34 0.12"23:05:49 UMAP embedding parameters a = 1.51 b = 0.9165
23:05:49 Read 1203 rows and found 38 numeric columns
23:05:49 Using Annoy for neighbor search, n_neighbors = 34
23:05:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:05:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87768f637a
23:05:49 Searching Annoy index using 1 thread, search_k = 3400
23:05:50 Annoy recall = 100%
23:05:52 Commencing smooth kNN distance calibration using 1 thread
23:05:57 Initializing from normalized Laplacian + noise
23:05:57 Commencing optimization for 500 epochs, with 50752 positive edges
23:06:02 Optimization finished
[1] "34 0.13"23:06:02 UMAP embedding parameters a = 1.478 b = 0.9272
23:06:02 Read 1203 rows and found 38 numeric columns
23:06:02 Using Annoy for neighbor search, n_neighbors = 34
23:06:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:06:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879b434c2
23:06:03 Searching Annoy index using 1 thread, search_k = 3400
23:06:03 Annoy recall = 100%
23:06:05 Commencing smooth kNN distance calibration using 1 thread
23:06:10 Initializing from normalized Laplacian + noise
23:06:10 Commencing optimization for 500 epochs, with 50752 positive edges
23:06:16 Optimization finished
[1] "34 0.14"23:06:16 UMAP embedding parameters a = 1.446 b = 0.938
23:06:16 Read 1203 rows and found 38 numeric columns
23:06:16 Using Annoy for neighbor search, n_neighbors = 34
23:06:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:06:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a43e7d4
23:06:16 Searching Annoy index using 1 thread, search_k = 3400
23:06:16 Annoy recall = 100%
23:06:19 Commencing smooth kNN distance calibration using 1 thread
23:06:24 Initializing from normalized Laplacian + noise
23:06:24 Commencing optimization for 500 epochs, with 50752 positive edges
23:06:29 Optimization finished
[1] "34 0.15"23:06:29 UMAP embedding parameters a = 1.414 b = 0.9488
23:06:29 Read 1203 rows and found 38 numeric columns
23:06:29 Using Annoy for neighbor search, n_neighbors = 34
23:06:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:06:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721efdf9f
23:06:30 Searching Annoy index using 1 thread, search_k = 3400
23:06:30 Annoy recall = 100%
23:06:33 Commencing smooth kNN distance calibration using 1 thread
23:06:37 Initializing from normalized Laplacian + noise
23:06:37 Commencing optimization for 500 epochs, with 50752 positive edges
23:06:43 Optimization finished
[1] "34 0.16"23:06:43 UMAP embedding parameters a = 1.383 b = 0.9596
23:06:43 Read 1203 rows and found 38 numeric columns
23:06:43 Using Annoy for neighbor search, n_neighbors = 34
23:06:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:06:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f14a94d
23:06:43 Searching Annoy index using 1 thread, search_k = 3400
23:06:43 Annoy recall = 100%
23:06:46 Commencing smooth kNN distance calibration using 1 thread
23:06:51 Initializing from normalized Laplacian + noise
23:06:51 Commencing optimization for 500 epochs, with 50752 positive edges
23:06:56 Optimization finished
[1] "34 0.17"23:06:56 UMAP embedding parameters a = 1.352 b = 0.9704
23:06:56 Read 1203 rows and found 38 numeric columns
23:06:56 Using Annoy for neighbor search, n_neighbors = 34
23:06:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:06:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873978db8c
23:06:56 Searching Annoy index using 1 thread, search_k = 3400
23:06:57 Annoy recall = 100%
23:06:59 Commencing smooth kNN distance calibration using 1 thread
23:07:04 Initializing from normalized Laplacian + noise
23:07:04 Commencing optimization for 500 epochs, with 50752 positive edges
23:07:09 Optimization finished
[1] "34 0.18"23:07:09 UMAP embedding parameters a = 1.321 b = 0.9813
23:07:09 Read 1203 rows and found 38 numeric columns
23:07:09 Using Annoy for neighbor search, n_neighbors = 34
23:07:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:07:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726dff77b
23:07:10 Searching Annoy index using 1 thread, search_k = 3400
23:07:10 Annoy recall = 100%
23:07:12 Commencing smooth kNN distance calibration using 1 thread
23:07:17 Initializing from normalized Laplacian + noise
23:07:17 Commencing optimization for 500 epochs, with 50752 positive edges
23:07:23 Optimization finished
[1] "34 0.19"23:07:23 UMAP embedding parameters a = 1.292 b = 0.9921
23:07:23 Read 1203 rows and found 38 numeric columns
23:07:23 Using Annoy for neighbor search, n_neighbors = 34
23:07:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:07:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876eff07e6
23:07:23 Searching Annoy index using 1 thread, search_k = 3400
23:07:23 Annoy recall = 100%
23:07:26 Commencing smooth kNN distance calibration using 1 thread
23:07:31 Initializing from normalized Laplacian + noise
23:07:31 Commencing optimization for 500 epochs, with 50752 positive edges
23:07:36 Optimization finished
[1] "34 0.2"23:07:36 UMAP embedding parameters a = 1.262 b = 1.003
23:07:36 Read 1203 rows and found 38 numeric columns
23:07:36 Using Annoy for neighbor search, n_neighbors = 34
23:07:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:07:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87239b8e30
23:07:37 Searching Annoy index using 1 thread, search_k = 3400
23:07:37 Annoy recall = 100%
23:07:39 Commencing smooth kNN distance calibration using 1 thread
23:07:44 Initializing from normalized Laplacian + noise
23:07:44 Commencing optimization for 500 epochs, with 50752 positive edges
23:07:49 Optimization finished
[1] "35 0"23:07:49 UMAP embedding parameters a = 1.933 b = 0.7905
23:07:49 Read 1203 rows and found 38 numeric columns
23:07:50 Using Annoy for neighbor search, n_neighbors = 35
23:07:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:07:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f532182
23:07:50 Searching Annoy index using 1 thread, search_k = 3500
23:07:50 Annoy recall = 100%
23:07:53 Commencing smooth kNN distance calibration using 1 thread
23:07:57 Initializing from normalized Laplacian + noise
23:07:57 Commencing optimization for 500 epochs, with 52240 positive edges
23:08:03 Optimization finished
[1] "35 0.01"23:08:03 UMAP embedding parameters a = 1.896 b = 0.8006
23:08:03 Read 1203 rows and found 38 numeric columns
23:08:03 Using Annoy for neighbor search, n_neighbors = 35
23:08:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:08:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87399f9629
23:08:03 Searching Annoy index using 1 thread, search_k = 3500
23:08:03 Annoy recall = 100%
23:08:06 Commencing smooth kNN distance calibration using 1 thread
23:08:11 Initializing from normalized Laplacian + noise
23:08:11 Commencing optimization for 500 epochs, with 52240 positive edges
23:08:16 Optimization finished
[1] "35 0.02"23:08:16 UMAP embedding parameters a = 1.859 b = 0.8109
23:08:16 Read 1203 rows and found 38 numeric columns
23:08:16 Using Annoy for neighbor search, n_neighbors = 35
23:08:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:08:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87654cb6af
23:08:17 Searching Annoy index using 1 thread, search_k = 3500
23:08:17 Annoy recall = 100%
23:08:19 Commencing smooth kNN distance calibration using 1 thread
23:08:24 Initializing from normalized Laplacian + noise
23:08:24 Commencing optimization for 500 epochs, with 52240 positive edges
23:08:30 Optimization finished
[1] "35 0.03"23:08:30 UMAP embedding parameters a = 1.822 b = 0.8212
23:08:30 Read 1203 rows and found 38 numeric columns
23:08:30 Using Annoy for neighbor search, n_neighbors = 35
23:08:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:08:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87876e71a
23:08:30 Searching Annoy index using 1 thread, search_k = 3500
23:08:30 Annoy recall = 100%
23:08:33 Commencing smooth kNN distance calibration using 1 thread
23:08:38 Initializing from normalized Laplacian + noise
23:08:38 Commencing optimization for 500 epochs, with 52240 positive edges
23:08:43 Optimization finished
[1] "35 0.04"23:08:43 UMAP embedding parameters a = 1.786 b = 0.8316
23:08:43 Read 1203 rows and found 38 numeric columns
23:08:43 Using Annoy for neighbor search, n_neighbors = 35
23:08:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:08:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e48875f
23:08:44 Searching Annoy index using 1 thread, search_k = 3500
23:08:44 Annoy recall = 100%
23:08:46 Commencing smooth kNN distance calibration using 1 thread
23:08:51 Initializing from normalized Laplacian + noise
23:08:51 Commencing optimization for 500 epochs, with 52240 positive edges
23:08:57 Optimization finished
[1] "35 0.05"23:08:57 UMAP embedding parameters a = 1.75 b = 0.8421
23:08:57 Read 1203 rows and found 38 numeric columns
23:08:57 Using Annoy for neighbor search, n_neighbors = 35
23:08:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:08:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871774099c
23:08:57 Searching Annoy index using 1 thread, search_k = 3500
23:08:57 Annoy recall = 100%
23:09:00 Commencing smooth kNN distance calibration using 1 thread
23:09:05 Initializing from normalized Laplacian + noise
23:09:05 Commencing optimization for 500 epochs, with 52240 positive edges
23:09:10 Optimization finished
[1] "35 0.06"23:09:10 UMAP embedding parameters a = 1.715 b = 0.8526
23:09:10 Read 1203 rows and found 38 numeric columns
23:09:10 Using Annoy for neighbor search, n_neighbors = 35
23:09:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:09:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873772e844
23:09:11 Searching Annoy index using 1 thread, search_k = 3500
23:09:11 Annoy recall = 100%
23:09:13 Commencing smooth kNN distance calibration using 1 thread
23:09:18 Initializing from normalized Laplacian + noise
23:09:18 Commencing optimization for 500 epochs, with 52240 positive edges
23:09:24 Optimization finished
[1] "35 0.07"23:09:24 UMAP embedding parameters a = 1.68 b = 0.8631
23:09:24 Read 1203 rows and found 38 numeric columns
23:09:24 Using Annoy for neighbor search, n_neighbors = 35
23:09:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:09:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e2878ab
23:09:24 Searching Annoy index using 1 thread, search_k = 3500
23:09:25 Annoy recall = 100%
23:09:27 Commencing smooth kNN distance calibration using 1 thread
23:09:32 Initializing from normalized Laplacian + noise
23:09:32 Commencing optimization for 500 epochs, with 52240 positive edges
23:09:37 Optimization finished
[1] "35 0.08"23:09:38 UMAP embedding parameters a = 1.645 b = 0.8737
23:09:38 Read 1203 rows and found 38 numeric columns
23:09:38 Using Annoy for neighbor search, n_neighbors = 35
23:09:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:09:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748780295
23:09:38 Searching Annoy index using 1 thread, search_k = 3500
23:09:38 Annoy recall = 100%
23:09:41 Commencing smooth kNN distance calibration using 1 thread
23:09:46 Initializing from normalized Laplacian + noise
23:09:46 Commencing optimization for 500 epochs, with 52240 positive edges
23:09:51 Optimization finished
[1] "35 0.09"23:09:51 UMAP embedding parameters a = 1.611 b = 0.8844
23:09:51 Read 1203 rows and found 38 numeric columns
23:09:51 Using Annoy for neighbor search, n_neighbors = 35
23:09:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:09:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731678bbd
23:09:51 Searching Annoy index using 1 thread, search_k = 3500
23:09:52 Annoy recall = 100%
23:09:54 Commencing smooth kNN distance calibration using 1 thread
23:09:59 Initializing from normalized Laplacian + noise
23:09:59 Commencing optimization for 500 epochs, with 52240 positive edges
23:10:05 Optimization finished
[1] "35 0.1"23:10:05 UMAP embedding parameters a = 1.577 b = 0.8951
23:10:05 Read 1203 rows and found 38 numeric columns
23:10:05 Using Annoy for neighbor search, n_neighbors = 35
23:10:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:10:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87781c1869
23:10:05 Searching Annoy index using 1 thread, search_k = 3500
23:10:06 Annoy recall = 100%
23:10:08 Commencing smooth kNN distance calibration using 1 thread
23:10:13 Initializing from normalized Laplacian + noise
23:10:13 Commencing optimization for 500 epochs, with 52240 positive edges
23:10:18 Optimization finished
[1] "35 0.11"23:10:19 UMAP embedding parameters a = 1.544 b = 0.9058
23:10:19 Read 1203 rows and found 38 numeric columns
23:10:19 Using Annoy for neighbor search, n_neighbors = 35
23:10:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:10:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ad0cf4
23:10:19 Searching Annoy index using 1 thread, search_k = 3500
23:10:19 Annoy recall = 100%
23:10:22 Commencing smooth kNN distance calibration using 1 thread
23:10:27 Initializing from normalized Laplacian + noise
23:10:27 Commencing optimization for 500 epochs, with 52240 positive edges
23:10:32 Optimization finished
[1] "35 0.12"23:10:32 UMAP embedding parameters a = 1.51 b = 0.9165
23:10:32 Read 1203 rows and found 38 numeric columns
23:10:32 Using Annoy for neighbor search, n_neighbors = 35
23:10:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:10:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873979237a
23:10:33 Searching Annoy index using 1 thread, search_k = 3500
23:10:33 Annoy recall = 100%
23:10:35 Commencing smooth kNN distance calibration using 1 thread
23:10:40 Initializing from normalized Laplacian + noise
23:10:40 Commencing optimization for 500 epochs, with 52240 positive edges
23:10:46 Optimization finished
[1] "35 0.13"23:10:46 UMAP embedding parameters a = 1.478 b = 0.9272
23:10:46 Read 1203 rows and found 38 numeric columns
23:10:46 Using Annoy for neighbor search, n_neighbors = 35
23:10:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:10:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710700c09
23:10:46 Searching Annoy index using 1 thread, search_k = 3500
23:10:46 Annoy recall = 100%
23:10:49 Commencing smooth kNN distance calibration using 1 thread
23:10:54 Initializing from normalized Laplacian + noise
23:10:54 Commencing optimization for 500 epochs, with 52240 positive edges
23:10:59 Optimization finished
[1] "35 0.14"23:10:59 UMAP embedding parameters a = 1.446 b = 0.938
23:10:59 Read 1203 rows and found 38 numeric columns
23:10:59 Using Annoy for neighbor search, n_neighbors = 35
23:10:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:11:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751f85fac
23:11:00 Searching Annoy index using 1 thread, search_k = 3500
23:11:00 Annoy recall = 100%
23:11:03 Commencing smooth kNN distance calibration using 1 thread
23:11:08 Initializing from normalized Laplacian + noise
23:11:08 Commencing optimization for 500 epochs, with 52240 positive edges
23:11:13 Optimization finished
[1] "35 0.15"23:11:13 UMAP embedding parameters a = 1.414 b = 0.9488
23:11:13 Read 1203 rows and found 38 numeric columns
23:11:13 Using Annoy for neighbor search, n_neighbors = 35
23:11:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:11:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871820798d
23:11:13 Searching Annoy index using 1 thread, search_k = 3500
23:11:14 Annoy recall = 100%
23:11:16 Commencing smooth kNN distance calibration using 1 thread
23:11:21 Initializing from normalized Laplacian + noise
23:11:21 Commencing optimization for 500 epochs, with 52240 positive edges
23:11:26 Optimization finished
[1] "35 0.16"23:11:27 UMAP embedding parameters a = 1.383 b = 0.9596
23:11:27 Read 1203 rows and found 38 numeric columns
23:11:27 Using Annoy for neighbor search, n_neighbors = 35
23:11:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:11:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d019fa3
23:11:27 Searching Annoy index using 1 thread, search_k = 3500
23:11:27 Annoy recall = 100%
23:11:30 Commencing smooth kNN distance calibration using 1 thread
23:11:35 Initializing from normalized Laplacian + noise
23:11:35 Commencing optimization for 500 epochs, with 52240 positive edges
23:11:40 Optimization finished
[1] "35 0.17"23:11:40 UMAP embedding parameters a = 1.352 b = 0.9704
23:11:40 Read 1203 rows and found 38 numeric columns
23:11:40 Using Annoy for neighbor search, n_neighbors = 35
23:11:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:11:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729a941a1
23:11:41 Searching Annoy index using 1 thread, search_k = 3500
23:11:41 Annoy recall = 100%
23:11:43 Commencing smooth kNN distance calibration using 1 thread
23:11:49 Initializing from normalized Laplacian + noise
23:11:49 Commencing optimization for 500 epochs, with 52240 positive edges
23:11:54 Optimization finished
[1] "35 0.18"23:11:54 UMAP embedding parameters a = 1.321 b = 0.9813
23:11:54 Read 1203 rows and found 38 numeric columns
23:11:54 Using Annoy for neighbor search, n_neighbors = 35
23:11:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:11:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722749aba
23:11:54 Searching Annoy index using 1 thread, search_k = 3500
23:11:55 Annoy recall = 100%
23:11:57 Commencing smooth kNN distance calibration using 1 thread
23:12:02 Initializing from normalized Laplacian + noise
23:12:02 Commencing optimization for 500 epochs, with 52240 positive edges
23:12:08 Optimization finished
[1] "35 0.19"23:12:08 UMAP embedding parameters a = 1.292 b = 0.9921
23:12:08 Read 1203 rows and found 38 numeric columns
23:12:08 Using Annoy for neighbor search, n_neighbors = 35
23:12:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:12:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e6139d3
23:12:08 Searching Annoy index using 1 thread, search_k = 3500
23:12:08 Annoy recall = 100%
23:12:11 Commencing smooth kNN distance calibration using 1 thread
23:12:16 Initializing from normalized Laplacian + noise
23:12:16 Commencing optimization for 500 epochs, with 52240 positive edges
23:12:21 Optimization finished
[1] "35 0.2"23:12:22 UMAP embedding parameters a = 1.262 b = 1.003
23:12:22 Read 1203 rows and found 38 numeric columns
23:12:22 Using Annoy for neighbor search, n_neighbors = 35
23:12:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:12:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cacffe3
23:12:22 Searching Annoy index using 1 thread, search_k = 3500
23:12:22 Annoy recall = 100%
23:12:25 Commencing smooth kNN distance calibration using 1 thread
23:12:30 Initializing from normalized Laplacian + noise
23:12:30 Commencing optimization for 500 epochs, with 52240 positive edges
23:12:35 Optimization finished
[1] "36 0"23:12:35 UMAP embedding parameters a = 1.933 b = 0.7905
23:12:35 Read 1203 rows and found 38 numeric columns
23:12:35 Using Annoy for neighbor search, n_neighbors = 36
23:12:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:12:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736144bc
23:12:36 Searching Annoy index using 1 thread, search_k = 3600
23:12:36 Annoy recall = 100%
23:12:39 Commencing smooth kNN distance calibration using 1 thread
23:12:44 Initializing from normalized Laplacian + noise
23:12:44 Commencing optimization for 500 epochs, with 53708 positive edges
23:12:49 Optimization finished
[1] "36 0.01"23:12:49 UMAP embedding parameters a = 1.896 b = 0.8006
23:12:49 Read 1203 rows and found 38 numeric columns
23:12:49 Using Annoy for neighbor search, n_neighbors = 36
23:12:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:12:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714f09d4d
23:12:50 Searching Annoy index using 1 thread, search_k = 3600
23:12:50 Annoy recall = 100%
23:12:52 Commencing smooth kNN distance calibration using 1 thread
23:12:57 Initializing from normalized Laplacian + noise
23:12:58 Commencing optimization for 500 epochs, with 53708 positive edges
23:13:03 Optimization finished
[1] "36 0.02"23:13:03 UMAP embedding parameters a = 1.859 b = 0.8109
23:13:03 Read 1203 rows and found 38 numeric columns
23:13:03 Using Annoy for neighbor search, n_neighbors = 36
23:13:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:13:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87666134a5
23:13:03 Searching Annoy index using 1 thread, search_k = 3600
23:13:04 Annoy recall = 100%
23:13:06 Commencing smooth kNN distance calibration using 1 thread
23:13:11 Initializing from normalized Laplacian + noise
23:13:11 Commencing optimization for 500 epochs, with 53708 positive edges
23:13:17 Optimization finished
[1] "36 0.03"23:13:17 UMAP embedding parameters a = 1.822 b = 0.8212
23:13:17 Read 1203 rows and found 38 numeric columns
23:13:17 Using Annoy for neighbor search, n_neighbors = 36
23:13:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:13:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875da52c91
23:13:17 Searching Annoy index using 1 thread, search_k = 3600
23:13:18 Annoy recall = 100%
23:13:20 Commencing smooth kNN distance calibration using 1 thread
23:13:25 Initializing from normalized Laplacian + noise
23:13:25 Commencing optimization for 500 epochs, with 53708 positive edges
23:13:31 Optimization finished
[1] "36 0.04"23:13:31 UMAP embedding parameters a = 1.786 b = 0.8316
23:13:31 Read 1203 rows and found 38 numeric columns
23:13:31 Using Annoy for neighbor search, n_neighbors = 36
23:13:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:13:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736e07ced
23:13:31 Searching Annoy index using 1 thread, search_k = 3600
23:13:31 Annoy recall = 100%
23:13:34 Commencing smooth kNN distance calibration using 1 thread
23:13:39 Initializing from normalized Laplacian + noise
23:13:39 Commencing optimization for 500 epochs, with 53708 positive edges
23:13:45 Optimization finished
[1] "36 0.05"23:13:45 UMAP embedding parameters a = 1.75 b = 0.8421
23:13:45 Read 1203 rows and found 38 numeric columns
23:13:45 Using Annoy for neighbor search, n_neighbors = 36
23:13:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:13:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872575ddf2
23:13:45 Searching Annoy index using 1 thread, search_k = 3600
23:13:46 Annoy recall = 100%
23:13:48 Commencing smooth kNN distance calibration using 1 thread
23:13:53 Initializing from normalized Laplacian + noise
23:13:54 Commencing optimization for 500 epochs, with 53708 positive edges
23:13:59 Optimization finished
[1] "36 0.06"23:13:59 UMAP embedding parameters a = 1.715 b = 0.8526
23:13:59 Read 1203 rows and found 38 numeric columns
23:13:59 Using Annoy for neighbor search, n_neighbors = 36
23:13:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:13:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87171e081d
23:13:59 Searching Annoy index using 1 thread, search_k = 3600
23:14:00 Annoy recall = 100%
23:14:02 Commencing smooth kNN distance calibration using 1 thread
23:14:07 Initializing from normalized Laplacian + noise
23:14:07 Commencing optimization for 500 epochs, with 53708 positive edges
23:14:13 Optimization finished
[1] "36 0.07"23:14:13 UMAP embedding parameters a = 1.68 b = 0.8631
23:14:13 Read 1203 rows and found 38 numeric columns
23:14:13 Using Annoy for neighbor search, n_neighbors = 36
23:14:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:14:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875dc07468
23:14:13 Searching Annoy index using 1 thread, search_k = 3600
23:14:14 Annoy recall = 100%
23:14:16 Commencing smooth kNN distance calibration using 1 thread
23:14:21 Initializing from normalized Laplacian + noise
23:14:21 Commencing optimization for 500 epochs, with 53708 positive edges
23:14:28 Optimization finished
[1] "36 0.08"23:14:28 UMAP embedding parameters a = 1.645 b = 0.8737
23:14:28 Read 1203 rows and found 38 numeric columns
23:14:28 Using Annoy for neighbor search, n_neighbors = 36
23:14:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:14:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871474e5d9
23:14:28 Searching Annoy index using 1 thread, search_k = 3600
23:14:29 Annoy recall = 100%
23:14:32 Commencing smooth kNN distance calibration using 1 thread
23:14:39 Initializing from normalized Laplacian + noise
23:14:39 Commencing optimization for 500 epochs, with 53708 positive edges
23:14:46 Optimization finished
[1] "36 0.09"23:14:46 UMAP embedding parameters a = 1.611 b = 0.8844
23:14:46 Read 1203 rows and found 38 numeric columns
23:14:46 Using Annoy for neighbor search, n_neighbors = 36
23:14:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:14:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ab9964d
23:14:46 Searching Annoy index using 1 thread, search_k = 3600
23:14:47 Annoy recall = 100%
23:14:50 Commencing smooth kNN distance calibration using 1 thread
23:14:56 Initializing from normalized Laplacian + noise
23:14:56 Commencing optimization for 500 epochs, with 53708 positive edges
23:15:02 Optimization finished
[1] "36 0.1"23:15:03 UMAP embedding parameters a = 1.577 b = 0.8951
23:15:03 Read 1203 rows and found 38 numeric columns
23:15:03 Using Annoy for neighbor search, n_neighbors = 36
23:15:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:15:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d1395ea
23:15:03 Searching Annoy index using 1 thread, search_k = 3600
23:15:03 Annoy recall = 100%
23:15:07 Commencing smooth kNN distance calibration using 1 thread
23:15:14 Initializing from normalized Laplacian + noise
23:15:14 Commencing optimization for 500 epochs, with 53708 positive edges
23:15:20 Optimization finished
[1] "36 0.11"23:15:20 UMAP embedding parameters a = 1.544 b = 0.9058
23:15:20 Read 1203 rows and found 38 numeric columns
23:15:20 Using Annoy for neighbor search, n_neighbors = 36
23:15:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:15:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e147c02
23:15:21 Searching Annoy index using 1 thread, search_k = 3600
23:15:21 Annoy recall = 100%
23:15:25 Commencing smooth kNN distance calibration using 1 thread
23:15:32 Initializing from normalized Laplacian + noise
23:15:32 Commencing optimization for 500 epochs, with 53708 positive edges
23:15:38 Optimization finished
[1] "36 0.12"23:15:38 UMAP embedding parameters a = 1.51 b = 0.9165
23:15:38 Read 1203 rows and found 38 numeric columns
23:15:38 Using Annoy for neighbor search, n_neighbors = 36
23:15:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:15:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720064cfd
23:15:39 Searching Annoy index using 1 thread, search_k = 3600
23:15:39 Annoy recall = 100%
23:15:42 Commencing smooth kNN distance calibration using 1 thread
23:15:49 Initializing from normalized Laplacian + noise
23:15:49 Commencing optimization for 500 epochs, with 53708 positive edges
23:15:55 Optimization finished
[1] "36 0.13"23:15:55 UMAP embedding parameters a = 1.478 b = 0.9272
23:15:55 Read 1203 rows and found 38 numeric columns
23:15:55 Using Annoy for neighbor search, n_neighbors = 36
23:15:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:15:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87258a7d04
23:15:55 Searching Annoy index using 1 thread, search_k = 3600
23:15:55 Annoy recall = 100%
23:15:59 Commencing smooth kNN distance calibration using 1 thread
23:16:05 Initializing from normalized Laplacian + noise
23:16:05 Commencing optimization for 500 epochs, with 53708 positive edges
23:16:11 Optimization finished
[1] "36 0.14"23:16:11 UMAP embedding parameters a = 1.446 b = 0.938
23:16:11 Read 1203 rows and found 38 numeric columns
23:16:11 Using Annoy for neighbor search, n_neighbors = 36
23:16:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:16:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c5d0361
23:16:11 Searching Annoy index using 1 thread, search_k = 3600
23:16:11 Annoy recall = 100%
23:16:15 Commencing smooth kNN distance calibration using 1 thread
23:16:21 Initializing from normalized Laplacian + noise
23:16:21 Commencing optimization for 500 epochs, with 53708 positive edges
23:16:29 Optimization finished
[1] "36 0.15"23:16:29 UMAP embedding parameters a = 1.414 b = 0.9488
23:16:29 Read 1203 rows and found 38 numeric columns
23:16:29 Using Annoy for neighbor search, n_neighbors = 36
23:16:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:16:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87377a5699
23:16:29 Searching Annoy index using 1 thread, search_k = 3600
23:16:30 Annoy recall = 100%
23:16:35 Commencing smooth kNN distance calibration using 1 thread
23:16:41 Initializing from normalized Laplacian + noise
23:16:41 Commencing optimization for 500 epochs, with 53708 positive edges
23:16:47 Optimization finished
[1] "36 0.16"23:16:47 UMAP embedding parameters a = 1.383 b = 0.9596
23:16:47 Read 1203 rows and found 38 numeric columns
23:16:47 Using Annoy for neighbor search, n_neighbors = 36
23:16:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:16:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cfd6548
23:16:47 Searching Annoy index using 1 thread, search_k = 3600
23:16:47 Annoy recall = 100%
23:16:50 Commencing smooth kNN distance calibration using 1 thread
23:16:56 Initializing from normalized Laplacian + noise
23:16:56 Commencing optimization for 500 epochs, with 53708 positive edges
23:17:01 Optimization finished
[1] "36 0.17"23:17:02 UMAP embedding parameters a = 1.352 b = 0.9704
23:17:02 Read 1203 rows and found 38 numeric columns
23:17:02 Using Annoy for neighbor search, n_neighbors = 36
23:17:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:17:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a857c0d
23:17:02 Searching Annoy index using 1 thread, search_k = 3600
23:17:02 Annoy recall = 100%
23:17:05 Commencing smooth kNN distance calibration using 1 thread
23:17:10 Initializing from normalized Laplacian + noise
23:17:11 Commencing optimization for 500 epochs, with 53708 positive edges
23:17:16 Optimization finished
[1] "36 0.18"23:17:16 UMAP embedding parameters a = 1.321 b = 0.9813
23:17:16 Read 1203 rows and found 38 numeric columns
23:17:16 Using Annoy for neighbor search, n_neighbors = 36
23:17:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:17:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ff2592f
23:17:16 Searching Annoy index using 1 thread, search_k = 3600
23:17:17 Annoy recall = 100%
23:17:19 Commencing smooth kNN distance calibration using 1 thread
23:17:25 Initializing from normalized Laplacian + noise
23:17:25 Commencing optimization for 500 epochs, with 53708 positive edges
23:17:30 Optimization finished
[1] "36 0.19"23:17:31 UMAP embedding parameters a = 1.292 b = 0.9921
23:17:31 Read 1203 rows and found 38 numeric columns
23:17:31 Using Annoy for neighbor search, n_neighbors = 36
23:17:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:17:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e64f105
23:17:31 Searching Annoy index using 1 thread, search_k = 3600
23:17:31 Annoy recall = 100%
23:17:34 Commencing smooth kNN distance calibration using 1 thread
23:17:39 Initializing from normalized Laplacian + noise
23:17:39 Commencing optimization for 500 epochs, with 53708 positive edges
23:17:45 Optimization finished
[1] "36 0.2"23:17:45 UMAP embedding parameters a = 1.262 b = 1.003
23:17:45 Read 1203 rows and found 38 numeric columns
23:17:45 Using Annoy for neighbor search, n_neighbors = 36
23:17:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:17:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752a19476
23:17:45 Searching Annoy index using 1 thread, search_k = 3600
23:17:46 Annoy recall = 100%
23:17:48 Commencing smooth kNN distance calibration using 1 thread
23:17:54 Initializing from normalized Laplacian + noise
23:17:54 Commencing optimization for 500 epochs, with 53708 positive edges
23:17:59 Optimization finished
[1] "37 0"23:17:59 UMAP embedding parameters a = 1.933 b = 0.7905
23:17:59 Read 1203 rows and found 38 numeric columns
23:17:59 Using Annoy for neighbor search, n_neighbors = 37
23:17:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:18:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729f6623
23:18:00 Searching Annoy index using 1 thread, search_k = 3700
23:18:00 Annoy recall = 100%
23:18:03 Commencing smooth kNN distance calibration using 1 thread
23:18:08 Initializing from normalized Laplacian + noise
23:18:08 Commencing optimization for 500 epochs, with 55164 positive edges
23:18:13 Optimization finished
[1] "37 0.01"23:18:14 UMAP embedding parameters a = 1.896 b = 0.8006
23:18:14 Read 1203 rows and found 38 numeric columns
23:18:14 Using Annoy for neighbor search, n_neighbors = 37
23:18:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:18:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747de1480
23:18:14 Searching Annoy index using 1 thread, search_k = 3700
23:18:14 Annoy recall = 100%
23:18:17 Commencing smooth kNN distance calibration using 1 thread
23:18:22 Initializing from normalized Laplacian + noise
23:18:22 Commencing optimization for 500 epochs, with 55164 positive edges
23:18:28 Optimization finished
[1] "37 0.02"23:18:28 UMAP embedding parameters a = 1.859 b = 0.8109
23:18:28 Read 1203 rows and found 38 numeric columns
23:18:28 Using Annoy for neighbor search, n_neighbors = 37
23:18:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:18:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876311a080
23:18:28 Searching Annoy index using 1 thread, search_k = 3700
23:18:28 Annoy recall = 100%
23:18:31 Commencing smooth kNN distance calibration using 1 thread
23:18:36 Initializing from normalized Laplacian + noise
23:18:36 Commencing optimization for 500 epochs, with 55164 positive edges
23:18:42 Optimization finished
[1] "37 0.03"23:18:42 UMAP embedding parameters a = 1.822 b = 0.8212
23:18:42 Read 1203 rows and found 38 numeric columns
23:18:42 Using Annoy for neighbor search, n_neighbors = 37
23:18:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:18:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875497c5cf
23:18:42 Searching Annoy index using 1 thread, search_k = 3700
23:18:43 Annoy recall = 100%
23:18:45 Commencing smooth kNN distance calibration using 1 thread
23:18:51 Initializing from normalized Laplacian + noise
23:18:51 Commencing optimization for 500 epochs, with 55164 positive edges
23:18:56 Optimization finished
[1] "37 0.04"23:18:56 UMAP embedding parameters a = 1.786 b = 0.8316
23:18:56 Read 1203 rows and found 38 numeric columns
23:18:56 Using Annoy for neighbor search, n_neighbors = 37
23:18:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:18:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ffe8e0d
23:18:57 Searching Annoy index using 1 thread, search_k = 3700
23:18:57 Annoy recall = 100%
23:19:00 Commencing smooth kNN distance calibration using 1 thread
23:19:05 Initializing from normalized Laplacian + noise
23:19:05 Commencing optimization for 500 epochs, with 55164 positive edges
23:19:10 Optimization finished
[1] "37 0.05"23:19:10 UMAP embedding parameters a = 1.75 b = 0.8421
23:19:10 Read 1203 rows and found 38 numeric columns
23:19:10 Using Annoy for neighbor search, n_neighbors = 37
23:19:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:19:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740134023
23:19:11 Searching Annoy index using 1 thread, search_k = 3700
23:19:11 Annoy recall = 100%
23:19:14 Commencing smooth kNN distance calibration using 1 thread
23:19:19 Initializing from normalized Laplacian + noise
23:19:19 Commencing optimization for 500 epochs, with 55164 positive edges
23:19:24 Optimization finished
[1] "37 0.06"23:19:25 UMAP embedding parameters a = 1.715 b = 0.8526
23:19:25 Read 1203 rows and found 38 numeric columns
23:19:25 Using Annoy for neighbor search, n_neighbors = 37
23:19:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:19:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e410771
23:19:25 Searching Annoy index using 1 thread, search_k = 3700
23:19:25 Annoy recall = 100%
23:19:28 Commencing smooth kNN distance calibration using 1 thread
23:19:33 Initializing from normalized Laplacian + noise
23:19:33 Commencing optimization for 500 epochs, with 55164 positive edges
23:19:39 Optimization finished
[1] "37 0.07"23:19:39 UMAP embedding parameters a = 1.68 b = 0.8631
23:19:39 Read 1203 rows and found 38 numeric columns
23:19:39 Using Annoy for neighbor search, n_neighbors = 37
23:19:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:19:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727328c7
23:19:39 Searching Annoy index using 1 thread, search_k = 3700
23:19:40 Annoy recall = 100%
23:19:42 Commencing smooth kNN distance calibration using 1 thread
23:19:47 Initializing from normalized Laplacian + noise
23:19:47 Commencing optimization for 500 epochs, with 55164 positive edges
23:19:53 Optimization finished
[1] "37 0.08"23:19:53 UMAP embedding parameters a = 1.645 b = 0.8737
23:19:53 Read 1203 rows and found 38 numeric columns
23:19:53 Using Annoy for neighbor search, n_neighbors = 37
23:19:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:19:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e7479f6
23:19:54 Searching Annoy index using 1 thread, search_k = 3700
23:19:54 Annoy recall = 100%
23:19:56 Commencing smooth kNN distance calibration using 1 thread
23:20:02 Initializing from normalized Laplacian + noise
23:20:02 Commencing optimization for 500 epochs, with 55164 positive edges
23:20:07 Optimization finished
[1] "37 0.09"23:20:07 UMAP embedding parameters a = 1.611 b = 0.8844
23:20:07 Read 1203 rows and found 38 numeric columns
23:20:07 Using Annoy for neighbor search, n_neighbors = 37
23:20:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:20:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875aee0754
23:20:08 Searching Annoy index using 1 thread, search_k = 3700
23:20:08 Annoy recall = 100%
23:20:11 Commencing smooth kNN distance calibration using 1 thread
23:20:16 Initializing from normalized Laplacian + noise
23:20:16 Commencing optimization for 500 epochs, with 55164 positive edges
23:20:21 Optimization finished
[1] "37 0.1"23:20:22 UMAP embedding parameters a = 1.577 b = 0.8951
23:20:22 Read 1203 rows and found 38 numeric columns
23:20:22 Using Annoy for neighbor search, n_neighbors = 37
23:20:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:20:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d46d84
23:20:22 Searching Annoy index using 1 thread, search_k = 3700
23:20:22 Annoy recall = 100%
23:20:25 Commencing smooth kNN distance calibration using 1 thread
23:20:30 Initializing from normalized Laplacian + noise
23:20:30 Commencing optimization for 500 epochs, with 55164 positive edges
23:20:36 Optimization finished
[1] "37 0.11"23:20:36 UMAP embedding parameters a = 1.544 b = 0.9058
23:20:36 Read 1203 rows and found 38 numeric columns
23:20:36 Using Annoy for neighbor search, n_neighbors = 37
23:20:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:20:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773651744
23:20:36 Searching Annoy index using 1 thread, search_k = 3700
23:20:37 Annoy recall = 100%
23:20:39 Commencing smooth kNN distance calibration using 1 thread
23:20:45 Initializing from normalized Laplacian + noise
23:20:45 Commencing optimization for 500 epochs, with 55164 positive edges
23:20:50 Optimization finished
[1] "37 0.12"23:20:50 UMAP embedding parameters a = 1.51 b = 0.9165
23:20:50 Read 1203 rows and found 38 numeric columns
23:20:50 Using Annoy for neighbor search, n_neighbors = 37
23:20:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:20:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87414f3bf9
23:20:51 Searching Annoy index using 1 thread, search_k = 3700
23:20:51 Annoy recall = 100%
23:20:54 Commencing smooth kNN distance calibration using 1 thread
23:20:59 Initializing from normalized Laplacian + noise
23:20:59 Commencing optimization for 500 epochs, with 55164 positive edges
23:21:04 Optimization finished
[1] "37 0.13"23:21:05 UMAP embedding parameters a = 1.478 b = 0.9272
23:21:05 Read 1203 rows and found 38 numeric columns
23:21:05 Using Annoy for neighbor search, n_neighbors = 37
23:21:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:21:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763799a15
23:21:05 Searching Annoy index using 1 thread, search_k = 3700
23:21:05 Annoy recall = 100%
23:21:08 Commencing smooth kNN distance calibration using 1 thread
23:21:13 Initializing from normalized Laplacian + noise
23:21:13 Commencing optimization for 500 epochs, with 55164 positive edges
23:21:19 Optimization finished
[1] "37 0.14"23:21:19 UMAP embedding parameters a = 1.446 b = 0.938
23:21:19 Read 1203 rows and found 38 numeric columns
23:21:19 Using Annoy for neighbor search, n_neighbors = 37
23:21:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:21:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a459431
23:21:19 Searching Annoy index using 1 thread, search_k = 3700
23:21:19 Annoy recall = 100%
23:21:22 Commencing smooth kNN distance calibration using 1 thread
23:21:27 Initializing from normalized Laplacian + noise
23:21:28 Commencing optimization for 500 epochs, with 55164 positive edges
23:21:33 Optimization finished
[1] "37 0.15"23:21:33 UMAP embedding parameters a = 1.414 b = 0.9488
23:21:33 Read 1203 rows and found 38 numeric columns
23:21:33 Using Annoy for neighbor search, n_neighbors = 37
23:21:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:21:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766c519eb
23:21:33 Searching Annoy index using 1 thread, search_k = 3700
23:21:34 Annoy recall = 100%
23:21:36 Commencing smooth kNN distance calibration using 1 thread
23:21:42 Initializing from normalized Laplacian + noise
23:21:42 Commencing optimization for 500 epochs, with 55164 positive edges
23:21:47 Optimization finished
[1] "37 0.16"23:21:48 UMAP embedding parameters a = 1.383 b = 0.9596
23:21:48 Read 1203 rows and found 38 numeric columns
23:21:48 Using Annoy for neighbor search, n_neighbors = 37
23:21:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:21:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a97a232
23:21:48 Searching Annoy index using 1 thread, search_k = 3700
23:21:48 Annoy recall = 100%
23:21:51 Commencing smooth kNN distance calibration using 1 thread
23:21:56 Initializing from normalized Laplacian + noise
23:21:56 Commencing optimization for 500 epochs, with 55164 positive edges
23:22:02 Optimization finished
[1] "37 0.17"23:22:02 UMAP embedding parameters a = 1.352 b = 0.9704
23:22:02 Read 1203 rows and found 38 numeric columns
23:22:02 Using Annoy for neighbor search, n_neighbors = 37
23:22:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:22:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878060899
23:22:02 Searching Annoy index using 1 thread, search_k = 3700
23:22:03 Annoy recall = 100%
23:22:05 Commencing smooth kNN distance calibration using 1 thread
23:22:11 Initializing from normalized Laplacian + noise
23:22:11 Commencing optimization for 500 epochs, with 55164 positive edges
23:22:16 Optimization finished
[1] "37 0.18"23:22:16 UMAP embedding parameters a = 1.321 b = 0.9813
23:22:16 Read 1203 rows and found 38 numeric columns
23:22:16 Using Annoy for neighbor search, n_neighbors = 37
23:22:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:22:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b39ffc4
23:22:17 Searching Annoy index using 1 thread, search_k = 3700
23:22:17 Annoy recall = 100%
23:22:20 Commencing smooth kNN distance calibration using 1 thread
23:22:25 Initializing from normalized Laplacian + noise
23:22:25 Commencing optimization for 500 epochs, with 55164 positive edges
23:22:30 Optimization finished
[1] "37 0.19"23:22:31 UMAP embedding parameters a = 1.292 b = 0.9921
23:22:31 Read 1203 rows and found 38 numeric columns
23:22:31 Using Annoy for neighbor search, n_neighbors = 37
23:22:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:22:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873551387f
23:22:31 Searching Annoy index using 1 thread, search_k = 3700
23:22:31 Annoy recall = 100%
23:22:34 Commencing smooth kNN distance calibration using 1 thread
23:22:39 Initializing from normalized Laplacian + noise
23:22:39 Commencing optimization for 500 epochs, with 55164 positive edges
23:22:45 Optimization finished
[1] "37 0.2"23:22:45 UMAP embedding parameters a = 1.262 b = 1.003
23:22:45 Read 1203 rows and found 38 numeric columns
23:22:45 Using Annoy for neighbor search, n_neighbors = 37
23:22:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:22:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725199e83
23:22:45 Searching Annoy index using 1 thread, search_k = 3700
23:22:46 Annoy recall = 100%
23:22:48 Commencing smooth kNN distance calibration using 1 thread
23:22:54 Initializing from normalized Laplacian + noise
23:22:54 Commencing optimization for 500 epochs, with 55164 positive edges
23:22:59 Optimization finished
[1] "38 0"23:22:59 UMAP embedding parameters a = 1.933 b = 0.7905
23:22:59 Read 1203 rows and found 38 numeric columns
23:22:59 Using Annoy for neighbor search, n_neighbors = 38
23:22:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:23:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87494e7bc7
23:23:00 Searching Annoy index using 1 thread, search_k = 3800
23:23:00 Annoy recall = 100%
23:23:03 Commencing smooth kNN distance calibration using 1 thread
23:23:08 Initializing from normalized Laplacian + noise
23:23:08 Commencing optimization for 500 epochs, with 56552 positive edges
23:23:14 Optimization finished
[1] "38 0.01"23:23:14 UMAP embedding parameters a = 1.896 b = 0.8006
23:23:14 Read 1203 rows and found 38 numeric columns
23:23:14 Using Annoy for neighbor search, n_neighbors = 38
23:23:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:23:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875557857c
23:23:14 Searching Annoy index using 1 thread, search_k = 3800
23:23:15 Annoy recall = 100%
23:23:17 Commencing smooth kNN distance calibration using 1 thread
23:23:23 Initializing from normalized Laplacian + noise
23:23:23 Commencing optimization for 500 epochs, with 56552 positive edges
23:23:28 Optimization finished
[1] "38 0.02"23:23:28 UMAP embedding parameters a = 1.859 b = 0.8109
23:23:28 Read 1203 rows and found 38 numeric columns
23:23:28 Using Annoy for neighbor search, n_neighbors = 38
23:23:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:23:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874aa41b87
23:23:29 Searching Annoy index using 1 thread, search_k = 3800
23:23:29 Annoy recall = 100%
23:23:32 Commencing smooth kNN distance calibration using 1 thread
23:23:37 Initializing from normalized Laplacian + noise
23:23:37 Commencing optimization for 500 epochs, with 56552 positive edges
23:23:43 Optimization finished
[1] "38 0.03"23:23:43 UMAP embedding parameters a = 1.822 b = 0.8212
23:23:43 Read 1203 rows and found 38 numeric columns
23:23:43 Using Annoy for neighbor search, n_neighbors = 38
23:23:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:23:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725ab7f28
23:23:43 Searching Annoy index using 1 thread, search_k = 3800
23:23:43 Annoy recall = 100%
23:23:46 Commencing smooth kNN distance calibration using 1 thread
23:23:52 Initializing from normalized Laplacian + noise
23:23:52 Commencing optimization for 500 epochs, with 56552 positive edges
23:23:57 Optimization finished
[1] "38 0.04"23:23:57 UMAP embedding parameters a = 1.786 b = 0.8316
23:23:57 Read 1203 rows and found 38 numeric columns
23:23:57 Using Annoy for neighbor search, n_neighbors = 38
23:23:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:23:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cd1dc16
23:23:58 Searching Annoy index using 1 thread, search_k = 3800
23:23:58 Annoy recall = 100%
23:24:01 Commencing smooth kNN distance calibration using 1 thread
23:24:06 Initializing from normalized Laplacian + noise
23:24:06 Commencing optimization for 500 epochs, with 56552 positive edges
23:24:12 Optimization finished
[1] "38 0.05"23:24:12 UMAP embedding parameters a = 1.75 b = 0.8421
23:24:12 Read 1203 rows and found 38 numeric columns
23:24:12 Using Annoy for neighbor search, n_neighbors = 38
23:24:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:24:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727a180cf
23:24:12 Searching Annoy index using 1 thread, search_k = 3800
23:24:12 Annoy recall = 100%
23:24:15 Commencing smooth kNN distance calibration using 1 thread
23:24:21 Initializing from normalized Laplacian + noise
23:24:21 Commencing optimization for 500 epochs, with 56552 positive edges
23:24:26 Optimization finished
[1] "38 0.06"23:24:26 UMAP embedding parameters a = 1.715 b = 0.8526
23:24:26 Read 1203 rows and found 38 numeric columns
23:24:26 Using Annoy for neighbor search, n_neighbors = 38
23:24:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:24:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730fb35
23:24:27 Searching Annoy index using 1 thread, search_k = 3800
23:24:27 Annoy recall = 100%
23:24:30 Commencing smooth kNN distance calibration using 1 thread
23:24:35 Initializing from normalized Laplacian + noise
23:24:35 Commencing optimization for 500 epochs, with 56552 positive edges
23:24:41 Optimization finished
[1] "38 0.07"23:24:41 UMAP embedding parameters a = 1.68 b = 0.8631
23:24:41 Read 1203 rows and found 38 numeric columns
23:24:41 Using Annoy for neighbor search, n_neighbors = 38
23:24:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:24:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cc43545
23:24:41 Searching Annoy index using 1 thread, search_k = 3800
23:24:42 Annoy recall = 100%
23:24:44 Commencing smooth kNN distance calibration using 1 thread
23:24:50 Initializing from normalized Laplacian + noise
23:24:50 Commencing optimization for 500 epochs, with 56552 positive edges
23:24:55 Optimization finished
[1] "38 0.08"23:24:56 UMAP embedding parameters a = 1.645 b = 0.8737
23:24:56 Read 1203 rows and found 38 numeric columns
23:24:56 Using Annoy for neighbor search, n_neighbors = 38
23:24:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:24:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87360671d4
23:24:56 Searching Annoy index using 1 thread, search_k = 3800
23:24:56 Annoy recall = 100%
23:24:59 Commencing smooth kNN distance calibration using 1 thread
23:25:04 Initializing from normalized Laplacian + noise
23:25:04 Commencing optimization for 500 epochs, with 56552 positive edges
23:25:10 Optimization finished
[1] "38 0.09"23:25:10 UMAP embedding parameters a = 1.611 b = 0.8844
23:25:10 Read 1203 rows and found 38 numeric columns
23:25:10 Using Annoy for neighbor search, n_neighbors = 38
23:25:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:25:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752d28fac
23:25:10 Searching Annoy index using 1 thread, search_k = 3800
23:25:11 Annoy recall = 100%
23:25:13 Commencing smooth kNN distance calibration using 1 thread
23:25:19 Initializing from normalized Laplacian + noise
23:25:19 Commencing optimization for 500 epochs, with 56552 positive edges
23:25:24 Optimization finished
[1] "38 0.1"23:25:25 UMAP embedding parameters a = 1.577 b = 0.8951
23:25:25 Read 1203 rows and found 38 numeric columns
23:25:25 Using Annoy for neighbor search, n_neighbors = 38
23:25:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:25:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f639b68
23:25:25 Searching Annoy index using 1 thread, search_k = 3800
23:25:25 Annoy recall = 100%
23:25:28 Commencing smooth kNN distance calibration using 1 thread
23:25:33 Initializing from normalized Laplacian + noise
23:25:33 Commencing optimization for 500 epochs, with 56552 positive edges
23:25:39 Optimization finished
[1] "38 0.11"23:25:39 UMAP embedding parameters a = 1.544 b = 0.9058
23:25:39 Read 1203 rows and found 38 numeric columns
23:25:39 Using Annoy for neighbor search, n_neighbors = 38
23:25:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:25:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877de48654
23:25:39 Searching Annoy index using 1 thread, search_k = 3800
23:25:40 Annoy recall = 100%
23:25:43 Commencing smooth kNN distance calibration using 1 thread
23:25:48 Initializing from normalized Laplacian + noise
23:25:48 Commencing optimization for 500 epochs, with 56552 positive edges
23:25:54 Optimization finished
[1] "38 0.12"23:25:54 UMAP embedding parameters a = 1.51 b = 0.9165
23:25:54 Read 1203 rows and found 38 numeric columns
23:25:54 Using Annoy for neighbor search, n_neighbors = 38
23:25:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:25:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735e4302c
23:25:54 Searching Annoy index using 1 thread, search_k = 3800
23:25:54 Annoy recall = 100%
23:25:57 Commencing smooth kNN distance calibration using 1 thread
23:26:03 Initializing from normalized Laplacian + noise
23:26:03 Commencing optimization for 500 epochs, with 56552 positive edges
23:26:08 Optimization finished
[1] "38 0.13"23:26:08 UMAP embedding parameters a = 1.478 b = 0.9272
23:26:08 Read 1203 rows and found 38 numeric columns
23:26:08 Using Annoy for neighbor search, n_neighbors = 38
23:26:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:26:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763fb6138
23:26:09 Searching Annoy index using 1 thread, search_k = 3800
23:26:09 Annoy recall = 100%
23:26:12 Commencing smooth kNN distance calibration using 1 thread
23:26:17 Initializing from normalized Laplacian + noise
23:26:17 Commencing optimization for 500 epochs, with 56552 positive edges
23:26:23 Optimization finished
[1] "38 0.14"23:26:23 UMAP embedding parameters a = 1.446 b = 0.938
23:26:23 Read 1203 rows and found 38 numeric columns
23:26:23 Using Annoy for neighbor search, n_neighbors = 38
23:26:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:26:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875de31461
23:26:23 Searching Annoy index using 1 thread, search_k = 3800
23:26:24 Annoy recall = 100%
23:26:26 Commencing smooth kNN distance calibration using 1 thread
23:26:32 Initializing from normalized Laplacian + noise
23:26:32 Commencing optimization for 500 epochs, with 56552 positive edges
23:26:37 Optimization finished
[1] "38 0.15"23:26:38 UMAP embedding parameters a = 1.414 b = 0.9488
23:26:38 Read 1203 rows and found 38 numeric columns
23:26:38 Using Annoy for neighbor search, n_neighbors = 38
23:26:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:26:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775f7704f
23:26:38 Searching Annoy index using 1 thread, search_k = 3800
23:26:38 Annoy recall = 100%
23:26:41 Commencing smooth kNN distance calibration using 1 thread
23:26:47 Initializing from normalized Laplacian + noise
23:26:47 Commencing optimization for 500 epochs, with 56552 positive edges
23:26:52 Optimization finished
[1] "38 0.16"23:26:52 UMAP embedding parameters a = 1.383 b = 0.9596
23:26:52 Read 1203 rows and found 38 numeric columns
23:26:52 Using Annoy for neighbor search, n_neighbors = 38
23:26:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:26:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87623c68a9
23:26:53 Searching Annoy index using 1 thread, search_k = 3800
23:26:53 Annoy recall = 100%
23:26:56 Commencing smooth kNN distance calibration using 1 thread
23:27:01 Initializing from normalized Laplacian + noise
23:27:01 Commencing optimization for 500 epochs, with 56552 positive edges
23:27:07 Optimization finished
[1] "38 0.17"23:27:07 UMAP embedding parameters a = 1.352 b = 0.9704
23:27:07 Read 1203 rows and found 38 numeric columns
23:27:07 Using Annoy for neighbor search, n_neighbors = 38
23:27:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:27:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760563d29
23:27:07 Searching Annoy index using 1 thread, search_k = 3800
23:27:08 Annoy recall = 100%
23:27:10 Commencing smooth kNN distance calibration using 1 thread
23:27:16 Initializing from normalized Laplacian + noise
23:27:16 Commencing optimization for 500 epochs, with 56552 positive edges
23:27:21 Optimization finished
[1] "38 0.18"23:27:22 UMAP embedding parameters a = 1.321 b = 0.9813
23:27:22 Read 1203 rows and found 38 numeric columns
23:27:22 Using Annoy for neighbor search, n_neighbors = 38
23:27:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:27:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87546bea46
23:27:22 Searching Annoy index using 1 thread, search_k = 3800
23:27:22 Annoy recall = 100%
23:27:25 Commencing smooth kNN distance calibration using 1 thread
23:27:30 Initializing from normalized Laplacian + noise
23:27:30 Commencing optimization for 500 epochs, with 56552 positive edges
23:27:36 Optimization finished
[1] "38 0.19"23:27:36 UMAP embedding parameters a = 1.292 b = 0.9921
23:27:36 Read 1203 rows and found 38 numeric columns
23:27:36 Using Annoy for neighbor search, n_neighbors = 38
23:27:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:27:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d2a6ffd
23:27:37 Searching Annoy index using 1 thread, search_k = 3800
23:27:37 Annoy recall = 100%
23:27:40 Commencing smooth kNN distance calibration using 1 thread
23:27:45 Initializing from normalized Laplacian + noise
23:27:45 Commencing optimization for 500 epochs, with 56552 positive edges
23:27:51 Optimization finished
[1] "38 0.2"23:27:51 UMAP embedding parameters a = 1.262 b = 1.003
23:27:51 Read 1203 rows and found 38 numeric columns
23:27:51 Using Annoy for neighbor search, n_neighbors = 38
23:27:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:27:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87662aaaad
23:27:51 Searching Annoy index using 1 thread, search_k = 3800
23:27:52 Annoy recall = 100%
23:27:54 Commencing smooth kNN distance calibration using 1 thread
23:28:00 Initializing from normalized Laplacian + noise
23:28:00 Commencing optimization for 500 epochs, with 56552 positive edges
23:28:06 Optimization finished
[1] "39 0"23:28:06 UMAP embedding parameters a = 1.933 b = 0.7905
23:28:06 Read 1203 rows and found 38 numeric columns
23:28:06 Using Annoy for neighbor search, n_neighbors = 39
23:28:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:28:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747d1018a
23:28:06 Searching Annoy index using 1 thread, search_k = 3900
23:28:06 Annoy recall = 100%
23:28:09 Commencing smooth kNN distance calibration using 1 thread
23:28:15 Initializing from normalized Laplacian + noise
23:28:15 Commencing optimization for 500 epochs, with 57926 positive edges
23:28:20 Optimization finished
[1] "39 0.01"23:28:20 UMAP embedding parameters a = 1.896 b = 0.8006
23:28:20 Read 1203 rows and found 38 numeric columns
23:28:20 Using Annoy for neighbor search, n_neighbors = 39
23:28:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:28:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e79abf6
23:28:21 Searching Annoy index using 1 thread, search_k = 3900
23:28:21 Annoy recall = 100%
23:28:24 Commencing smooth kNN distance calibration using 1 thread
23:28:29 Initializing from normalized Laplacian + noise
23:28:29 Commencing optimization for 500 epochs, with 57926 positive edges
23:28:35 Optimization finished
[1] "39 0.02"23:28:35 UMAP embedding parameters a = 1.859 b = 0.8109
23:28:35 Read 1203 rows and found 38 numeric columns
23:28:35 Using Annoy for neighbor search, n_neighbors = 39
23:28:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:28:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749a444c2
23:28:35 Searching Annoy index using 1 thread, search_k = 3900
23:28:36 Annoy recall = 100%
23:28:38 Commencing smooth kNN distance calibration using 1 thread
23:28:44 Initializing from normalized Laplacian + noise
23:28:44 Commencing optimization for 500 epochs, with 57926 positive edges
23:28:50 Optimization finished
[1] "39 0.03"23:28:50 UMAP embedding parameters a = 1.822 b = 0.8212
23:28:50 Read 1203 rows and found 38 numeric columns
23:28:50 Using Annoy for neighbor search, n_neighbors = 39
23:28:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:28:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87721695bb
23:28:50 Searching Annoy index using 1 thread, search_k = 3900
23:28:50 Annoy recall = 100%
23:28:53 Commencing smooth kNN distance calibration using 1 thread
23:28:59 Initializing from normalized Laplacian + noise
23:28:59 Commencing optimization for 500 epochs, with 57926 positive edges
23:29:04 Optimization finished
[1] "39 0.04"23:29:05 UMAP embedding parameters a = 1.786 b = 0.8316
23:29:05 Read 1203 rows and found 38 numeric columns
23:29:05 Using Annoy for neighbor search, n_neighbors = 39
23:29:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:29:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87653ec5e1
23:29:05 Searching Annoy index using 1 thread, search_k = 3900
23:29:05 Annoy recall = 100%
23:29:08 Commencing smooth kNN distance calibration using 1 thread
23:29:13 Initializing from normalized Laplacian + noise
23:29:14 Commencing optimization for 500 epochs, with 57926 positive edges
23:29:19 Optimization finished
[1] "39 0.05"23:29:19 UMAP embedding parameters a = 1.75 b = 0.8421
23:29:19 Read 1203 rows and found 38 numeric columns
23:29:19 Using Annoy for neighbor search, n_neighbors = 39
23:29:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:29:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87443be6f4
23:29:20 Searching Annoy index using 1 thread, search_k = 3900
23:29:20 Annoy recall = 100%
23:29:23 Commencing smooth kNN distance calibration using 1 thread
23:29:28 Initializing from normalized Laplacian + noise
23:29:28 Commencing optimization for 500 epochs, with 57926 positive edges
23:29:34 Optimization finished
[1] "39 0.06"23:29:34 UMAP embedding parameters a = 1.715 b = 0.8526
23:29:34 Read 1203 rows and found 38 numeric columns
23:29:34 Using Annoy for neighbor search, n_neighbors = 39
23:29:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:29:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a1c9e54
23:29:34 Searching Annoy index using 1 thread, search_k = 3900
23:29:35 Annoy recall = 100%
23:29:37 Commencing smooth kNN distance calibration using 1 thread
23:29:43 Initializing from normalized Laplacian + noise
23:29:43 Commencing optimization for 500 epochs, with 57926 positive edges
23:29:49 Optimization finished
[1] "39 0.07"23:29:49 UMAP embedding parameters a = 1.68 b = 0.8631
23:29:49 Read 1203 rows and found 38 numeric columns
23:29:49 Using Annoy for neighbor search, n_neighbors = 39
23:29:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:29:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876078c5a6
23:29:49 Searching Annoy index using 1 thread, search_k = 3900
23:29:49 Annoy recall = 100%
23:29:52 Commencing smooth kNN distance calibration using 1 thread
23:29:58 Initializing from normalized Laplacian + noise
23:29:58 Commencing optimization for 500 epochs, with 57926 positive edges
23:30:03 Optimization finished
[1] "39 0.08"23:30:04 UMAP embedding parameters a = 1.645 b = 0.8737
23:30:04 Read 1203 rows and found 38 numeric columns
23:30:04 Using Annoy for neighbor search, n_neighbors = 39
23:30:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:30:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87798d1f73
23:30:04 Searching Annoy index using 1 thread, search_k = 3900
23:30:04 Annoy recall = 100%
23:30:07 Commencing smooth kNN distance calibration using 1 thread
23:30:13 Initializing from normalized Laplacian + noise
23:30:13 Commencing optimization for 500 epochs, with 57926 positive edges
23:30:18 Optimization finished
[1] "39 0.09"23:30:18 UMAP embedding parameters a = 1.611 b = 0.8844
23:30:18 Read 1203 rows and found 38 numeric columns
23:30:18 Using Annoy for neighbor search, n_neighbors = 39
23:30:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:30:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f363cd7
23:30:19 Searching Annoy index using 1 thread, search_k = 3900
23:30:19 Annoy recall = 100%
23:30:22 Commencing smooth kNN distance calibration using 1 thread
23:30:27 Initializing from normalized Laplacian + noise
23:30:27 Commencing optimization for 500 epochs, with 57926 positive edges
23:30:33 Optimization finished
[1] "39 0.1"23:30:33 UMAP embedding parameters a = 1.577 b = 0.8951
23:30:33 Read 1203 rows and found 38 numeric columns
23:30:33 Using Annoy for neighbor search, n_neighbors = 39
23:30:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:30:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729c7416d
23:30:34 Searching Annoy index using 1 thread, search_k = 3900
23:30:34 Annoy recall = 100%
23:30:37 Commencing smooth kNN distance calibration using 1 thread
23:30:42 Initializing from normalized Laplacian + noise
23:30:42 Commencing optimization for 500 epochs, with 57926 positive edges
23:30:48 Optimization finished
[1] "39 0.11"23:30:48 UMAP embedding parameters a = 1.544 b = 0.9058
23:30:48 Read 1203 rows and found 38 numeric columns
23:30:48 Using Annoy for neighbor search, n_neighbors = 39
23:30:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:30:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ee4a4f0
23:30:48 Searching Annoy index using 1 thread, search_k = 3900
23:30:49 Annoy recall = 100%
23:30:51 Commencing smooth kNN distance calibration using 1 thread
23:30:57 Initializing from normalized Laplacian + noise
23:30:57 Commencing optimization for 500 epochs, with 57926 positive edges
23:31:03 Optimization finished
[1] "39 0.12"23:31:03 UMAP embedding parameters a = 1.51 b = 0.9165
23:31:03 Read 1203 rows and found 38 numeric columns
23:31:03 Using Annoy for neighbor search, n_neighbors = 39
23:31:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:31:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769da585e
23:31:03 Searching Annoy index using 1 thread, search_k = 3900
23:31:04 Annoy recall = 100%
23:31:06 Commencing smooth kNN distance calibration using 1 thread
23:31:12 Initializing from normalized Laplacian + noise
23:31:12 Commencing optimization for 500 epochs, with 57926 positive edges
23:31:18 Optimization finished
[1] "39 0.13"23:31:18 UMAP embedding parameters a = 1.478 b = 0.9272
23:31:18 Read 1203 rows and found 38 numeric columns
23:31:18 Using Annoy for neighbor search, n_neighbors = 39
23:31:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:31:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f72c095
23:31:18 Searching Annoy index using 1 thread, search_k = 3900
23:31:18 Annoy recall = 100%
23:31:21 Commencing smooth kNN distance calibration using 1 thread
23:31:27 Initializing from normalized Laplacian + noise
23:31:27 Commencing optimization for 500 epochs, with 57926 positive edges
23:31:32 Optimization finished
[1] "39 0.14"23:31:33 UMAP embedding parameters a = 1.446 b = 0.938
23:31:33 Read 1203 rows and found 38 numeric columns
23:31:33 Using Annoy for neighbor search, n_neighbors = 39
23:31:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:31:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bb68106
23:31:33 Searching Annoy index using 1 thread, search_k = 3900
23:31:33 Annoy recall = 100%
23:31:36 Commencing smooth kNN distance calibration using 1 thread
23:31:42 Initializing from normalized Laplacian + noise
23:31:42 Commencing optimization for 500 epochs, with 57926 positive edges
23:31:47 Optimization finished
[1] "39 0.15"23:31:48 UMAP embedding parameters a = 1.414 b = 0.9488
23:31:48 Read 1203 rows and found 38 numeric columns
23:31:48 Using Annoy for neighbor search, n_neighbors = 39
23:31:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:31:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87117bd92d
23:31:48 Searching Annoy index using 1 thread, search_k = 3900
23:31:48 Annoy recall = 100%
23:31:51 Commencing smooth kNN distance calibration using 1 thread
23:31:57 Initializing from normalized Laplacian + noise
23:31:57 Commencing optimization for 500 epochs, with 57926 positive edges
23:32:02 Optimization finished
[1] "39 0.16"23:32:02 UMAP embedding parameters a = 1.383 b = 0.9596
23:32:02 Read 1203 rows and found 38 numeric columns
23:32:02 Using Annoy for neighbor search, n_neighbors = 39
23:32:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:32:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fa3bbcb
23:32:03 Searching Annoy index using 1 thread, search_k = 3900
23:32:03 Annoy recall = 100%
23:32:06 Commencing smooth kNN distance calibration using 1 thread
23:32:12 Initializing from normalized Laplacian + noise
23:32:12 Commencing optimization for 500 epochs, with 57926 positive edges
23:32:17 Optimization finished
[1] "39 0.17"23:32:17 UMAP embedding parameters a = 1.352 b = 0.9704
23:32:17 Read 1203 rows and found 38 numeric columns
23:32:17 Using Annoy for neighbor search, n_neighbors = 39
23:32:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:32:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87687ab64b
23:32:18 Searching Annoy index using 1 thread, search_k = 3900
23:32:18 Annoy recall = 100%
23:32:21 Commencing smooth kNN distance calibration using 1 thread
23:32:26 Initializing from normalized Laplacian + noise
23:32:26 Commencing optimization for 500 epochs, with 57926 positive edges
23:32:32 Optimization finished
[1] "39 0.18"23:32:32 UMAP embedding parameters a = 1.321 b = 0.9813
23:32:32 Read 1203 rows and found 38 numeric columns
23:32:32 Using Annoy for neighbor search, n_neighbors = 39
23:32:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:32:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747824b01
23:32:33 Searching Annoy index using 1 thread, search_k = 3900
23:32:33 Annoy recall = 100%
23:32:36 Commencing smooth kNN distance calibration using 1 thread
23:32:41 Initializing from normalized Laplacian + noise
23:32:41 Commencing optimization for 500 epochs, with 57926 positive edges
23:32:47 Optimization finished
[1] "39 0.19"23:32:47 UMAP embedding parameters a = 1.292 b = 0.9921
23:32:47 Read 1203 rows and found 38 numeric columns
23:32:47 Using Annoy for neighbor search, n_neighbors = 39
23:32:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:32:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722764b77
23:32:47 Searching Annoy index using 1 thread, search_k = 3900
23:32:48 Annoy recall = 100%
23:32:51 Commencing smooth kNN distance calibration using 1 thread
23:32:56 Initializing from normalized Laplacian + noise
23:32:56 Commencing optimization for 500 epochs, with 57926 positive edges
23:33:02 Optimization finished
[1] "39 0.2"23:33:02 UMAP embedding parameters a = 1.262 b = 1.003
23:33:02 Read 1203 rows and found 38 numeric columns
23:33:02 Using Annoy for neighbor search, n_neighbors = 39
23:33:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:33:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777de51b3
23:33:02 Searching Annoy index using 1 thread, search_k = 3900
23:33:03 Annoy recall = 100%
23:33:06 Commencing smooth kNN distance calibration using 1 thread
23:33:11 Initializing from normalized Laplacian + noise
23:33:11 Commencing optimization for 500 epochs, with 57926 positive edges
23:33:17 Optimization finished
[1] "40 0"23:33:17 UMAP embedding parameters a = 1.933 b = 0.7905
23:33:17 Read 1203 rows and found 38 numeric columns
23:33:17 Using Annoy for neighbor search, n_neighbors = 40
23:33:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:33:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874566d156
23:33:17 Searching Annoy index using 1 thread, search_k = 4000
23:33:18 Annoy recall = 100%
23:33:21 Commencing smooth kNN distance calibration using 1 thread
23:33:26 Initializing from normalized Laplacian + noise
23:33:26 Commencing optimization for 500 epochs, with 59332 positive edges
23:33:32 Optimization finished
[1] "40 0.01"23:33:32 UMAP embedding parameters a = 1.896 b = 0.8006
23:33:32 Read 1203 rows and found 38 numeric columns
23:33:32 Using Annoy for neighbor search, n_neighbors = 40
23:33:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:33:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87585a7ba3
23:33:32 Searching Annoy index using 1 thread, search_k = 4000
23:33:33 Annoy recall = 100%
23:33:36 Commencing smooth kNN distance calibration using 1 thread
23:33:41 Initializing from normalized Laplacian + noise
23:33:41 Commencing optimization for 500 epochs, with 59332 positive edges
23:33:47 Optimization finished
[1] "40 0.02"23:33:47 UMAP embedding parameters a = 1.859 b = 0.8109
23:33:47 Read 1203 rows and found 38 numeric columns
23:33:47 Using Annoy for neighbor search, n_neighbors = 40
23:33:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:33:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bd9b2eb
23:33:47 Searching Annoy index using 1 thread, search_k = 4000
23:33:48 Annoy recall = 100%
23:33:51 Commencing smooth kNN distance calibration using 1 thread
23:33:56 Initializing from normalized Laplacian + noise
23:33:56 Commencing optimization for 500 epochs, with 59332 positive edges
23:34:02 Optimization finished
[1] "40 0.03"23:34:02 UMAP embedding parameters a = 1.822 b = 0.8212
23:34:02 Read 1203 rows and found 38 numeric columns
23:34:02 Using Annoy for neighbor search, n_neighbors = 40
23:34:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:34:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872349e5b7
23:34:02 Searching Annoy index using 1 thread, search_k = 4000
23:34:03 Annoy recall = 100%
23:34:06 Commencing smooth kNN distance calibration using 1 thread
23:34:11 Initializing from normalized Laplacian + noise
23:34:11 Commencing optimization for 500 epochs, with 59332 positive edges
23:34:17 Optimization finished
[1] "40 0.04"23:34:17 UMAP embedding parameters a = 1.786 b = 0.8316
23:34:17 Read 1203 rows and found 38 numeric columns
23:34:17 Using Annoy for neighbor search, n_neighbors = 40
23:34:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:34:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e51ebf2
23:34:18 Searching Annoy index using 1 thread, search_k = 4000
23:34:18 Annoy recall = 100%
23:34:21 Commencing smooth kNN distance calibration using 1 thread
23:34:26 Initializing from normalized Laplacian + noise
23:34:26 Commencing optimization for 500 epochs, with 59332 positive edges
23:34:32 Optimization finished
[1] "40 0.05"23:34:32 UMAP embedding parameters a = 1.75 b = 0.8421
23:34:32 Read 1203 rows and found 38 numeric columns
23:34:32 Using Annoy for neighbor search, n_neighbors = 40
23:34:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:34:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e161b94
23:34:33 Searching Annoy index using 1 thread, search_k = 4000
23:34:33 Annoy recall = 100%
23:34:36 Commencing smooth kNN distance calibration using 1 thread
23:34:41 Initializing from normalized Laplacian + noise
23:34:41 Commencing optimization for 500 epochs, with 59332 positive edges
23:34:47 Optimization finished
[1] "40 0.06"23:34:47 UMAP embedding parameters a = 1.715 b = 0.8526
23:34:47 Read 1203 rows and found 38 numeric columns
23:34:47 Using Annoy for neighbor search, n_neighbors = 40
23:34:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:34:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a022e0
23:34:48 Searching Annoy index using 1 thread, search_k = 4000
23:34:48 Annoy recall = 100%
23:34:51 Commencing smooth kNN distance calibration using 1 thread
23:34:56 Initializing from normalized Laplacian + noise
23:34:56 Commencing optimization for 500 epochs, with 59332 positive edges
23:35:02 Optimization finished
[1] "40 0.07"23:35:02 UMAP embedding parameters a = 1.68 b = 0.8631
23:35:02 Read 1203 rows and found 38 numeric columns
23:35:02 Using Annoy for neighbor search, n_neighbors = 40
23:35:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:35:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722bdd638
23:35:03 Searching Annoy index using 1 thread, search_k = 4000
23:35:03 Annoy recall = 100%
23:35:06 Commencing smooth kNN distance calibration using 1 thread
23:35:12 Initializing from normalized Laplacian + noise
23:35:12 Commencing optimization for 500 epochs, with 59332 positive edges
23:35:17 Optimization finished
[1] "40 0.08"23:35:18 UMAP embedding parameters a = 1.645 b = 0.8737
23:35:18 Read 1203 rows and found 38 numeric columns
23:35:18 Using Annoy for neighbor search, n_neighbors = 40
23:35:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:35:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b408b91
23:35:18 Searching Annoy index using 1 thread, search_k = 4000
23:35:18 Annoy recall = 100%
23:35:21 Commencing smooth kNN distance calibration using 1 thread
23:35:27 Initializing from normalized Laplacian + noise
23:35:27 Commencing optimization for 500 epochs, with 59332 positive edges
23:35:32 Optimization finished
[1] "40 0.09"23:35:33 UMAP embedding parameters a = 1.611 b = 0.8844
23:35:33 Read 1203 rows and found 38 numeric columns
23:35:33 Using Annoy for neighbor search, n_neighbors = 40
23:35:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:35:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769cacd8d
23:35:33 Searching Annoy index using 1 thread, search_k = 4000
23:35:33 Annoy recall = 100%
23:35:36 Commencing smooth kNN distance calibration using 1 thread
23:35:42 Initializing from normalized Laplacian + noise
23:35:42 Commencing optimization for 500 epochs, with 59332 positive edges
23:35:47 Optimization finished
[1] "40 0.1"23:35:48 UMAP embedding parameters a = 1.577 b = 0.8951
23:35:48 Read 1203 rows and found 38 numeric columns
23:35:48 Using Annoy for neighbor search, n_neighbors = 40
23:35:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:35:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a8ed7c2
23:35:48 Searching Annoy index using 1 thread, search_k = 4000
23:35:48 Annoy recall = 100%
23:35:51 Commencing smooth kNN distance calibration using 1 thread
23:35:57 Initializing from normalized Laplacian + noise
23:35:57 Commencing optimization for 500 epochs, with 59332 positive edges
23:36:03 Optimization finished
[1] "40 0.11"23:36:03 UMAP embedding parameters a = 1.544 b = 0.9058
23:36:03 Read 1203 rows and found 38 numeric columns
23:36:03 Using Annoy for neighbor search, n_neighbors = 40
23:36:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:36:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779ba3787
23:36:03 Searching Annoy index using 1 thread, search_k = 4000
23:36:03 Annoy recall = 100%
23:36:06 Commencing smooth kNN distance calibration using 1 thread
23:36:12 Initializing from normalized Laplacian + noise
23:36:12 Commencing optimization for 500 epochs, with 59332 positive edges
23:36:18 Optimization finished
[1] "40 0.12"23:36:18 UMAP embedding parameters a = 1.51 b = 0.9165
23:36:18 Read 1203 rows and found 38 numeric columns
23:36:18 Using Annoy for neighbor search, n_neighbors = 40
23:36:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:36:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87336f124f
23:36:18 Searching Annoy index using 1 thread, search_k = 4000
23:36:19 Annoy recall = 100%
23:36:22 Commencing smooth kNN distance calibration using 1 thread
23:36:27 Initializing from normalized Laplacian + noise
23:36:27 Commencing optimization for 500 epochs, with 59332 positive edges
23:36:33 Optimization finished
[1] "40 0.13"23:36:33 UMAP embedding parameters a = 1.478 b = 0.9272
23:36:33 Read 1203 rows and found 38 numeric columns
23:36:33 Using Annoy for neighbor search, n_neighbors = 40
23:36:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:36:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ca56d7d
23:36:33 Searching Annoy index using 1 thread, search_k = 4000
23:36:34 Annoy recall = 100%
23:36:37 Commencing smooth kNN distance calibration using 1 thread
23:36:42 Initializing from normalized Laplacian + noise
23:36:42 Commencing optimization for 500 epochs, with 59332 positive edges
23:36:48 Optimization finished
[1] "40 0.14"23:36:48 UMAP embedding parameters a = 1.446 b = 0.938
23:36:48 Read 1203 rows and found 38 numeric columns
23:36:48 Using Annoy for neighbor search, n_neighbors = 40
23:36:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:36:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ef8fd69
23:36:49 Searching Annoy index using 1 thread, search_k = 4000
23:36:49 Annoy recall = 100%
23:36:52 Commencing smooth kNN distance calibration using 1 thread
23:36:58 Initializing from normalized Laplacian + noise
23:36:58 Commencing optimization for 500 epochs, with 59332 positive edges
23:37:03 Optimization finished
[1] "40 0.15"23:37:03 UMAP embedding parameters a = 1.414 b = 0.9488
23:37:03 Read 1203 rows and found 38 numeric columns
23:37:04 Using Annoy for neighbor search, n_neighbors = 40
23:37:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:37:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777aaf943
23:37:04 Searching Annoy index using 1 thread, search_k = 4000
23:37:04 Annoy recall = 100%
23:37:07 Commencing smooth kNN distance calibration using 1 thread
23:37:13 Initializing from normalized Laplacian + noise
23:37:13 Commencing optimization for 500 epochs, with 59332 positive edges
23:37:19 Optimization finished
[1] "40 0.16"23:37:19 UMAP embedding parameters a = 1.383 b = 0.9596
23:37:19 Read 1203 rows and found 38 numeric columns
23:37:19 Using Annoy for neighbor search, n_neighbors = 40
23:37:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:37:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756c20bd1
23:37:19 Searching Annoy index using 1 thread, search_k = 4000
23:37:19 Annoy recall = 100%
23:37:22 Commencing smooth kNN distance calibration using 1 thread
23:37:28 Initializing from normalized Laplacian + noise
23:37:28 Commencing optimization for 500 epochs, with 59332 positive edges
23:37:34 Optimization finished
[1] "40 0.17"23:37:34 UMAP embedding parameters a = 1.352 b = 0.9704
23:37:34 Read 1203 rows and found 38 numeric columns
23:37:34 Using Annoy for neighbor search, n_neighbors = 40
23:37:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:37:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f71c30f
23:37:34 Searching Annoy index using 1 thread, search_k = 4000
23:37:35 Annoy recall = 100%
23:37:37 Commencing smooth kNN distance calibration using 1 thread
23:37:43 Initializing from normalized Laplacian + noise
23:37:43 Commencing optimization for 500 epochs, with 59332 positive edges
23:37:49 Optimization finished
[1] "40 0.18"23:37:49 UMAP embedding parameters a = 1.321 b = 0.9813
23:37:49 Read 1203 rows and found 38 numeric columns
23:37:49 Using Annoy for neighbor search, n_neighbors = 40
23:37:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:37:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87713818b7
23:37:49 Searching Annoy index using 1 thread, search_k = 4000
23:37:50 Annoy recall = 100%
23:37:53 Commencing smooth kNN distance calibration using 1 thread
23:37:58 Initializing from normalized Laplacian + noise
23:37:58 Commencing optimization for 500 epochs, with 59332 positive edges
23:38:04 Optimization finished
[1] "40 0.19"23:38:04 UMAP embedding parameters a = 1.292 b = 0.9921
23:38:04 Read 1203 rows and found 38 numeric columns
23:38:04 Using Annoy for neighbor search, n_neighbors = 40
23:38:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:38:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775f848a8
23:38:05 Searching Annoy index using 1 thread, search_k = 4000
23:38:05 Annoy recall = 100%
23:38:08 Commencing smooth kNN distance calibration using 1 thread
23:38:14 Initializing from normalized Laplacian + noise
23:38:14 Commencing optimization for 500 epochs, with 59332 positive edges
23:38:19 Optimization finished
[1] "40 0.2"23:38:20 UMAP embedding parameters a = 1.262 b = 1.003
23:38:20 Read 1203 rows and found 38 numeric columns
23:38:20 Using Annoy for neighbor search, n_neighbors = 40
23:38:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:38:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876939047c
23:38:20 Searching Annoy index using 1 thread, search_k = 4000
23:38:20 Annoy recall = 100%
23:38:23 Commencing smooth kNN distance calibration using 1 thread
23:38:29 Initializing from normalized Laplacian + noise
23:38:29 Commencing optimization for 500 epochs, with 59332 positive edges
23:38:35 Optimization finished
[1] "41 0"23:38:35 UMAP embedding parameters a = 1.933 b = 0.7905
23:38:35 Read 1203 rows and found 38 numeric columns
23:38:35 Using Annoy for neighbor search, n_neighbors = 41
23:38:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:38:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87401cbda7
23:38:35 Searching Annoy index using 1 thread, search_k = 4100
23:38:36 Annoy recall = 100%
23:38:38 Commencing smooth kNN distance calibration using 1 thread
23:38:44 Initializing from normalized Laplacian + noise
23:38:44 Commencing optimization for 500 epochs, with 60716 positive edges
23:38:50 Optimization finished
[1] "41 0.01"23:38:50 UMAP embedding parameters a = 1.896 b = 0.8006
23:38:50 Read 1203 rows and found 38 numeric columns
23:38:50 Using Annoy for neighbor search, n_neighbors = 41
23:38:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:38:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fd2a106
23:38:50 Searching Annoy index using 1 thread, search_k = 4100
23:38:51 Annoy recall = 100%
23:38:54 Commencing smooth kNN distance calibration using 1 thread
23:38:59 Initializing from normalized Laplacian + noise
23:39:00 Commencing optimization for 500 epochs, with 60716 positive edges
23:39:05 Optimization finished
[1] "41 0.02"23:39:05 UMAP embedding parameters a = 1.859 b = 0.8109
23:39:06 Read 1203 rows and found 38 numeric columns
23:39:06 Using Annoy for neighbor search, n_neighbors = 41
23:39:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:39:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738abc511
23:39:06 Searching Annoy index using 1 thread, search_k = 4100
23:39:06 Annoy recall = 100%
23:39:09 Commencing smooth kNN distance calibration using 1 thread
23:39:15 Initializing from normalized Laplacian + noise
23:39:15 Commencing optimization for 500 epochs, with 60716 positive edges
23:39:21 Optimization finished
[1] "41 0.03"23:39:21 UMAP embedding parameters a = 1.822 b = 0.8212
23:39:21 Read 1203 rows and found 38 numeric columns
23:39:21 Using Annoy for neighbor search, n_neighbors = 41
23:39:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:39:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871bd33ead
23:39:21 Searching Annoy index using 1 thread, search_k = 4100
23:39:22 Annoy recall = 100%
23:39:24 Commencing smooth kNN distance calibration using 1 thread
23:39:30 Initializing from normalized Laplacian + noise
23:39:30 Commencing optimization for 500 epochs, with 60716 positive edges
23:39:36 Optimization finished
[1] "41 0.04"23:39:36 UMAP embedding parameters a = 1.786 b = 0.8316
23:39:36 Read 1203 rows and found 38 numeric columns
23:39:36 Using Annoy for neighbor search, n_neighbors = 41
23:39:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:39:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87714e7a33
23:39:36 Searching Annoy index using 1 thread, search_k = 4100
23:39:37 Annoy recall = 100%
23:39:40 Commencing smooth kNN distance calibration using 1 thread
23:39:45 Initializing from normalized Laplacian + noise
23:39:46 Commencing optimization for 500 epochs, with 60716 positive edges
23:39:51 Optimization finished
[1] "41 0.05"23:39:52 UMAP embedding parameters a = 1.75 b = 0.8421
23:39:52 Read 1203 rows and found 38 numeric columns
23:39:52 Using Annoy for neighbor search, n_neighbors = 41
23:39:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:39:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8784f80dc
23:39:52 Searching Annoy index using 1 thread, search_k = 4100
23:39:52 Annoy recall = 100%
23:39:55 Commencing smooth kNN distance calibration using 1 thread
23:40:01 Initializing from normalized Laplacian + noise
23:40:01 Commencing optimization for 500 epochs, with 60716 positive edges
23:40:07 Optimization finished
[1] "41 0.06"23:40:07 UMAP embedding parameters a = 1.715 b = 0.8526
23:40:07 Read 1203 rows and found 38 numeric columns
23:40:07 Using Annoy for neighbor search, n_neighbors = 41
23:40:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:40:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744df4f8
23:40:07 Searching Annoy index using 1 thread, search_k = 4100
23:40:08 Annoy recall = 100%
23:40:10 Commencing smooth kNN distance calibration using 1 thread
23:40:16 Initializing from normalized Laplacian + noise
23:40:16 Commencing optimization for 500 epochs, with 60716 positive edges
23:40:22 Optimization finished
[1] "41 0.07"23:40:22 UMAP embedding parameters a = 1.68 b = 0.8631
23:40:22 Read 1203 rows and found 38 numeric columns
23:40:22 Using Annoy for neighbor search, n_neighbors = 41
23:40:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:40:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738d0c535
23:40:23 Searching Annoy index using 1 thread, search_k = 4100
23:40:23 Annoy recall = 100%
23:40:26 Commencing smooth kNN distance calibration using 1 thread
23:40:32 Initializing from normalized Laplacian + noise
23:40:32 Commencing optimization for 500 epochs, with 60716 positive edges
23:40:37 Optimization finished
[1] "41 0.08"23:40:38 UMAP embedding parameters a = 1.645 b = 0.8737
23:40:38 Read 1203 rows and found 38 numeric columns
23:40:38 Using Annoy for neighbor search, n_neighbors = 41
23:40:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:40:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ac5cc53
23:40:38 Searching Annoy index using 1 thread, search_k = 4100
23:40:38 Annoy recall = 100%
23:40:41 Commencing smooth kNN distance calibration using 1 thread
23:40:47 Initializing from normalized Laplacian + noise
23:40:47 Commencing optimization for 500 epochs, with 60716 positive edges
23:40:53 Optimization finished
[1] "41 0.09"23:40:53 UMAP embedding parameters a = 1.611 b = 0.8844
23:40:53 Read 1203 rows and found 38 numeric columns
23:40:53 Using Annoy for neighbor search, n_neighbors = 41
23:40:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:40:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c2c46ab
23:40:53 Searching Annoy index using 1 thread, search_k = 4100
23:40:54 Annoy recall = 100%
23:40:56 Commencing smooth kNN distance calibration using 1 thread
23:41:02 Initializing from normalized Laplacian + noise
23:41:02 Commencing optimization for 500 epochs, with 60716 positive edges
23:41:08 Optimization finished
[1] "41 0.1"23:41:08 UMAP embedding parameters a = 1.577 b = 0.8951
23:41:08 Read 1203 rows and found 38 numeric columns
23:41:08 Using Annoy for neighbor search, n_neighbors = 41
23:41:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:41:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e37968b
23:41:09 Searching Annoy index using 1 thread, search_k = 4100
23:41:09 Annoy recall = 100%
23:41:12 Commencing smooth kNN distance calibration using 1 thread
23:41:18 Initializing from normalized Laplacian + noise
23:41:18 Commencing optimization for 500 epochs, with 60716 positive edges
23:41:23 Optimization finished
[1] "41 0.11"23:41:24 UMAP embedding parameters a = 1.544 b = 0.9058
23:41:24 Read 1203 rows and found 38 numeric columns
23:41:24 Using Annoy for neighbor search, n_neighbors = 41
23:41:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:41:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732047f6
23:41:24 Searching Annoy index using 1 thread, search_k = 4100
23:41:24 Annoy recall = 100%
23:41:27 Commencing smooth kNN distance calibration using 1 thread
23:41:33 Initializing from normalized Laplacian + noise
23:41:33 Commencing optimization for 500 epochs, with 60716 positive edges
23:41:39 Optimization finished
[1] "41 0.12"23:41:39 UMAP embedding parameters a = 1.51 b = 0.9165
23:41:39 Read 1203 rows and found 38 numeric columns
23:41:39 Using Annoy for neighbor search, n_neighbors = 41
23:41:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:41:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875805f997
23:41:39 Searching Annoy index using 1 thread, search_k = 4100
23:41:40 Annoy recall = 100%
23:41:42 Commencing smooth kNN distance calibration using 1 thread
23:41:48 Initializing from normalized Laplacian + noise
23:41:48 Commencing optimization for 500 epochs, with 60716 positive edges
23:41:54 Optimization finished
[1] "41 0.13"23:41:54 UMAP embedding parameters a = 1.478 b = 0.9272
23:41:54 Read 1203 rows and found 38 numeric columns
23:41:54 Using Annoy for neighbor search, n_neighbors = 41
23:41:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:41:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721817c42
23:41:55 Searching Annoy index using 1 thread, search_k = 4100
23:41:55 Annoy recall = 100%
23:41:58 Commencing smooth kNN distance calibration using 1 thread
23:42:04 Initializing from normalized Laplacian + noise
23:42:04 Commencing optimization for 500 epochs, with 60716 positive edges
23:42:10 Optimization finished
[1] "41 0.14"23:42:10 UMAP embedding parameters a = 1.446 b = 0.938
23:42:10 Read 1203 rows and found 38 numeric columns
23:42:10 Using Annoy for neighbor search, n_neighbors = 41
23:42:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:42:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87517233e9
23:42:10 Searching Annoy index using 1 thread, search_k = 4100
23:42:10 Annoy recall = 100%
23:42:13 Commencing smooth kNN distance calibration using 1 thread
23:42:19 Initializing from normalized Laplacian + noise
23:42:19 Commencing optimization for 500 epochs, with 60716 positive edges
23:42:25 Optimization finished
[1] "41 0.15"23:42:25 UMAP embedding parameters a = 1.414 b = 0.9488
23:42:25 Read 1203 rows and found 38 numeric columns
23:42:25 Using Annoy for neighbor search, n_neighbors = 41
23:42:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:42:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87161c152b
23:42:26 Searching Annoy index using 1 thread, search_k = 4100
23:42:26 Annoy recall = 100%
23:42:29 Commencing smooth kNN distance calibration using 1 thread
23:42:35 Initializing from normalized Laplacian + noise
23:42:35 Commencing optimization for 500 epochs, with 60716 positive edges
23:42:40 Optimization finished
[1] "41 0.16"23:42:41 UMAP embedding parameters a = 1.383 b = 0.9596
23:42:41 Read 1203 rows and found 38 numeric columns
23:42:41 Using Annoy for neighbor search, n_neighbors = 41
23:42:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:42:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725219f23
23:42:41 Searching Annoy index using 1 thread, search_k = 4100
23:42:41 Annoy recall = 100%
23:42:44 Commencing smooth kNN distance calibration using 1 thread
23:42:50 Initializing from normalized Laplacian + noise
23:42:50 Commencing optimization for 500 epochs, with 60716 positive edges
23:42:56 Optimization finished
[1] "41 0.17"23:42:56 UMAP embedding parameters a = 1.352 b = 0.9704
23:42:56 Read 1203 rows and found 38 numeric columns
23:42:56 Using Annoy for neighbor search, n_neighbors = 41
23:42:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:42:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774300a21
23:42:56 Searching Annoy index using 1 thread, search_k = 4100
23:42:57 Annoy recall = 100%
23:43:00 Commencing smooth kNN distance calibration using 1 thread
23:43:05 Initializing from normalized Laplacian + noise
23:43:05 Commencing optimization for 500 epochs, with 60716 positive edges
23:43:11 Optimization finished
[1] "41 0.18"23:43:11 UMAP embedding parameters a = 1.321 b = 0.9813
23:43:11 Read 1203 rows and found 38 numeric columns
23:43:11 Using Annoy for neighbor search, n_neighbors = 41
23:43:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:43:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87115ca0bd
23:43:12 Searching Annoy index using 1 thread, search_k = 4100
23:43:12 Annoy recall = 100%
23:43:15 Commencing smooth kNN distance calibration using 1 thread
23:43:21 Initializing from normalized Laplacian + noise
23:43:21 Commencing optimization for 500 epochs, with 60716 positive edges
23:43:27 Optimization finished
[1] "41 0.19"23:43:27 UMAP embedding parameters a = 1.292 b = 0.9921
23:43:27 Read 1203 rows and found 38 numeric columns
23:43:27 Using Annoy for neighbor search, n_neighbors = 41
23:43:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:43:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87eec6cb0
23:43:27 Searching Annoy index using 1 thread, search_k = 4100
23:43:28 Annoy recall = 100%
23:43:31 Commencing smooth kNN distance calibration using 1 thread
23:43:36 Initializing from normalized Laplacian + noise
23:43:36 Commencing optimization for 500 epochs, with 60716 positive edges
23:43:42 Optimization finished
[1] "41 0.2"23:43:42 UMAP embedding parameters a = 1.262 b = 1.003
23:43:42 Read 1203 rows and found 38 numeric columns
23:43:42 Using Annoy for neighbor search, n_neighbors = 41
23:43:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:43:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ebee1e4
23:43:43 Searching Annoy index using 1 thread, search_k = 4100
23:43:43 Annoy recall = 100%
23:43:46 Commencing smooth kNN distance calibration using 1 thread
23:43:52 Initializing from normalized Laplacian + noise
23:43:52 Commencing optimization for 500 epochs, with 60716 positive edges
23:43:58 Optimization finished
[1] "42 0"23:43:58 UMAP embedding parameters a = 1.933 b = 0.7905
23:43:58 Read 1203 rows and found 38 numeric columns
23:43:58 Using Annoy for neighbor search, n_neighbors = 42
23:43:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:43:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b16d844
23:43:58 Searching Annoy index using 1 thread, search_k = 4200
23:43:58 Annoy recall = 100%
23:44:01 Commencing smooth kNN distance calibration using 1 thread
23:44:07 Initializing from normalized Laplacian + noise
23:44:07 Commencing optimization for 500 epochs, with 62096 positive edges
23:44:13 Optimization finished
[1] "42 0.01"23:44:13 UMAP embedding parameters a = 1.896 b = 0.8006
23:44:13 Read 1203 rows and found 38 numeric columns
23:44:13 Using Annoy for neighbor search, n_neighbors = 42
23:44:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:44:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87425b7f00
23:44:14 Searching Annoy index using 1 thread, search_k = 4200
23:44:14 Annoy recall = 100%
23:44:17 Commencing smooth kNN distance calibration using 1 thread
23:44:23 Initializing from normalized Laplacian + noise
23:44:23 Commencing optimization for 500 epochs, with 62096 positive edges
23:44:29 Optimization finished
[1] "42 0.02"23:44:29 UMAP embedding parameters a = 1.859 b = 0.8109
23:44:29 Read 1203 rows and found 38 numeric columns
23:44:29 Using Annoy for neighbor search, n_neighbors = 42
23:44:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:44:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b644f61
23:44:29 Searching Annoy index using 1 thread, search_k = 4200
23:44:30 Annoy recall = 100%
23:44:33 Commencing smooth kNN distance calibration using 1 thread
23:44:38 Initializing from normalized Laplacian + noise
23:44:38 Commencing optimization for 500 epochs, with 62096 positive edges
23:44:44 Optimization finished
[1] "42 0.03"23:44:44 UMAP embedding parameters a = 1.822 b = 0.8212
23:44:44 Read 1203 rows and found 38 numeric columns
23:44:44 Using Annoy for neighbor search, n_neighbors = 42
23:44:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:44:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a0fd5ad
23:44:45 Searching Annoy index using 1 thread, search_k = 4200
23:44:45 Annoy recall = 100%
23:44:48 Commencing smooth kNN distance calibration using 1 thread
23:44:54 Initializing from normalized Laplacian + noise
23:44:54 Commencing optimization for 500 epochs, with 62096 positive edges
23:45:00 Optimization finished
[1] "42 0.04"23:45:00 UMAP embedding parameters a = 1.786 b = 0.8316
23:45:00 Read 1203 rows and found 38 numeric columns
23:45:00 Using Annoy for neighbor search, n_neighbors = 42
23:45:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:45:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a067843
23:45:00 Searching Annoy index using 1 thread, search_k = 4200
23:45:01 Annoy recall = 100%
23:45:04 Commencing smooth kNN distance calibration using 1 thread
23:45:09 Initializing from normalized Laplacian + noise
23:45:09 Commencing optimization for 500 epochs, with 62096 positive edges
23:45:15 Optimization finished
[1] "42 0.05"23:45:16 UMAP embedding parameters a = 1.75 b = 0.8421
23:45:16 Read 1203 rows and found 38 numeric columns
23:45:16 Using Annoy for neighbor search, n_neighbors = 42
23:45:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:45:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712265b33
23:45:16 Searching Annoy index using 1 thread, search_k = 4200
23:45:16 Annoy recall = 100%
23:45:19 Commencing smooth kNN distance calibration using 1 thread
23:45:25 Initializing from normalized Laplacian + noise
23:45:25 Commencing optimization for 500 epochs, with 62096 positive edges
23:45:31 Optimization finished
[1] "42 0.06"23:45:31 UMAP embedding parameters a = 1.715 b = 0.8526
23:45:31 Read 1203 rows and found 38 numeric columns
23:45:31 Using Annoy for neighbor search, n_neighbors = 42
23:45:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:45:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87298198bc
23:45:32 Searching Annoy index using 1 thread, search_k = 4200
23:45:32 Annoy recall = 100%
23:45:35 Commencing smooth kNN distance calibration using 1 thread
23:45:41 Initializing from normalized Laplacian + noise
23:45:41 Commencing optimization for 500 epochs, with 62096 positive edges
23:45:47 Optimization finished
[1] "42 0.07"23:45:47 UMAP embedding parameters a = 1.68 b = 0.8631
23:45:47 Read 1203 rows and found 38 numeric columns
23:45:47 Using Annoy for neighbor search, n_neighbors = 42
23:45:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:45:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b3e90fa
23:45:47 Searching Annoy index using 1 thread, search_k = 4200
23:45:47 Annoy recall = 100%
23:45:50 Commencing smooth kNN distance calibration using 1 thread
23:45:56 Initializing from normalized Laplacian + noise
23:45:56 Commencing optimization for 500 epochs, with 62096 positive edges
23:46:02 Optimization finished
[1] "42 0.08"23:46:02 UMAP embedding parameters a = 1.645 b = 0.8737
23:46:02 Read 1203 rows and found 38 numeric columns
23:46:02 Using Annoy for neighbor search, n_neighbors = 42
23:46:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:46:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8781ea3db
23:46:03 Searching Annoy index using 1 thread, search_k = 4200
23:46:03 Annoy recall = 100%
23:46:06 Commencing smooth kNN distance calibration using 1 thread
23:46:12 Initializing from normalized Laplacian + noise
23:46:12 Commencing optimization for 500 epochs, with 62096 positive edges
23:46:18 Optimization finished
[1] "42 0.09"23:46:18 UMAP embedding parameters a = 1.611 b = 0.8844
23:46:18 Read 1203 rows and found 38 numeric columns
23:46:18 Using Annoy for neighbor search, n_neighbors = 42
23:46:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:46:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712ba9d38
23:46:18 Searching Annoy index using 1 thread, search_k = 4200
23:46:19 Annoy recall = 100%
23:46:22 Commencing smooth kNN distance calibration using 1 thread
23:46:28 Initializing from normalized Laplacian + noise
23:46:28 Commencing optimization for 500 epochs, with 62096 positive edges
23:46:33 Optimization finished
[1] "42 0.1"23:46:34 UMAP embedding parameters a = 1.577 b = 0.8951
23:46:34 Read 1203 rows and found 38 numeric columns
23:46:34 Using Annoy for neighbor search, n_neighbors = 42
23:46:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:46:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b5b4ea1
23:46:34 Searching Annoy index using 1 thread, search_k = 4200
23:46:34 Annoy recall = 100%
23:46:37 Commencing smooth kNN distance calibration using 1 thread
23:46:43 Initializing from normalized Laplacian + noise
23:46:43 Commencing optimization for 500 epochs, with 62096 positive edges
23:46:49 Optimization finished
[1] "42 0.11"23:46:49 UMAP embedding parameters a = 1.544 b = 0.9058
23:46:49 Read 1203 rows and found 38 numeric columns
23:46:49 Using Annoy for neighbor search, n_neighbors = 42
23:46:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:46:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767f144e2
23:46:50 Searching Annoy index using 1 thread, search_k = 4200
23:46:50 Annoy recall = 100%
23:46:53 Commencing smooth kNN distance calibration using 1 thread
23:46:59 Initializing from normalized Laplacian + noise
23:46:59 Commencing optimization for 500 epochs, with 62096 positive edges
23:47:05 Optimization finished
[1] "42 0.12"23:47:05 UMAP embedding parameters a = 1.51 b = 0.9165
23:47:05 Read 1203 rows and found 38 numeric columns
23:47:05 Using Annoy for neighbor search, n_neighbors = 42
23:47:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:47:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b66624a
23:47:05 Searching Annoy index using 1 thread, search_k = 4200
23:47:06 Annoy recall = 100%
23:47:09 Commencing smooth kNN distance calibration using 1 thread
23:47:15 Initializing from normalized Laplacian + noise
23:47:15 Commencing optimization for 500 epochs, with 62096 positive edges
23:47:20 Optimization finished
[1] "42 0.13"23:47:21 UMAP embedding parameters a = 1.478 b = 0.9272
23:47:21 Read 1203 rows and found 38 numeric columns
23:47:21 Using Annoy for neighbor search, n_neighbors = 42
23:47:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:47:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772e8d4e
23:47:21 Searching Annoy index using 1 thread, search_k = 4200
23:47:21 Annoy recall = 100%
23:47:24 Commencing smooth kNN distance calibration using 1 thread
23:47:30 Initializing from normalized Laplacian + noise
23:47:30 Commencing optimization for 500 epochs, with 62096 positive edges
23:47:36 Optimization finished
[1] "42 0.14"23:47:36 UMAP embedding parameters a = 1.446 b = 0.938
23:47:36 Read 1203 rows and found 38 numeric columns
23:47:36 Using Annoy for neighbor search, n_neighbors = 42
23:47:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:47:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87593fbf15
23:47:37 Searching Annoy index using 1 thread, search_k = 4200
23:47:37 Annoy recall = 100%
23:47:40 Commencing smooth kNN distance calibration using 1 thread
23:47:46 Initializing from normalized Laplacian + noise
23:47:46 Commencing optimization for 500 epochs, with 62096 positive edges
23:47:52 Optimization finished
[1] "42 0.15"23:47:52 UMAP embedding parameters a = 1.414 b = 0.9488
23:47:52 Read 1203 rows and found 38 numeric columns
23:47:52 Using Annoy for neighbor search, n_neighbors = 42
23:47:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:47:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753b5e326
23:47:52 Searching Annoy index using 1 thread, search_k = 4200
23:47:53 Annoy recall = 100%
23:47:56 Commencing smooth kNN distance calibration using 1 thread
23:48:02 Initializing from normalized Laplacian + noise
23:48:02 Commencing optimization for 500 epochs, with 62096 positive edges
23:48:07 Optimization finished
[1] "42 0.16"23:48:08 UMAP embedding parameters a = 1.383 b = 0.9596
23:48:08 Read 1203 rows and found 38 numeric columns
23:48:08 Using Annoy for neighbor search, n_neighbors = 42
23:48:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:48:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b7c8246
23:48:08 Searching Annoy index using 1 thread, search_k = 4200
23:48:08 Annoy recall = 100%
23:48:11 Commencing smooth kNN distance calibration using 1 thread
23:48:17 Initializing from normalized Laplacian + noise
23:48:17 Commencing optimization for 500 epochs, with 62096 positive edges
23:48:23 Optimization finished
[1] "42 0.17"23:48:23 UMAP embedding parameters a = 1.352 b = 0.9704
23:48:23 Read 1203 rows and found 38 numeric columns
23:48:23 Using Annoy for neighbor search, n_neighbors = 42
23:48:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:48:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871210844a
23:48:24 Searching Annoy index using 1 thread, search_k = 4200
23:48:24 Annoy recall = 100%
23:48:27 Commencing smooth kNN distance calibration using 1 thread
23:48:33 Initializing from normalized Laplacian + noise
23:48:33 Commencing optimization for 500 epochs, with 62096 positive edges
23:48:39 Optimization finished
[1] "42 0.18"23:48:39 UMAP embedding parameters a = 1.321 b = 0.9813
23:48:39 Read 1203 rows and found 38 numeric columns
23:48:39 Using Annoy for neighbor search, n_neighbors = 42
23:48:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:48:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e7baf7a
23:48:40 Searching Annoy index using 1 thread, search_k = 4200
23:48:40 Annoy recall = 100%
23:48:43 Commencing smooth kNN distance calibration using 1 thread
23:48:49 Initializing from normalized Laplacian + noise
23:48:49 Commencing optimization for 500 epochs, with 62096 positive edges
23:48:55 Optimization finished
[1] "42 0.19"23:48:55 UMAP embedding parameters a = 1.292 b = 0.9921
23:48:55 Read 1203 rows and found 38 numeric columns
23:48:55 Using Annoy for neighbor search, n_neighbors = 42
23:48:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:48:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a8c8f2
23:48:55 Searching Annoy index using 1 thread, search_k = 4200
23:48:56 Annoy recall = 100%
23:48:59 Commencing smooth kNN distance calibration using 1 thread
23:49:05 Initializing from normalized Laplacian + noise
23:49:05 Commencing optimization for 500 epochs, with 62096 positive edges
23:49:10 Optimization finished
[1] "42 0.2"23:49:11 UMAP embedding parameters a = 1.262 b = 1.003
23:49:11 Read 1203 rows and found 38 numeric columns
23:49:11 Using Annoy for neighbor search, n_neighbors = 42
23:49:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710481ad5
23:49:11 Searching Annoy index using 1 thread, search_k = 4200
23:49:11 Annoy recall = 100%
23:49:14 Commencing smooth kNN distance calibration using 1 thread
23:49:20 Initializing from normalized Laplacian + noise
23:49:20 Commencing optimization for 500 epochs, with 62096 positive edges
23:49:26 Optimization finished
[1] "43 0"23:49:26 UMAP embedding parameters a = 1.933 b = 0.7905
23:49:27 Read 1203 rows and found 38 numeric columns
23:49:27 Using Annoy for neighbor search, n_neighbors = 43
23:49:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719bf770
23:49:27 Searching Annoy index using 1 thread, search_k = 4300
23:49:27 Annoy recall = 100%
23:49:30 Commencing smooth kNN distance calibration using 1 thread
23:49:36 Initializing from normalized Laplacian + noise
23:49:36 Commencing optimization for 500 epochs, with 63538 positive edges
23:49:42 Optimization finished
[1] "43 0.01"23:49:42 UMAP embedding parameters a = 1.896 b = 0.8006
23:49:42 Read 1203 rows and found 38 numeric columns
23:49:42 Using Annoy for neighbor search, n_neighbors = 43
23:49:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875faec289
23:49:43 Searching Annoy index using 1 thread, search_k = 4300
23:49:43 Annoy recall = 100%
23:49:46 Commencing smooth kNN distance calibration using 1 thread
23:49:52 Initializing from normalized Laplacian + noise
23:49:52 Commencing optimization for 500 epochs, with 63538 positive edges
23:49:58 Optimization finished
[1] "43 0.02"23:49:58 UMAP embedding parameters a = 1.859 b = 0.8109
23:49:58 Read 1203 rows and found 38 numeric columns
23:49:58 Using Annoy for neighbor search, n_neighbors = 43
23:49:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731c99718
23:49:58 Searching Annoy index using 1 thread, search_k = 4300
23:49:59 Annoy recall = 100%
23:50:02 Commencing smooth kNN distance calibration using 1 thread
23:50:08 Initializing from normalized Laplacian + noise
23:50:08 Commencing optimization for 500 epochs, with 63538 positive edges
23:50:14 Optimization finished
[1] "43 0.03"23:50:14 UMAP embedding parameters a = 1.822 b = 0.8212
23:50:14 Read 1203 rows and found 38 numeric columns
23:50:14 Using Annoy for neighbor search, n_neighbors = 43
23:50:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:50:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87530e2b59
23:50:14 Searching Annoy index using 1 thread, search_k = 4300
23:50:15 Annoy recall = 100%
23:50:18 Commencing smooth kNN distance calibration using 1 thread
23:50:24 Initializing from normalized Laplacian + noise
23:50:24 Commencing optimization for 500 epochs, with 63538 positive edges
23:50:30 Optimization finished
[1] "43 0.04"23:50:30 UMAP embedding parameters a = 1.786 b = 0.8316
23:50:30 Read 1203 rows and found 38 numeric columns
23:50:30 Using Annoy for neighbor search, n_neighbors = 43
23:50:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:50:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775cad7b4
23:50:30 Searching Annoy index using 1 thread, search_k = 4300
23:50:31 Annoy recall = 100%
23:50:34 Commencing smooth kNN distance calibration using 1 thread
23:50:40 Initializing from normalized Laplacian + noise
23:50:40 Commencing optimization for 500 epochs, with 63538 positive edges
23:50:46 Optimization finished
[1] "43 0.05"23:50:46 UMAP embedding parameters a = 1.75 b = 0.8421
23:50:46 Read 1203 rows and found 38 numeric columns
23:50:46 Using Annoy for neighbor search, n_neighbors = 43
23:50:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:50:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756eb363b
23:50:46 Searching Annoy index using 1 thread, search_k = 4300
23:50:46 Annoy recall = 100%
23:50:49 Commencing smooth kNN distance calibration using 1 thread
23:50:55 Initializing from normalized Laplacian + noise
23:50:56 Commencing optimization for 500 epochs, with 63538 positive edges
23:51:01 Optimization finished
[1] "43 0.06"23:51:02 UMAP embedding parameters a = 1.715 b = 0.8526
23:51:02 Read 1203 rows and found 38 numeric columns
23:51:02 Using Annoy for neighbor search, n_neighbors = 43
23:51:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:51:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87473e357b
23:51:02 Searching Annoy index using 1 thread, search_k = 4300
23:51:02 Annoy recall = 100%
23:51:05 Commencing smooth kNN distance calibration using 1 thread
23:51:11 Initializing from normalized Laplacian + noise
23:51:11 Commencing optimization for 500 epochs, with 63538 positive edges
23:51:17 Optimization finished
[1] "43 0.07"23:51:18 UMAP embedding parameters a = 1.68 b = 0.8631
23:51:18 Read 1203 rows and found 38 numeric columns
23:51:18 Using Annoy for neighbor search, n_neighbors = 43
23:51:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:51:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877277871
23:51:18 Searching Annoy index using 1 thread, search_k = 4300
23:51:18 Annoy recall = 100%
23:51:21 Commencing smooth kNN distance calibration using 1 thread
23:51:27 Initializing from normalized Laplacian + noise
23:51:27 Commencing optimization for 500 epochs, with 63538 positive edges
23:51:33 Optimization finished
[1] "43 0.08"23:51:34 UMAP embedding parameters a = 1.645 b = 0.8737
23:51:34 Read 1203 rows and found 38 numeric columns
23:51:34 Using Annoy for neighbor search, n_neighbors = 43
23:51:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:51:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765d7a2eb
23:51:34 Searching Annoy index using 1 thread, search_k = 4300
23:51:34 Annoy recall = 100%
23:51:37 Commencing smooth kNN distance calibration using 1 thread
23:51:43 Initializing from normalized Laplacian + noise
23:51:43 Commencing optimization for 500 epochs, with 63538 positive edges
23:51:49 Optimization finished
[1] "43 0.09"23:51:50 UMAP embedding parameters a = 1.611 b = 0.8844
23:51:50 Read 1203 rows and found 38 numeric columns
23:51:50 Using Annoy for neighbor search, n_neighbors = 43
23:51:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:51:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725fd175f
23:51:50 Searching Annoy index using 1 thread, search_k = 4300
23:51:50 Annoy recall = 100%
23:51:53 Commencing smooth kNN distance calibration using 1 thread
23:51:59 Initializing from normalized Laplacian + noise
23:51:59 Commencing optimization for 500 epochs, with 63538 positive edges
23:52:05 Optimization finished
[1] "43 0.1"23:52:06 UMAP embedding parameters a = 1.577 b = 0.8951
23:52:06 Read 1203 rows and found 38 numeric columns
23:52:06 Using Annoy for neighbor search, n_neighbors = 43
23:52:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:52:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87123e50b6
23:52:06 Searching Annoy index using 1 thread, search_k = 4300
23:52:06 Annoy recall = 100%
23:52:09 Commencing smooth kNN distance calibration using 1 thread
23:52:15 Initializing from normalized Laplacian + noise
23:52:15 Commencing optimization for 500 epochs, with 63538 positive edges
23:52:21 Optimization finished
[1] "43 0.11"23:52:22 UMAP embedding parameters a = 1.544 b = 0.9058
23:52:22 Read 1203 rows and found 38 numeric columns
23:52:22 Using Annoy for neighbor search, n_neighbors = 43
23:52:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:52:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87283321eb
23:52:22 Searching Annoy index using 1 thread, search_k = 4300
23:52:22 Annoy recall = 100%
23:52:25 Commencing smooth kNN distance calibration using 1 thread
23:52:31 Initializing from normalized Laplacian + noise
23:52:31 Commencing optimization for 500 epochs, with 63538 positive edges
23:52:37 Optimization finished
[1] "43 0.12"23:52:38 UMAP embedding parameters a = 1.51 b = 0.9165
23:52:38 Read 1203 rows and found 38 numeric columns
23:52:38 Using Annoy for neighbor search, n_neighbors = 43
23:52:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:52:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87616166c0
23:52:38 Searching Annoy index using 1 thread, search_k = 4300
23:52:38 Annoy recall = 100%
23:52:41 Commencing smooth kNN distance calibration using 1 thread
23:52:47 Initializing from normalized Laplacian + noise
23:52:47 Commencing optimization for 500 epochs, with 63538 positive edges
23:52:53 Optimization finished
[1] "43 0.13"23:52:54 UMAP embedding parameters a = 1.478 b = 0.9272
23:52:54 Read 1203 rows and found 38 numeric columns
23:52:54 Using Annoy for neighbor search, n_neighbors = 43
23:52:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:52:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c4e2663
23:52:54 Searching Annoy index using 1 thread, search_k = 4300
23:52:54 Annoy recall = 100%
23:52:57 Commencing smooth kNN distance calibration using 1 thread
23:53:03 Initializing from normalized Laplacian + noise
23:53:03 Commencing optimization for 500 epochs, with 63538 positive edges
23:53:09 Optimization finished
[1] "43 0.14"23:53:10 UMAP embedding parameters a = 1.446 b = 0.938
23:53:10 Read 1203 rows and found 38 numeric columns
23:53:10 Using Annoy for neighbor search, n_neighbors = 43
23:53:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:53:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762399a2f
23:53:10 Searching Annoy index using 1 thread, search_k = 4300
23:53:10 Annoy recall = 100%
23:53:13 Commencing smooth kNN distance calibration using 1 thread
23:53:19 Initializing from normalized Laplacian + noise
23:53:19 Commencing optimization for 500 epochs, with 63538 positive edges
23:53:25 Optimization finished
[1] "43 0.15"23:53:26 UMAP embedding parameters a = 1.414 b = 0.9488
23:53:26 Read 1203 rows and found 38 numeric columns
23:53:26 Using Annoy for neighbor search, n_neighbors = 43
23:53:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:53:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877387c1f3
23:53:26 Searching Annoy index using 1 thread, search_k = 4300
23:53:26 Annoy recall = 100%
23:53:29 Commencing smooth kNN distance calibration using 1 thread
23:53:36 Initializing from normalized Laplacian + noise
23:53:36 Commencing optimization for 500 epochs, with 63538 positive edges
23:53:42 Optimization finished
[1] "43 0.16"23:53:42 UMAP embedding parameters a = 1.383 b = 0.9596
23:53:42 Read 1203 rows and found 38 numeric columns
23:53:42 Using Annoy for neighbor search, n_neighbors = 43
23:53:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:53:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725cfbf20
23:53:42 Searching Annoy index using 1 thread, search_k = 4300
23:53:43 Annoy recall = 100%
23:53:46 Commencing smooth kNN distance calibration using 1 thread
23:53:52 Initializing from normalized Laplacian + noise
23:53:52 Commencing optimization for 500 epochs, with 63538 positive edges
23:53:58 Optimization finished
[1] "43 0.17"23:53:58 UMAP embedding parameters a = 1.352 b = 0.9704
23:53:58 Read 1203 rows and found 38 numeric columns
23:53:58 Using Annoy for neighbor search, n_neighbors = 43
23:53:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:53:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d782b29
23:53:58 Searching Annoy index using 1 thread, search_k = 4300
23:53:58 Annoy recall = 100%
23:54:02 Commencing smooth kNN distance calibration using 1 thread
23:54:08 Initializing from normalized Laplacian + noise
23:54:08 Commencing optimization for 500 epochs, with 63538 positive edges
23:54:14 Optimization finished
[1] "43 0.18"23:54:14 UMAP embedding parameters a = 1.321 b = 0.9813
23:54:14 Read 1203 rows and found 38 numeric columns
23:54:14 Using Annoy for neighbor search, n_neighbors = 43
23:54:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:54:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ba665cf
23:54:14 Searching Annoy index using 1 thread, search_k = 4300
23:54:15 Annoy recall = 100%
23:54:18 Commencing smooth kNN distance calibration using 1 thread
23:54:24 Initializing from normalized Laplacian + noise
23:54:24 Commencing optimization for 500 epochs, with 63538 positive edges
23:54:30 Optimization finished
[1] "43 0.19"23:54:30 UMAP embedding parameters a = 1.292 b = 0.9921
23:54:30 Read 1203 rows and found 38 numeric columns
23:54:30 Using Annoy for neighbor search, n_neighbors = 43
23:54:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:54:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87388a5c58
23:54:30 Searching Annoy index using 1 thread, search_k = 4300
23:54:31 Annoy recall = 100%
23:54:34 Commencing smooth kNN distance calibration using 1 thread
23:54:40 Initializing from normalized Laplacian + noise
23:54:40 Commencing optimization for 500 epochs, with 63538 positive edges
23:54:46 Optimization finished
[1] "43 0.2"23:54:46 UMAP embedding parameters a = 1.262 b = 1.003
23:54:46 Read 1203 rows and found 38 numeric columns
23:54:46 Using Annoy for neighbor search, n_neighbors = 43
23:54:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:54:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778d379cb
23:54:46 Searching Annoy index using 1 thread, search_k = 4300
23:54:47 Annoy recall = 100%
23:54:50 Commencing smooth kNN distance calibration using 1 thread
23:54:56 Initializing from normalized Laplacian + noise
23:54:56 Commencing optimization for 500 epochs, with 63538 positive edges
23:55:02 Optimization finished
[1] "44 0"23:55:02 UMAP embedding parameters a = 1.933 b = 0.7905
23:55:02 Read 1203 rows and found 38 numeric columns
23:55:02 Using Annoy for neighbor search, n_neighbors = 44
23:55:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:55:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876397aab1
23:55:02 Searching Annoy index using 1 thread, search_k = 4400
23:55:03 Annoy recall = 100%
23:55:06 Commencing smooth kNN distance calibration using 1 thread
23:55:12 Initializing from normalized Laplacian + noise
23:55:12 Commencing optimization for 500 epochs, with 64996 positive edges
23:55:18 Optimization finished
[1] "44 0.01"23:55:18 UMAP embedding parameters a = 1.896 b = 0.8006
23:55:18 Read 1203 rows and found 38 numeric columns
23:55:18 Using Annoy for neighbor search, n_neighbors = 44
23:55:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:55:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f0bea2
23:55:19 Searching Annoy index using 1 thread, search_k = 4400
23:55:19 Annoy recall = 100%
23:55:22 Commencing smooth kNN distance calibration using 1 thread
23:55:28 Initializing from normalized Laplacian + noise
23:55:28 Commencing optimization for 500 epochs, with 64996 positive edges
23:55:34 Optimization finished
[1] "44 0.02"23:55:35 UMAP embedding parameters a = 1.859 b = 0.8109
23:55:35 Read 1203 rows and found 38 numeric columns
23:55:35 Using Annoy for neighbor search, n_neighbors = 44
23:55:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:55:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720719
23:55:35 Searching Annoy index using 1 thread, search_k = 4400
23:55:35 Annoy recall = 100%
23:55:38 Commencing smooth kNN distance calibration using 1 thread
23:55:44 Initializing from normalized Laplacian + noise
23:55:44 Commencing optimization for 500 epochs, with 64996 positive edges
23:55:51 Optimization finished
[1] "44 0.03"23:55:51 UMAP embedding parameters a = 1.822 b = 0.8212
23:55:51 Read 1203 rows and found 38 numeric columns
23:55:51 Using Annoy for neighbor search, n_neighbors = 44
23:55:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:55:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cd769c6
23:55:51 Searching Annoy index using 1 thread, search_k = 4400
23:55:51 Annoy recall = 100%
23:55:54 Commencing smooth kNN distance calibration using 1 thread
23:56:01 Initializing from normalized Laplacian + noise
23:56:01 Commencing optimization for 500 epochs, with 64996 positive edges
23:56:07 Optimization finished
[1] "44 0.04"23:56:07 UMAP embedding parameters a = 1.786 b = 0.8316
23:56:07 Read 1203 rows and found 38 numeric columns
23:56:07 Using Annoy for neighbor search, n_neighbors = 44
23:56:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:56:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757a6a1c9
23:56:07 Searching Annoy index using 1 thread, search_k = 4400
23:56:08 Annoy recall = 100%
23:56:11 Commencing smooth kNN distance calibration using 1 thread
23:56:17 Initializing from normalized Laplacian + noise
23:56:17 Commencing optimization for 500 epochs, with 64996 positive edges
23:56:23 Optimization finished
[1] "44 0.05"23:56:23 UMAP embedding parameters a = 1.75 b = 0.8421
23:56:23 Read 1203 rows and found 38 numeric columns
23:56:23 Using Annoy for neighbor search, n_neighbors = 44
23:56:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:56:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b7e8960
23:56:23 Searching Annoy index using 1 thread, search_k = 4400
23:56:24 Annoy recall = 100%
23:56:27 Commencing smooth kNN distance calibration using 1 thread
23:56:33 Initializing from normalized Laplacian + noise
23:56:33 Commencing optimization for 500 epochs, with 64996 positive edges
23:56:39 Optimization finished
[1] "44 0.06"23:56:39 UMAP embedding parameters a = 1.715 b = 0.8526
23:56:39 Read 1203 rows and found 38 numeric columns
23:56:39 Using Annoy for neighbor search, n_neighbors = 44
23:56:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:56:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ee7ee11
23:56:40 Searching Annoy index using 1 thread, search_k = 4400
23:56:40 Annoy recall = 100%
23:56:43 Commencing smooth kNN distance calibration using 1 thread
23:56:49 Initializing from normalized Laplacian + noise
23:56:49 Commencing optimization for 500 epochs, with 64996 positive edges
23:56:55 Optimization finished
[1] "44 0.07"23:56:56 UMAP embedding parameters a = 1.68 b = 0.8631
23:56:56 Read 1203 rows and found 38 numeric columns
23:56:56 Using Annoy for neighbor search, n_neighbors = 44
23:56:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:56:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756225143
23:56:56 Searching Annoy index using 1 thread, search_k = 4400
23:56:56 Annoy recall = 100%
23:56:59 Commencing smooth kNN distance calibration using 1 thread
23:57:06 Initializing from normalized Laplacian + noise
23:57:06 Commencing optimization for 500 epochs, with 64996 positive edges
23:57:12 Optimization finished
[1] "44 0.08"23:57:12 UMAP embedding parameters a = 1.645 b = 0.8737
23:57:12 Read 1203 rows and found 38 numeric columns
23:57:12 Using Annoy for neighbor search, n_neighbors = 44
23:57:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:57:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713275252
23:57:12 Searching Annoy index using 1 thread, search_k = 4400
23:57:12 Annoy recall = 100%
23:57:16 Commencing smooth kNN distance calibration using 1 thread
23:57:22 Initializing from normalized Laplacian + noise
23:57:22 Commencing optimization for 500 epochs, with 64996 positive edges
23:57:28 Optimization finished
[1] "44 0.09"23:57:28 UMAP embedding parameters a = 1.611 b = 0.8844
23:57:28 Read 1203 rows and found 38 numeric columns
23:57:28 Using Annoy for neighbor search, n_neighbors = 44
23:57:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:57:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f3008e6
23:57:28 Searching Annoy index using 1 thread, search_k = 4400
23:57:29 Annoy recall = 100%
23:57:32 Commencing smooth kNN distance calibration using 1 thread
23:57:38 Initializing from normalized Laplacian + noise
23:57:38 Commencing optimization for 500 epochs, with 64996 positive edges
23:57:44 Optimization finished
[1] "44 0.1"23:57:44 UMAP embedding parameters a = 1.577 b = 0.8951
23:57:44 Read 1203 rows and found 38 numeric columns
23:57:44 Using Annoy for neighbor search, n_neighbors = 44
23:57:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:57:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757be48b3
23:57:45 Searching Annoy index using 1 thread, search_k = 4400
23:57:45 Annoy recall = 100%
23:57:48 Commencing smooth kNN distance calibration using 1 thread
23:57:54 Initializing from normalized Laplacian + noise
23:57:55 Commencing optimization for 500 epochs, with 64996 positive edges
23:58:01 Optimization finished
[1] "44 0.11"23:58:01 UMAP embedding parameters a = 1.544 b = 0.9058
23:58:01 Read 1203 rows and found 38 numeric columns
23:58:01 Using Annoy for neighbor search, n_neighbors = 44
23:58:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:58:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772d614db
23:58:01 Searching Annoy index using 1 thread, search_k = 4400
23:58:01 Annoy recall = 100%
23:58:04 Commencing smooth kNN distance calibration using 1 thread
23:58:11 Initializing from normalized Laplacian + noise
23:58:11 Commencing optimization for 500 epochs, with 64996 positive edges
23:58:17 Optimization finished
[1] "44 0.12"23:58:17 UMAP embedding parameters a = 1.51 b = 0.9165
23:58:17 Read 1203 rows and found 38 numeric columns
23:58:17 Using Annoy for neighbor search, n_neighbors = 44
23:58:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:58:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710f99ffe
23:58:17 Searching Annoy index using 1 thread, search_k = 4400
23:58:18 Annoy recall = 100%
23:58:21 Commencing smooth kNN distance calibration using 1 thread
23:58:27 Initializing from normalized Laplacian + noise
23:58:27 Commencing optimization for 500 epochs, with 64996 positive edges
23:58:33 Optimization finished
[1] "44 0.13"23:58:33 UMAP embedding parameters a = 1.478 b = 0.9272
23:58:33 Read 1203 rows and found 38 numeric columns
23:58:33 Using Annoy for neighbor search, n_neighbors = 44
23:58:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:58:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872acc740d
23:58:34 Searching Annoy index using 1 thread, search_k = 4400
23:58:34 Annoy recall = 100%
23:58:37 Commencing smooth kNN distance calibration using 1 thread
23:58:43 Initializing from normalized Laplacian + noise
23:58:43 Commencing optimization for 500 epochs, with 64996 positive edges
23:58:50 Optimization finished
[1] "44 0.14"23:58:50 UMAP embedding parameters a = 1.446 b = 0.938
23:58:50 Read 1203 rows and found 38 numeric columns
23:58:50 Using Annoy for neighbor search, n_neighbors = 44
23:58:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:58:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768a0ec8f
23:58:50 Searching Annoy index using 1 thread, search_k = 4400
23:58:50 Annoy recall = 100%
23:58:54 Commencing smooth kNN distance calibration using 1 thread
23:59:00 Initializing from normalized Laplacian + noise
23:59:00 Commencing optimization for 500 epochs, with 64996 positive edges
23:59:06 Optimization finished
[1] "44 0.15"23:59:06 UMAP embedding parameters a = 1.414 b = 0.9488
23:59:06 Read 1203 rows and found 38 numeric columns
23:59:06 Using Annoy for neighbor search, n_neighbors = 44
23:59:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:59:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767e4d639
23:59:06 Searching Annoy index using 1 thread, search_k = 4400
23:59:07 Annoy recall = 100%
23:59:10 Commencing smooth kNN distance calibration using 1 thread
23:59:16 Initializing from normalized Laplacian + noise
23:59:16 Commencing optimization for 500 epochs, with 64996 positive edges
23:59:22 Optimization finished
[1] "44 0.16"23:59:22 UMAP embedding parameters a = 1.383 b = 0.9596
23:59:22 Read 1203 rows and found 38 numeric columns
23:59:22 Using Annoy for neighbor search, n_neighbors = 44
23:59:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:59:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87720aa988
23:59:23 Searching Annoy index using 1 thread, search_k = 4400
23:59:23 Annoy recall = 100%
23:59:26 Commencing smooth kNN distance calibration using 1 thread
23:59:32 Initializing from normalized Laplacian + noise
23:59:33 Commencing optimization for 500 epochs, with 64996 positive edges
23:59:39 Optimization finished
[1] "44 0.17"23:59:39 UMAP embedding parameters a = 1.352 b = 0.9704
23:59:39 Read 1203 rows and found 38 numeric columns
23:59:39 Using Annoy for neighbor search, n_neighbors = 44
23:59:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:59:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876fc86501
23:59:39 Searching Annoy index using 1 thread, search_k = 4400
23:59:40 Annoy recall = 100%
23:59:43 Commencing smooth kNN distance calibration using 1 thread
23:59:49 Initializing from normalized Laplacian + noise
23:59:49 Commencing optimization for 500 epochs, with 64996 positive edges
23:59:55 Optimization finished
[1] "44 0.18"23:59:55 UMAP embedding parameters a = 1.321 b = 0.9813
23:59:55 Read 1203 rows and found 38 numeric columns
23:59:55 Using Annoy for neighbor search, n_neighbors = 44
23:59:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:59:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874dbc7925
23:59:56 Searching Annoy index using 1 thread, search_k = 4400
23:59:56 Annoy recall = 100%
23:59:59 Commencing smooth kNN distance calibration using 1 thread
00:00:05 Initializing from normalized Laplacian + noise
00:00:05 Commencing optimization for 500 epochs, with 64996 positive edges
00:00:11 Optimization finished
[1] "44 0.19"00:00:12 UMAP embedding parameters a = 1.292 b = 0.9921
00:00:12 Read 1203 rows and found 38 numeric columns
00:00:12 Using Annoy for neighbor search, n_neighbors = 44
00:00:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:00:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871807c0e7
00:00:12 Searching Annoy index using 1 thread, search_k = 4400
00:00:12 Annoy recall = 100%
00:00:15 Commencing smooth kNN distance calibration using 1 thread
00:00:22 Initializing from normalized Laplacian + noise
00:00:22 Commencing optimization for 500 epochs, with 64996 positive edges
00:00:28 Optimization finished
[1] "44 0.2"00:00:28 UMAP embedding parameters a = 1.262 b = 1.003
00:00:28 Read 1203 rows and found 38 numeric columns
00:00:28 Using Annoy for neighbor search, n_neighbors = 44
00:00:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:00:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87206b5b7
00:00:28 Searching Annoy index using 1 thread, search_k = 4400
00:00:29 Annoy recall = 100%
00:00:32 Commencing smooth kNN distance calibration using 1 thread
00:00:38 Initializing from normalized Laplacian + noise
00:00:38 Commencing optimization for 500 epochs, with 64996 positive edges
00:00:44 Optimization finished
[1] "45 0"00:00:44 UMAP embedding parameters a = 1.933 b = 0.7905
00:00:44 Read 1203 rows and found 38 numeric columns
00:00:44 Using Annoy for neighbor search, n_neighbors = 45
00:00:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:00:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775ef9b10
00:00:45 Searching Annoy index using 1 thread, search_k = 4500
00:00:45 Annoy recall = 100%
00:00:48 Commencing smooth kNN distance calibration using 1 thread
00:00:55 Initializing from normalized Laplacian + noise
00:00:55 Commencing optimization for 500 epochs, with 66368 positive edges
00:01:01 Optimization finished
[1] "45 0.01"00:01:01 UMAP embedding parameters a = 1.896 b = 0.8006
00:01:01 Read 1203 rows and found 38 numeric columns
00:01:01 Using Annoy for neighbor search, n_neighbors = 45
00:01:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:01:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87796927a7
00:01:01 Searching Annoy index using 1 thread, search_k = 4500
00:01:02 Annoy recall = 100%
00:01:05 Commencing smooth kNN distance calibration using 1 thread
00:01:11 Initializing from normalized Laplacian + noise
00:01:11 Commencing optimization for 500 epochs, with 66368 positive edges
00:01:17 Optimization finished
[1] "45 0.02"00:01:17 UMAP embedding parameters a = 1.859 b = 0.8109
00:01:17 Read 1203 rows and found 38 numeric columns
00:01:17 Using Annoy for neighbor search, n_neighbors = 45
00:01:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:01:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e54dc1a
00:01:18 Searching Annoy index using 1 thread, search_k = 4500
00:01:18 Annoy recall = 100%
00:01:21 Commencing smooth kNN distance calibration using 1 thread
00:01:28 Initializing from normalized Laplacian + noise
00:01:28 Commencing optimization for 500 epochs, with 66368 positive edges
00:01:34 Optimization finished
[1] "45 0.03"00:01:34 UMAP embedding parameters a = 1.822 b = 0.8212
00:01:34 Read 1203 rows and found 38 numeric columns
00:01:34 Using Annoy for neighbor search, n_neighbors = 45
00:01:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:01:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875829353f
00:01:34 Searching Annoy index using 1 thread, search_k = 4500
00:01:35 Annoy recall = 100%
00:01:38 Commencing smooth kNN distance calibration using 1 thread
00:01:44 Initializing from normalized Laplacian + noise
00:01:44 Commencing optimization for 500 epochs, with 66368 positive edges
00:01:50 Optimization finished
[1] "45 0.04"00:01:50 UMAP embedding parameters a = 1.786 b = 0.8316
00:01:50 Read 1203 rows and found 38 numeric columns
00:01:50 Using Annoy for neighbor search, n_neighbors = 45
00:01:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:01:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cf0e99b
00:01:51 Searching Annoy index using 1 thread, search_k = 4500
00:01:51 Annoy recall = 100%
00:01:54 Commencing smooth kNN distance calibration using 1 thread
00:02:01 Initializing from normalized Laplacian + noise
00:02:01 Commencing optimization for 500 epochs, with 66368 positive edges
00:02:07 Optimization finished
[1] "45 0.05"00:02:07 UMAP embedding parameters a = 1.75 b = 0.8421
00:02:07 Read 1203 rows and found 38 numeric columns
00:02:07 Using Annoy for neighbor search, n_neighbors = 45
00:02:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:02:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724249b3a
00:02:07 Searching Annoy index using 1 thread, search_k = 4500
00:02:08 Annoy recall = 100%
00:02:11 Commencing smooth kNN distance calibration using 1 thread
00:02:17 Initializing from normalized Laplacian + noise
00:02:17 Commencing optimization for 500 epochs, with 66368 positive edges
00:02:23 Optimization finished
[1] "45 0.06"00:02:23 UMAP embedding parameters a = 1.715 b = 0.8526
00:02:23 Read 1203 rows and found 38 numeric columns
00:02:23 Using Annoy for neighbor search, n_neighbors = 45
00:02:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:02:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765a16069
00:02:24 Searching Annoy index using 1 thread, search_k = 4500
00:02:24 Annoy recall = 100%
00:02:27 Commencing smooth kNN distance calibration using 1 thread
00:02:34 Initializing from normalized Laplacian + noise
00:02:34 Commencing optimization for 500 epochs, with 66368 positive edges
00:02:40 Optimization finished
[1] "45 0.07"00:02:40 UMAP embedding parameters a = 1.68 b = 0.8631
00:02:40 Read 1203 rows and found 38 numeric columns
00:02:40 Using Annoy for neighbor search, n_neighbors = 45
00:02:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:02:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768974f6a
00:02:40 Searching Annoy index using 1 thread, search_k = 4500
00:02:41 Annoy recall = 100%
00:02:44 Commencing smooth kNN distance calibration using 1 thread
00:02:50 Initializing from normalized Laplacian + noise
00:02:50 Commencing optimization for 500 epochs, with 66368 positive edges
00:02:57 Optimization finished
[1] "45 0.08"00:02:57 UMAP embedding parameters a = 1.645 b = 0.8737
00:02:57 Read 1203 rows and found 38 numeric columns
00:02:57 Using Annoy for neighbor search, n_neighbors = 45
00:02:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:02:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875caef793
00:02:57 Searching Annoy index using 1 thread, search_k = 4500
00:02:57 Annoy recall = 100%
00:03:01 Commencing smooth kNN distance calibration using 1 thread
00:03:07 Initializing from normalized Laplacian + noise
00:03:07 Commencing optimization for 500 epochs, with 66368 positive edges
00:03:13 Optimization finished
[1] "45 0.09"00:03:13 UMAP embedding parameters a = 1.611 b = 0.8844
00:03:13 Read 1203 rows and found 38 numeric columns
00:03:13 Using Annoy for neighbor search, n_neighbors = 45
00:03:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:03:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e74da34
00:03:14 Searching Annoy index using 1 thread, search_k = 4500
00:03:14 Annoy recall = 100%
00:03:17 Commencing smooth kNN distance calibration using 1 thread
00:03:23 Initializing from normalized Laplacian + noise
00:03:24 Commencing optimization for 500 epochs, with 66368 positive edges
00:03:30 Optimization finished
[1] "45 0.1"00:03:30 UMAP embedding parameters a = 1.577 b = 0.8951
00:03:30 Read 1203 rows and found 38 numeric columns
00:03:30 Using Annoy for neighbor search, n_neighbors = 45
00:03:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:03:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c2efa1b
00:03:30 Searching Annoy index using 1 thread, search_k = 4500
00:03:31 Annoy recall = 100%
00:03:34 Commencing smooth kNN distance calibration using 1 thread
00:03:40 Initializing from normalized Laplacian + noise
00:03:40 Commencing optimization for 500 epochs, with 66368 positive edges
00:03:46 Optimization finished
[1] "45 0.11"00:03:47 UMAP embedding parameters a = 1.544 b = 0.9058
00:03:47 Read 1203 rows and found 38 numeric columns
00:03:47 Using Annoy for neighbor search, n_neighbors = 45
00:03:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:03:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87609fb635
00:03:47 Searching Annoy index using 1 thread, search_k = 4500
00:03:47 Annoy recall = 100%
00:03:51 Commencing smooth kNN distance calibration using 1 thread
00:03:57 Initializing from normalized Laplacian + noise
00:03:57 Commencing optimization for 500 epochs, with 66368 positive edges
00:04:03 Optimization finished
[1] "45 0.12"00:04:04 UMAP embedding parameters a = 1.51 b = 0.9165
00:04:04 Read 1203 rows and found 38 numeric columns
00:04:04 Using Annoy for neighbor search, n_neighbors = 45
00:04:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:04:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e76e14d
00:04:04 Searching Annoy index using 1 thread, search_k = 4500
00:04:04 Annoy recall = 100%
00:04:08 Commencing smooth kNN distance calibration using 1 thread
00:04:14 Initializing from normalized Laplacian + noise
00:04:14 Commencing optimization for 500 epochs, with 66368 positive edges
00:04:20 Optimization finished
[1] "45 0.13"00:04:21 UMAP embedding parameters a = 1.478 b = 0.9272
00:04:21 Read 1203 rows and found 38 numeric columns
00:04:21 Using Annoy for neighbor search, n_neighbors = 45
00:04:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:04:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8790663e1
00:04:21 Searching Annoy index using 1 thread, search_k = 4500
00:04:21 Annoy recall = 100%
00:04:25 Commencing smooth kNN distance calibration using 1 thread
00:04:31 Initializing from normalized Laplacian + noise
00:04:31 Commencing optimization for 500 epochs, with 66368 positive edges
00:04:37 Optimization finished
[1] "45 0.14"00:04:38 UMAP embedding parameters a = 1.446 b = 0.938
00:04:38 Read 1203 rows and found 38 numeric columns
00:04:38 Using Annoy for neighbor search, n_neighbors = 45
00:04:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:04:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87384657fe
00:04:38 Searching Annoy index using 1 thread, search_k = 4500
00:04:38 Annoy recall = 100%
00:04:42 Commencing smooth kNN distance calibration using 1 thread
00:04:48 Initializing from normalized Laplacian + noise
00:04:48 Commencing optimization for 500 epochs, with 66368 positive edges
00:04:55 Optimization finished
[1] "45 0.15"00:04:55 UMAP embedding parameters a = 1.414 b = 0.9488
00:04:55 Read 1203 rows and found 38 numeric columns
00:04:55 Using Annoy for neighbor search, n_neighbors = 45
00:04:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:04:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769f56aad
00:04:55 Searching Annoy index using 1 thread, search_k = 4500
00:04:56 Annoy recall = 100%
00:04:59 Commencing smooth kNN distance calibration using 1 thread
00:05:05 Initializing from normalized Laplacian + noise
00:05:05 Commencing optimization for 500 epochs, with 66368 positive edges
00:05:12 Optimization finished
[1] "45 0.16"00:05:12 UMAP embedding parameters a = 1.383 b = 0.9596
00:05:12 Read 1203 rows and found 38 numeric columns
00:05:12 Using Annoy for neighbor search, n_neighbors = 45
00:05:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:05:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757ee51f2
00:05:12 Searching Annoy index using 1 thread, search_k = 4500
00:05:13 Annoy recall = 100%
00:05:16 Commencing smooth kNN distance calibration using 1 thread
00:05:22 Initializing from normalized Laplacian + noise
00:05:22 Commencing optimization for 500 epochs, with 66368 positive edges
00:05:29 Optimization finished
[1] "45 0.17"00:05:29 UMAP embedding parameters a = 1.352 b = 0.9704
00:05:29 Read 1203 rows and found 38 numeric columns
00:05:29 Using Annoy for neighbor search, n_neighbors = 45
00:05:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:05:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e68a941
00:05:29 Searching Annoy index using 1 thread, search_k = 4500
00:05:30 Annoy recall = 100%
00:05:33 Commencing smooth kNN distance calibration using 1 thread
00:05:40 Initializing from normalized Laplacian + noise
00:05:40 Commencing optimization for 500 epochs, with 66368 positive edges
00:05:46 Optimization finished
[1] "45 0.18"00:05:46 UMAP embedding parameters a = 1.321 b = 0.9813
00:05:46 Read 1203 rows and found 38 numeric columns
00:05:46 Using Annoy for neighbor search, n_neighbors = 45
00:05:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:05:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d1cbcff
00:05:46 Searching Annoy index using 1 thread, search_k = 4500
00:05:47 Annoy recall = 100%
00:05:50 Commencing smooth kNN distance calibration using 1 thread
00:05:57 Initializing from normalized Laplacian + noise
00:05:57 Commencing optimization for 500 epochs, with 66368 positive edges
00:06:03 Optimization finished
[1] "45 0.19"00:06:03 UMAP embedding parameters a = 1.292 b = 0.9921
00:06:03 Read 1203 rows and found 38 numeric columns
00:06:03 Using Annoy for neighbor search, n_neighbors = 45
00:06:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:06:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87371e5ad9
00:06:04 Searching Annoy index using 1 thread, search_k = 4500
00:06:04 Annoy recall = 100%
00:06:07 Commencing smooth kNN distance calibration using 1 thread
00:06:14 Initializing from normalized Laplacian + noise
00:06:14 Commencing optimization for 500 epochs, with 66368 positive edges
00:06:20 Optimization finished
[1] "45 0.2"00:06:20 UMAP embedding parameters a = 1.262 b = 1.003
00:06:20 Read 1203 rows and found 38 numeric columns
00:06:20 Using Annoy for neighbor search, n_neighbors = 45
00:06:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:06:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876626f1f5
00:06:21 Searching Annoy index using 1 thread, search_k = 4500
00:06:21 Annoy recall = 100%
00:06:24 Commencing smooth kNN distance calibration using 1 thread
00:06:31 Initializing from normalized Laplacian + noise
00:06:31 Commencing optimization for 500 epochs, with 66368 positive edges
00:06:37 Optimization finished
[1] "46 0"00:06:38 UMAP embedding parameters a = 1.933 b = 0.7905
00:06:38 Read 1203 rows and found 38 numeric columns
00:06:38 Using Annoy for neighbor search, n_neighbors = 46
00:06:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:06:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ff2d1da
00:06:38 Searching Annoy index using 1 thread, search_k = 4600
00:06:38 Annoy recall = 100%
00:06:42 Commencing smooth kNN distance calibration using 1 thread
00:06:48 Initializing from normalized Laplacian + noise
00:06:48 Commencing optimization for 500 epochs, with 67814 positive edges
00:06:55 Optimization finished
[1] "46 0.01"00:06:55 UMAP embedding parameters a = 1.896 b = 0.8006
00:06:55 Read 1203 rows and found 38 numeric columns
00:06:55 Using Annoy for neighbor search, n_neighbors = 46
00:06:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:06:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874817fad7
00:06:55 Searching Annoy index using 1 thread, search_k = 4600
00:06:55 Annoy recall = 100%
00:06:59 Commencing smooth kNN distance calibration using 1 thread
00:07:05 Initializing from normalized Laplacian + noise
00:07:05 Commencing optimization for 500 epochs, with 67814 positive edges
00:07:12 Optimization finished
[1] "46 0.02"00:07:12 UMAP embedding parameters a = 1.859 b = 0.8109
00:07:12 Read 1203 rows and found 38 numeric columns
00:07:12 Using Annoy for neighbor search, n_neighbors = 46
00:07:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:07:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710f36602
00:07:12 Searching Annoy index using 1 thread, search_k = 4600
00:07:13 Annoy recall = 100%
00:07:16 Commencing smooth kNN distance calibration using 1 thread
00:07:23 Initializing from normalized Laplacian + noise
00:07:23 Commencing optimization for 500 epochs, with 67814 positive edges
00:07:29 Optimization finished
[1] "46 0.03"00:07:29 UMAP embedding parameters a = 1.822 b = 0.8212
00:07:29 Read 1203 rows and found 38 numeric columns
00:07:29 Using Annoy for neighbor search, n_neighbors = 46
00:07:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:07:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875893be6a
00:07:29 Searching Annoy index using 1 thread, search_k = 4600
00:07:30 Annoy recall = 100%
00:07:33 Commencing smooth kNN distance calibration using 1 thread
00:07:40 Initializing from normalized Laplacian + noise
00:07:40 Commencing optimization for 500 epochs, with 67814 positive edges
00:07:46 Optimization finished
[1] "46 0.04"00:07:46 UMAP embedding parameters a = 1.786 b = 0.8316
00:07:46 Read 1203 rows and found 38 numeric columns
00:07:46 Using Annoy for neighbor search, n_neighbors = 46
00:07:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:07:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ffcd111
00:07:47 Searching Annoy index using 1 thread, search_k = 4600
00:07:47 Annoy recall = 100%
00:07:50 Commencing smooth kNN distance calibration using 1 thread
00:07:57 Initializing from normalized Laplacian + noise
00:07:57 Commencing optimization for 500 epochs, with 67814 positive edges
00:08:04 Optimization finished
[1] "46 0.05"00:08:04 UMAP embedding parameters a = 1.75 b = 0.8421
00:08:04 Read 1203 rows and found 38 numeric columns
00:08:04 Using Annoy for neighbor search, n_neighbors = 46
00:08:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:08:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fe0f8a
00:08:04 Searching Annoy index using 1 thread, search_k = 4600
00:08:04 Annoy recall = 100%
00:08:08 Commencing smooth kNN distance calibration using 1 thread
00:08:14 Initializing from normalized Laplacian + noise
00:08:14 Commencing optimization for 500 epochs, with 67814 positive edges
00:08:21 Optimization finished
[1] "46 0.06"00:08:21 UMAP embedding parameters a = 1.715 b = 0.8526
00:08:21 Read 1203 rows and found 38 numeric columns
00:08:21 Using Annoy for neighbor search, n_neighbors = 46
00:08:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:08:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87485c236b
00:08:21 Searching Annoy index using 1 thread, search_k = 4600
00:08:22 Annoy recall = 100%
00:08:25 Commencing smooth kNN distance calibration using 1 thread
00:08:32 Initializing from normalized Laplacian + noise
00:08:32 Commencing optimization for 500 epochs, with 67814 positive edges
00:08:38 Optimization finished
[1] "46 0.07"00:08:38 UMAP embedding parameters a = 1.68 b = 0.8631
00:08:38 Read 1203 rows and found 38 numeric columns
00:08:38 Using Annoy for neighbor search, n_neighbors = 46
00:08:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:08:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877db94a36
00:08:39 Searching Annoy index using 1 thread, search_k = 4600
00:08:39 Annoy recall = 100%
00:08:42 Commencing smooth kNN distance calibration using 1 thread
00:08:49 Initializing from normalized Laplacian + noise
00:08:49 Commencing optimization for 500 epochs, with 67814 positive edges
00:08:55 Optimization finished
[1] "46 0.08"00:08:56 UMAP embedding parameters a = 1.645 b = 0.8737
00:08:56 Read 1203 rows and found 38 numeric columns
00:08:56 Using Annoy for neighbor search, n_neighbors = 46
00:08:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:08:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b05d071
00:08:56 Searching Annoy index using 1 thread, search_k = 4600
00:08:56 Annoy recall = 100%
00:09:00 Commencing smooth kNN distance calibration using 1 thread
00:09:06 Initializing from normalized Laplacian + noise
00:09:06 Commencing optimization for 500 epochs, with 67814 positive edges
00:09:13 Optimization finished
[1] "46 0.09"00:09:13 UMAP embedding parameters a = 1.611 b = 0.8844
00:09:13 Read 1203 rows and found 38 numeric columns
00:09:13 Using Annoy for neighbor search, n_neighbors = 46
00:09:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:09:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a62d922
00:09:13 Searching Annoy index using 1 thread, search_k = 4600
00:09:14 Annoy recall = 100%
00:09:17 Commencing smooth kNN distance calibration using 1 thread
00:09:24 Initializing from normalized Laplacian + noise
00:09:24 Commencing optimization for 500 epochs, with 67814 positive edges
00:09:30 Optimization finished
[1] "46 0.1"00:09:30 UMAP embedding parameters a = 1.577 b = 0.8951
00:09:30 Read 1203 rows and found 38 numeric columns
00:09:30 Using Annoy for neighbor search, n_neighbors = 46
00:09:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:09:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773a8e546
00:09:30 Searching Annoy index using 1 thread, search_k = 4600
00:09:31 Annoy recall = 100%
00:09:34 Commencing smooth kNN distance calibration using 1 thread
00:09:41 Initializing from normalized Laplacian + noise
00:09:41 Commencing optimization for 500 epochs, with 67814 positive edges
00:09:47 Optimization finished
[1] "46 0.11"00:09:47 UMAP embedding parameters a = 1.544 b = 0.9058
00:09:47 Read 1203 rows and found 38 numeric columns
00:09:47 Using Annoy for neighbor search, n_neighbors = 46
00:09:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:09:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87146ef818
00:09:48 Searching Annoy index using 1 thread, search_k = 4600
00:09:48 Annoy recall = 100%
00:09:52 Commencing smooth kNN distance calibration using 1 thread
00:09:58 Initializing from normalized Laplacian + noise
00:09:58 Commencing optimization for 500 epochs, with 67814 positive edges
00:10:05 Optimization finished
[1] "46 0.12"00:10:05 UMAP embedding parameters a = 1.51 b = 0.9165
00:10:05 Read 1203 rows and found 38 numeric columns
00:10:05 Using Annoy for neighbor search, n_neighbors = 46
00:10:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:10:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748b7b53c
00:10:05 Searching Annoy index using 1 thread, search_k = 4600
00:10:06 Annoy recall = 100%
00:10:09 Commencing smooth kNN distance calibration using 1 thread
00:10:16 Initializing from normalized Laplacian + noise
00:10:16 Commencing optimization for 500 epochs, with 67814 positive edges
00:10:22 Optimization finished
[1] "46 0.13"00:10:22 UMAP embedding parameters a = 1.478 b = 0.9272
00:10:22 Read 1203 rows and found 38 numeric columns
00:10:22 Using Annoy for neighbor search, n_neighbors = 46
00:10:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:10:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bd21a86
00:10:23 Searching Annoy index using 1 thread, search_k = 4600
00:10:23 Annoy recall = 100%
00:10:26 Commencing smooth kNN distance calibration using 1 thread
00:10:33 Initializing from normalized Laplacian + noise
00:10:33 Commencing optimization for 500 epochs, with 67814 positive edges
00:10:39 Optimization finished
[1] "46 0.14"00:10:39 UMAP embedding parameters a = 1.446 b = 0.938
00:10:39 Read 1203 rows and found 38 numeric columns
00:10:39 Using Annoy for neighbor search, n_neighbors = 46
00:10:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:10:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715fe1b3
00:10:40 Searching Annoy index using 1 thread, search_k = 4600
00:10:40 Annoy recall = 100%
00:10:44 Commencing smooth kNN distance calibration using 1 thread
00:10:50 Initializing from normalized Laplacian + noise
00:10:50 Commencing optimization for 500 epochs, with 67814 positive edges
00:10:57 Optimization finished
[1] "46 0.15"00:10:57 UMAP embedding parameters a = 1.414 b = 0.9488
00:10:57 Read 1203 rows and found 38 numeric columns
00:10:57 Using Annoy for neighbor search, n_neighbors = 46
00:10:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:10:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cdc5077
00:10:57 Searching Annoy index using 1 thread, search_k = 4600
00:10:58 Annoy recall = 100%
00:11:01 Commencing smooth kNN distance calibration using 1 thread
00:11:08 Initializing from normalized Laplacian + noise
00:11:08 Commencing optimization for 500 epochs, with 67814 positive edges
00:11:14 Optimization finished
[1] "46 0.16"00:11:14 UMAP embedding parameters a = 1.383 b = 0.9596
00:11:14 Read 1203 rows and found 38 numeric columns
00:11:14 Using Annoy for neighbor search, n_neighbors = 46
00:11:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:11:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731737aef
00:11:15 Searching Annoy index using 1 thread, search_k = 4600
00:11:15 Annoy recall = 100%
00:11:18 Commencing smooth kNN distance calibration using 1 thread
00:11:25 Initializing from normalized Laplacian + noise
00:11:25 Commencing optimization for 500 epochs, with 67814 positive edges
00:11:31 Optimization finished
[1] "46 0.17"00:11:32 UMAP embedding parameters a = 1.352 b = 0.9704
00:11:32 Read 1203 rows and found 38 numeric columns
00:11:32 Using Annoy for neighbor search, n_neighbors = 46
00:11:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:11:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769f7311d
00:11:32 Searching Annoy index using 1 thread, search_k = 4600
00:11:32 Annoy recall = 100%
00:11:36 Commencing smooth kNN distance calibration using 1 thread
00:11:42 Initializing from normalized Laplacian + noise
00:11:43 Commencing optimization for 500 epochs, with 67814 positive edges
00:11:49 Optimization finished
[1] "46 0.18"00:11:49 UMAP embedding parameters a = 1.321 b = 0.9813
00:11:49 Read 1203 rows and found 38 numeric columns
00:11:49 Using Annoy for neighbor search, n_neighbors = 46
00:11:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:11:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87498b480a
00:11:49 Searching Annoy index using 1 thread, search_k = 4600
00:11:50 Annoy recall = 100%
00:11:53 Commencing smooth kNN distance calibration using 1 thread
00:12:00 Initializing from normalized Laplacian + noise
00:12:00 Commencing optimization for 500 epochs, with 67814 positive edges
00:12:06 Optimization finished
[1] "46 0.19"00:12:07 UMAP embedding parameters a = 1.292 b = 0.9921
00:12:07 Read 1203 rows and found 38 numeric columns
00:12:07 Using Annoy for neighbor search, n_neighbors = 46
00:12:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:12:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fe85523
00:12:07 Searching Annoy index using 1 thread, search_k = 4600
00:12:07 Annoy recall = 100%
00:12:11 Commencing smooth kNN distance calibration using 1 thread
00:12:17 Initializing from normalized Laplacian + noise
00:12:17 Commencing optimization for 500 epochs, with 67814 positive edges
00:12:24 Optimization finished
[1] "46 0.2"00:12:24 UMAP embedding parameters a = 1.262 b = 1.003
00:12:24 Read 1203 rows and found 38 numeric columns
00:12:24 Using Annoy for neighbor search, n_neighbors = 46
00:12:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:12:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736262b38
00:12:24 Searching Annoy index using 1 thread, search_k = 4600
00:12:25 Annoy recall = 100%
00:12:28 Commencing smooth kNN distance calibration using 1 thread
00:12:35 Initializing from normalized Laplacian + noise
00:12:35 Commencing optimization for 500 epochs, with 67814 positive edges
00:12:41 Optimization finished
[1] "47 0"00:12:41 UMAP embedding parameters a = 1.933 b = 0.7905
00:12:41 Read 1203 rows and found 38 numeric columns
00:12:41 Using Annoy for neighbor search, n_neighbors = 47
00:12:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:12:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a2afe3f
00:12:42 Searching Annoy index using 1 thread, search_k = 4700
00:12:42 Annoy recall = 100%
00:12:46 Commencing smooth kNN distance calibration using 1 thread
00:12:52 Initializing from normalized Laplacian + noise
00:12:52 Commencing optimization for 500 epochs, with 69254 positive edges
00:12:59 Optimization finished
[1] "47 0.01"00:12:59 UMAP embedding parameters a = 1.896 b = 0.8006
00:12:59 Read 1203 rows and found 38 numeric columns
00:12:59 Using Annoy for neighbor search, n_neighbors = 47
00:12:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:12:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e5f3670
00:12:59 Searching Annoy index using 1 thread, search_k = 4700
00:13:00 Annoy recall = 100%
00:13:03 Commencing smooth kNN distance calibration using 1 thread
00:13:10 Initializing from normalized Laplacian + noise
00:13:10 Commencing optimization for 500 epochs, with 69254 positive edges
00:13:16 Optimization finished
[1] "47 0.02"00:13:17 UMAP embedding parameters a = 1.859 b = 0.8109
00:13:17 Read 1203 rows and found 38 numeric columns
00:13:17 Using Annoy for neighbor search, n_neighbors = 47
00:13:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:13:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f2c8f1a
00:13:17 Searching Annoy index using 1 thread, search_k = 4700
00:13:17 Annoy recall = 100%
00:13:21 Commencing smooth kNN distance calibration using 1 thread
00:13:27 Initializing from normalized Laplacian + noise
00:13:27 Commencing optimization for 500 epochs, with 69254 positive edges
00:13:34 Optimization finished
[1] "47 0.03"00:13:34 UMAP embedding parameters a = 1.822 b = 0.8212
00:13:34 Read 1203 rows and found 38 numeric columns
00:13:34 Using Annoy for neighbor search, n_neighbors = 47
00:13:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:13:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876271563e
00:13:34 Searching Annoy index using 1 thread, search_k = 4700
00:13:35 Annoy recall = 100%
00:13:38 Commencing smooth kNN distance calibration using 1 thread
00:13:45 Initializing from normalized Laplacian + noise
00:13:45 Commencing optimization for 500 epochs, with 69254 positive edges
00:13:51 Optimization finished
[1] "47 0.04"00:13:52 UMAP embedding parameters a = 1.786 b = 0.8316
00:13:52 Read 1203 rows and found 38 numeric columns
00:13:52 Using Annoy for neighbor search, n_neighbors = 47
00:13:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:13:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875854a11e
00:13:52 Searching Annoy index using 1 thread, search_k = 4700
00:13:52 Annoy recall = 100%
00:13:56 Commencing smooth kNN distance calibration using 1 thread
00:14:02 Initializing from normalized Laplacian + noise
00:14:03 Commencing optimization for 500 epochs, with 69254 positive edges
00:14:09 Optimization finished
[1] "47 0.05"00:14:09 UMAP embedding parameters a = 1.75 b = 0.8421
00:14:09 Read 1203 rows and found 38 numeric columns
00:14:09 Using Annoy for neighbor search, n_neighbors = 47
00:14:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:14:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87171ae10c
00:14:10 Searching Annoy index using 1 thread, search_k = 4700
00:14:10 Annoy recall = 100%
00:14:13 Commencing smooth kNN distance calibration using 1 thread
00:14:20 Initializing from normalized Laplacian + noise
00:14:20 Commencing optimization for 500 epochs, with 69254 positive edges
00:14:27 Optimization finished
[1] "47 0.06"00:14:27 UMAP embedding parameters a = 1.715 b = 0.8526
00:14:27 Read 1203 rows and found 38 numeric columns
00:14:27 Using Annoy for neighbor search, n_neighbors = 47
00:14:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:14:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770d9ff7f
00:14:27 Searching Annoy index using 1 thread, search_k = 4700
00:14:27 Annoy recall = 100%
00:14:31 Commencing smooth kNN distance calibration using 1 thread
00:14:38 Initializing from normalized Laplacian + noise
00:14:38 Commencing optimization for 500 epochs, with 69254 positive edges
00:14:44 Optimization finished
[1] "47 0.07"00:14:44 UMAP embedding parameters a = 1.68 b = 0.8631
00:14:44 Read 1203 rows and found 38 numeric columns
00:14:44 Using Annoy for neighbor search, n_neighbors = 47
00:14:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:14:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755715e1d
00:14:45 Searching Annoy index using 1 thread, search_k = 4700
00:14:45 Annoy recall = 100%
00:14:48 Commencing smooth kNN distance calibration using 1 thread
00:14:55 Initializing from normalized Laplacian + noise
00:14:55 Commencing optimization for 500 epochs, with 69254 positive edges
00:15:02 Optimization finished
[1] "47 0.08"00:15:02 UMAP embedding parameters a = 1.645 b = 0.8737
00:15:02 Read 1203 rows and found 38 numeric columns
00:15:02 Using Annoy for neighbor search, n_neighbors = 47
00:15:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:15:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e393be5
00:15:02 Searching Annoy index using 1 thread, search_k = 4700
00:15:03 Annoy recall = 100%
00:15:06 Commencing smooth kNN distance calibration using 1 thread
00:15:13 Initializing from normalized Laplacian + noise
00:15:13 Commencing optimization for 500 epochs, with 69254 positive edges
00:15:19 Optimization finished
[1] "47 0.09"00:15:20 UMAP embedding parameters a = 1.611 b = 0.8844
00:15:20 Read 1203 rows and found 38 numeric columns
00:15:20 Using Annoy for neighbor search, n_neighbors = 47
00:15:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:15:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875700f174
00:15:20 Searching Annoy index using 1 thread, search_k = 4700
00:15:20 Annoy recall = 100%
00:15:24 Commencing smooth kNN distance calibration using 1 thread
00:15:31 Initializing from normalized Laplacian + noise
00:15:31 Commencing optimization for 500 epochs, with 69254 positive edges
00:15:37 Optimization finished
[1] "47 0.1"00:15:37 UMAP embedding parameters a = 1.577 b = 0.8951
00:15:37 Read 1203 rows and found 38 numeric columns
00:15:37 Using Annoy for neighbor search, n_neighbors = 47
00:15:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:15:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745642ff8
00:15:37 Searching Annoy index using 1 thread, search_k = 4700
00:15:38 Annoy recall = 100%
00:15:41 Commencing smooth kNN distance calibration using 1 thread
00:15:48 Initializing from normalized Laplacian + noise
00:15:48 Commencing optimization for 500 epochs, with 69254 positive edges
00:15:55 Optimization finished
[1] "47 0.11"00:15:55 UMAP embedding parameters a = 1.544 b = 0.9058
00:15:55 Read 1203 rows and found 38 numeric columns
00:15:55 Using Annoy for neighbor search, n_neighbors = 47
00:15:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:15:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87165136bd
00:15:55 Searching Annoy index using 1 thread, search_k = 4700
00:15:56 Annoy recall = 100%
00:15:59 Commencing smooth kNN distance calibration using 1 thread
00:16:06 Initializing from normalized Laplacian + noise
00:16:06 Commencing optimization for 500 epochs, with 69254 positive edges
00:16:12 Optimization finished
[1] "47 0.12"00:16:12 UMAP embedding parameters a = 1.51 b = 0.9165
00:16:12 Read 1203 rows and found 38 numeric columns
00:16:12 Using Annoy for neighbor search, n_neighbors = 47
00:16:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:16:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767f45776
00:16:13 Searching Annoy index using 1 thread, search_k = 4700
00:16:13 Annoy recall = 100%
00:16:17 Commencing smooth kNN distance calibration using 1 thread
00:16:24 Initializing from normalized Laplacian + noise
00:16:24 Commencing optimization for 500 epochs, with 69254 positive edges
00:16:30 Optimization finished
[1] "47 0.13"00:16:30 UMAP embedding parameters a = 1.478 b = 0.9272
00:16:30 Read 1203 rows and found 38 numeric columns
00:16:30 Using Annoy for neighbor search, n_neighbors = 47
00:16:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:16:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871df7ee62
00:16:31 Searching Annoy index using 1 thread, search_k = 4700
00:16:31 Annoy recall = 100%
00:16:34 Commencing smooth kNN distance calibration using 1 thread
00:16:41 Initializing from normalized Laplacian + noise
00:16:41 Commencing optimization for 500 epochs, with 69254 positive edges
00:16:48 Optimization finished
[1] "47 0.14"00:16:48 UMAP embedding parameters a = 1.446 b = 0.938
00:16:48 Read 1203 rows and found 38 numeric columns
00:16:48 Using Annoy for neighbor search, n_neighbors = 47
00:16:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:16:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87464e07ce
00:16:48 Searching Annoy index using 1 thread, search_k = 4700
00:16:49 Annoy recall = 100%
00:16:52 Commencing smooth kNN distance calibration using 1 thread
00:16:59 Initializing from normalized Laplacian + noise
00:16:59 Commencing optimization for 500 epochs, with 69254 positive edges
00:17:05 Optimization finished
[1] "47 0.15"00:17:06 UMAP embedding parameters a = 1.414 b = 0.9488
00:17:06 Read 1203 rows and found 38 numeric columns
00:17:06 Using Annoy for neighbor search, n_neighbors = 47
00:17:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:17:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876af26700
00:17:06 Searching Annoy index using 1 thread, search_k = 4700
00:17:06 Annoy recall = 100%
00:17:10 Commencing smooth kNN distance calibration using 1 thread
00:17:16 Initializing from normalized Laplacian + noise
00:17:17 Commencing optimization for 500 epochs, with 69254 positive edges
00:17:23 Optimization finished
[1] "47 0.16"00:17:23 UMAP embedding parameters a = 1.383 b = 0.9596
00:17:23 Read 1203 rows and found 38 numeric columns
00:17:23 Using Annoy for neighbor search, n_neighbors = 47
00:17:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:17:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87665411cd
00:17:23 Searching Annoy index using 1 thread, search_k = 4700
00:17:24 Annoy recall = 100%
00:17:27 Commencing smooth kNN distance calibration using 1 thread
00:17:34 Initializing from normalized Laplacian + noise
00:17:34 Commencing optimization for 500 epochs, with 69254 positive edges
00:17:40 Optimization finished
[1] "47 0.17"00:17:41 UMAP embedding parameters a = 1.352 b = 0.9704
00:17:41 Read 1203 rows and found 38 numeric columns
00:17:41 Using Annoy for neighbor search, n_neighbors = 47
00:17:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:17:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744075204
00:17:41 Searching Annoy index using 1 thread, search_k = 4700
00:17:41 Annoy recall = 100%
00:17:45 Commencing smooth kNN distance calibration using 1 thread
00:17:52 Initializing from normalized Laplacian + noise
00:17:52 Commencing optimization for 500 epochs, with 69254 positive edges
00:17:58 Optimization finished
[1] "47 0.18"00:17:58 UMAP embedding parameters a = 1.321 b = 0.9813
00:17:58 Read 1203 rows and found 38 numeric columns
00:17:58 Using Annoy for neighbor search, n_neighbors = 47
00:17:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:17:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f83771
00:17:59 Searching Annoy index using 1 thread, search_k = 4700
00:17:59 Annoy recall = 100%
00:18:02 Commencing smooth kNN distance calibration using 1 thread
00:18:09 Initializing from normalized Laplacian + noise
00:18:09 Commencing optimization for 500 epochs, with 69254 positive edges
00:18:16 Optimization finished
[1] "47 0.19"00:18:16 UMAP embedding parameters a = 1.292 b = 0.9921
00:18:16 Read 1203 rows and found 38 numeric columns
00:18:16 Using Annoy for neighbor search, n_neighbors = 47
00:18:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:18:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730b6eaef
00:18:16 Searching Annoy index using 1 thread, search_k = 4700
00:18:17 Annoy recall = 100%
00:18:20 Commencing smooth kNN distance calibration using 1 thread
00:18:27 Initializing from normalized Laplacian + noise
00:18:27 Commencing optimization for 500 epochs, with 69254 positive edges
00:18:33 Optimization finished
[1] "47 0.2"00:18:33 UMAP embedding parameters a = 1.262 b = 1.003
00:18:33 Read 1203 rows and found 38 numeric columns
00:18:33 Using Annoy for neighbor search, n_neighbors = 47
00:18:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:18:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737b0374a
00:18:34 Searching Annoy index using 1 thread, search_k = 4700
00:18:34 Annoy recall = 100%
00:18:37 Commencing smooth kNN distance calibration using 1 thread
00:18:44 Initializing from normalized Laplacian + noise
00:18:44 Commencing optimization for 500 epochs, with 69254 positive edges
00:18:51 Optimization finished
[1] "48 0"00:18:51 UMAP embedding parameters a = 1.933 b = 0.7905
00:18:51 Read 1203 rows and found 38 numeric columns
00:18:51 Using Annoy for neighbor search, n_neighbors = 48
00:18:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:18:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a672f8a
00:18:51 Searching Annoy index using 1 thread, search_k = 4800
00:18:52 Annoy recall = 100%
00:18:55 Commencing smooth kNN distance calibration using 1 thread
00:19:02 Initializing from normalized Laplacian + noise
00:19:02 Commencing optimization for 500 epochs, with 70626 positive edges
00:19:08 Optimization finished
[1] "48 0.01"00:19:09 UMAP embedding parameters a = 1.896 b = 0.8006
00:19:09 Read 1203 rows and found 38 numeric columns
00:19:09 Using Annoy for neighbor search, n_neighbors = 48
00:19:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:19:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87796ea02b
00:19:09 Searching Annoy index using 1 thread, search_k = 4800
00:19:09 Annoy recall = 100%
00:19:13 Commencing smooth kNN distance calibration using 1 thread
00:19:20 Initializing from normalized Laplacian + noise
00:19:20 Commencing optimization for 500 epochs, with 70626 positive edges
00:19:26 Optimization finished
[1] "48 0.02"00:19:26 UMAP embedding parameters a = 1.859 b = 0.8109
00:19:26 Read 1203 rows and found 38 numeric columns
00:19:26 Using Annoy for neighbor search, n_neighbors = 48
00:19:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:19:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738251d0
00:19:27 Searching Annoy index using 1 thread, search_k = 4800
00:19:27 Annoy recall = 100%
00:19:30 Commencing smooth kNN distance calibration using 1 thread
00:19:37 Initializing from normalized Laplacian + noise
00:19:37 Commencing optimization for 500 epochs, with 70626 positive edges
00:19:44 Optimization finished
[1] "48 0.03"00:19:44 UMAP embedding parameters a = 1.822 b = 0.8212
00:19:44 Read 1203 rows and found 38 numeric columns
00:19:44 Using Annoy for neighbor search, n_neighbors = 48
00:19:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:19:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871bc7113d
00:19:44 Searching Annoy index using 1 thread, search_k = 4800
00:19:45 Annoy recall = 100%
00:19:48 Commencing smooth kNN distance calibration using 1 thread
00:19:55 Initializing from normalized Laplacian + noise
00:19:55 Commencing optimization for 500 epochs, with 70626 positive edges
00:20:01 Optimization finished
[1] "48 0.04"00:20:02 UMAP embedding parameters a = 1.786 b = 0.8316
00:20:02 Read 1203 rows and found 38 numeric columns
00:20:02 Using Annoy for neighbor search, n_neighbors = 48
00:20:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:20:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87664af0a2
00:20:02 Searching Annoy index using 1 thread, search_k = 4800
00:20:02 Annoy recall = 100%
00:20:06 Commencing smooth kNN distance calibration using 1 thread
00:20:13 Initializing from normalized Laplacian + noise
00:20:13 Commencing optimization for 500 epochs, with 70626 positive edges
00:20:19 Optimization finished
[1] "48 0.05"00:20:19 UMAP embedding parameters a = 1.75 b = 0.8421
00:20:19 Read 1203 rows and found 38 numeric columns
00:20:19 Using Annoy for neighbor search, n_neighbors = 48
00:20:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:20:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734f5ccbf
00:20:20 Searching Annoy index using 1 thread, search_k = 4800
00:20:20 Annoy recall = 100%
00:20:24 Commencing smooth kNN distance calibration using 1 thread
00:20:30 Initializing from normalized Laplacian + noise
00:20:30 Commencing optimization for 500 epochs, with 70626 positive edges
00:20:37 Optimization finished
[1] "48 0.06"00:20:37 UMAP embedding parameters a = 1.715 b = 0.8526
00:20:37 Read 1203 rows and found 38 numeric columns
00:20:37 Using Annoy for neighbor search, n_neighbors = 48
00:20:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:20:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875be425b
00:20:37 Searching Annoy index using 1 thread, search_k = 4800
00:20:38 Annoy recall = 100%
00:20:41 Commencing smooth kNN distance calibration using 1 thread
00:20:48 Initializing from normalized Laplacian + noise
00:20:48 Commencing optimization for 500 epochs, with 70626 positive edges
00:20:55 Optimization finished
[1] "48 0.07"00:20:55 UMAP embedding parameters a = 1.68 b = 0.8631
00:20:55 Read 1203 rows and found 38 numeric columns
00:20:55 Using Annoy for neighbor search, n_neighbors = 48
00:20:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:20:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fd638ac
00:20:55 Searching Annoy index using 1 thread, search_k = 4800
00:20:55 Annoy recall = 100%
00:20:59 Commencing smooth kNN distance calibration using 1 thread
00:21:06 Initializing from normalized Laplacian + noise
00:21:06 Commencing optimization for 500 epochs, with 70626 positive edges
00:21:12 Optimization finished
[1] "48 0.08"00:21:13 UMAP embedding parameters a = 1.645 b = 0.8737
00:21:13 Read 1203 rows and found 38 numeric columns
00:21:13 Using Annoy for neighbor search, n_neighbors = 48
00:21:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:21:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744de21e2
00:21:13 Searching Annoy index using 1 thread, search_k = 4800
00:21:13 Annoy recall = 100%
00:21:17 Commencing smooth kNN distance calibration using 1 thread
00:21:24 Initializing from normalized Laplacian + noise
00:21:24 Commencing optimization for 500 epochs, with 70626 positive edges
00:21:30 Optimization finished
[1] "48 0.09"00:21:30 UMAP embedding parameters a = 1.611 b = 0.8844
00:21:30 Read 1203 rows and found 38 numeric columns
00:21:30 Using Annoy for neighbor search, n_neighbors = 48
00:21:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:21:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873be46d93
00:21:31 Searching Annoy index using 1 thread, search_k = 4800
00:21:31 Annoy recall = 100%
00:21:34 Commencing smooth kNN distance calibration using 1 thread
00:21:41 Initializing from normalized Laplacian + noise
00:21:41 Commencing optimization for 500 epochs, with 70626 positive edges
00:21:48 Optimization finished
[1] "48 0.1"00:21:48 UMAP embedding parameters a = 1.577 b = 0.8951
00:21:48 Read 1203 rows and found 38 numeric columns
00:21:48 Using Annoy for neighbor search, n_neighbors = 48
00:21:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:21:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a0136ec
00:21:48 Searching Annoy index using 1 thread, search_k = 4800
00:21:49 Annoy recall = 100%
00:21:52 Commencing smooth kNN distance calibration using 1 thread
00:21:59 Initializing from normalized Laplacian + noise
00:21:59 Commencing optimization for 500 epochs, with 70626 positive edges
00:22:06 Optimization finished
[1] "48 0.11"00:22:06 UMAP embedding parameters a = 1.544 b = 0.9058
00:22:06 Read 1203 rows and found 38 numeric columns
00:22:06 Using Annoy for neighbor search, n_neighbors = 48
00:22:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:22:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87333d5853
00:22:06 Searching Annoy index using 1 thread, search_k = 4800
00:22:07 Annoy recall = 100%
00:22:10 Commencing smooth kNN distance calibration using 1 thread
00:22:17 Initializing from normalized Laplacian + noise
00:22:17 Commencing optimization for 500 epochs, with 70626 positive edges
00:22:23 Optimization finished
[1] "48 0.12"00:22:24 UMAP embedding parameters a = 1.51 b = 0.9165
00:22:24 Read 1203 rows and found 38 numeric columns
00:22:24 Using Annoy for neighbor search, n_neighbors = 48
00:22:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:22:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b10fcad
00:22:24 Searching Annoy index using 1 thread, search_k = 4800
00:22:24 Annoy recall = 100%
00:22:28 Commencing smooth kNN distance calibration using 1 thread
00:22:35 Initializing from normalized Laplacian + noise
00:22:35 Commencing optimization for 500 epochs, with 70626 positive edges
00:22:41 Optimization finished
[1] "48 0.13"00:22:41 UMAP embedding parameters a = 1.478 b = 0.9272
00:22:41 Read 1203 rows and found 38 numeric columns
00:22:41 Using Annoy for neighbor search, n_neighbors = 48
00:22:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:22:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c728d2a
00:22:42 Searching Annoy index using 1 thread, search_k = 4800
00:22:42 Annoy recall = 100%
00:22:46 Commencing smooth kNN distance calibration using 1 thread
00:22:52 Initializing from normalized Laplacian + noise
00:22:53 Commencing optimization for 500 epochs, with 70626 positive edges
00:22:59 Optimization finished
[1] "48 0.14"00:22:59 UMAP embedding parameters a = 1.446 b = 0.938
00:22:59 Read 1203 rows and found 38 numeric columns
00:22:59 Using Annoy for neighbor search, n_neighbors = 48
00:22:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:23:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b91f971
00:23:00 Searching Annoy index using 1 thread, search_k = 4800
00:23:00 Annoy recall = 100%
00:23:03 Commencing smooth kNN distance calibration using 1 thread
00:23:10 Initializing from normalized Laplacian + noise
00:23:10 Commencing optimization for 500 epochs, with 70626 positive edges
00:23:17 Optimization finished
[1] "48 0.15"00:23:17 UMAP embedding parameters a = 1.414 b = 0.9488
00:23:17 Read 1203 rows and found 38 numeric columns
00:23:17 Using Annoy for neighbor search, n_neighbors = 48
00:23:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:23:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87122bddba
00:23:17 Searching Annoy index using 1 thread, search_k = 4800
00:23:18 Annoy recall = 100%
00:23:21 Commencing smooth kNN distance calibration using 1 thread
00:23:28 Initializing from normalized Laplacian + noise
00:23:28 Commencing optimization for 500 epochs, with 70626 positive edges
00:23:35 Optimization finished
[1] "48 0.16"00:23:35 UMAP embedding parameters a = 1.383 b = 0.9596
00:23:35 Read 1203 rows and found 38 numeric columns
00:23:35 Using Annoy for neighbor search, n_neighbors = 48
00:23:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:23:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d4c8ca9
00:23:35 Searching Annoy index using 1 thread, search_k = 4800
00:23:36 Annoy recall = 100%
00:23:39 Commencing smooth kNN distance calibration using 1 thread
00:23:46 Initializing from normalized Laplacian + noise
00:23:46 Commencing optimization for 500 epochs, with 70626 positive edges
00:23:53 Optimization finished
[1] "48 0.17"00:23:53 UMAP embedding parameters a = 1.352 b = 0.9704
00:23:53 Read 1203 rows and found 38 numeric columns
00:23:53 Using Annoy for neighbor search, n_neighbors = 48
00:23:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:23:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876103578e
00:23:53 Searching Annoy index using 1 thread, search_k = 4800
00:23:53 Annoy recall = 100%
00:23:57 Commencing smooth kNN distance calibration using 1 thread
00:24:04 Initializing from normalized Laplacian + noise
00:24:04 Commencing optimization for 500 epochs, with 70626 positive edges
00:24:10 Optimization finished
[1] "48 0.18"00:24:11 UMAP embedding parameters a = 1.321 b = 0.9813
00:24:11 Read 1203 rows and found 38 numeric columns
00:24:11 Using Annoy for neighbor search, n_neighbors = 48
00:24:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:24:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876065199f
00:24:11 Searching Annoy index using 1 thread, search_k = 4800
00:24:11 Annoy recall = 100%
00:24:15 Commencing smooth kNN distance calibration using 1 thread
00:24:22 Initializing from normalized Laplacian + noise
00:24:22 Commencing optimization for 500 epochs, with 70626 positive edges
00:24:28 Optimization finished
[1] "48 0.19"00:24:29 UMAP embedding parameters a = 1.292 b = 0.9921
00:24:29 Read 1203 rows and found 38 numeric columns
00:24:29 Using Annoy for neighbor search, n_neighbors = 48
00:24:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:24:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744d7e1e
00:24:29 Searching Annoy index using 1 thread, search_k = 4800
00:24:29 Annoy recall = 100%
00:24:33 Commencing smooth kNN distance calibration using 1 thread
00:24:40 Initializing from normalized Laplacian + noise
00:24:40 Commencing optimization for 500 epochs, with 70626 positive edges
00:24:46 Optimization finished
[1] "48 0.2"00:24:46 UMAP embedding parameters a = 1.262 b = 1.003
00:24:46 Read 1203 rows and found 38 numeric columns
00:24:46 Using Annoy for neighbor search, n_neighbors = 48
00:24:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:24:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726678786
00:24:47 Searching Annoy index using 1 thread, search_k = 4800
00:24:47 Annoy recall = 100%
00:24:51 Commencing smooth kNN distance calibration using 1 thread
00:24:58 Initializing from normalized Laplacian + noise
00:24:58 Commencing optimization for 500 epochs, with 70626 positive edges
00:25:04 Optimization finished
[1] "49 0"00:25:04 UMAP embedding parameters a = 1.933 b = 0.7905
00:25:04 Read 1203 rows and found 38 numeric columns
00:25:04 Using Annoy for neighbor search, n_neighbors = 49
00:25:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776b6505c
00:25:05 Searching Annoy index using 1 thread, search_k = 4900
00:25:05 Annoy recall = 100%
00:25:09 Commencing smooth kNN distance calibration using 1 thread
00:25:15 Initializing from normalized Laplacian + noise
00:25:16 Commencing optimization for 500 epochs, with 71994 positive edges
00:25:22 Optimization finished
[1] "49 0.01"00:25:22 UMAP embedding parameters a = 1.896 b = 0.8006
00:25:22 Read 1203 rows and found 38 numeric columns
00:25:22 Using Annoy for neighbor search, n_neighbors = 49
00:25:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c41d594
00:25:23 Searching Annoy index using 1 thread, search_k = 4900
00:25:23 Annoy recall = 100%
00:25:26 Commencing smooth kNN distance calibration using 1 thread
00:25:34 Initializing from normalized Laplacian + noise
00:25:34 Commencing optimization for 500 epochs, with 71994 positive edges
00:25:40 Optimization finished
[1] "49 0.02"00:25:40 UMAP embedding parameters a = 1.859 b = 0.8109
00:25:40 Read 1203 rows and found 38 numeric columns
00:25:40 Using Annoy for neighbor search, n_neighbors = 49
00:25:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87445f75e8
00:25:41 Searching Annoy index using 1 thread, search_k = 4900
00:25:41 Annoy recall = 100%
00:25:44 Commencing smooth kNN distance calibration using 1 thread
00:25:51 Initializing from normalized Laplacian + noise
00:25:51 Commencing optimization for 500 epochs, with 71994 positive edges
00:25:58 Optimization finished
[1] "49 0.03"00:25:58 UMAP embedding parameters a = 1.822 b = 0.8212
00:25:58 Read 1203 rows and found 38 numeric columns
00:25:58 Using Annoy for neighbor search, n_neighbors = 49
00:25:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d04582a
00:25:59 Searching Annoy index using 1 thread, search_k = 4900
00:25:59 Annoy recall = 100%
00:26:02 Commencing smooth kNN distance calibration using 1 thread
00:26:09 Initializing from normalized Laplacian + noise
00:26:09 Commencing optimization for 500 epochs, with 71994 positive edges
00:26:16 Optimization finished
[1] "49 0.04"00:26:16 UMAP embedding parameters a = 1.786 b = 0.8316
00:26:16 Read 1203 rows and found 38 numeric columns
00:26:16 Using Annoy for neighbor search, n_neighbors = 49
00:26:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:26:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757343c95
00:26:17 Searching Annoy index using 1 thread, search_k = 4900
00:26:17 Annoy recall = 100%
00:26:20 Commencing smooth kNN distance calibration using 1 thread
00:26:27 Initializing from normalized Laplacian + noise
00:26:27 Commencing optimization for 500 epochs, with 71994 positive edges
00:26:34 Optimization finished
[1] "49 0.05"00:26:34 UMAP embedding parameters a = 1.75 b = 0.8421
00:26:34 Read 1203 rows and found 38 numeric columns
00:26:34 Using Annoy for neighbor search, n_neighbors = 49
00:26:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:26:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ab387b5
00:26:35 Searching Annoy index using 1 thread, search_k = 4900
00:26:35 Annoy recall = 100%
00:26:39 Commencing smooth kNN distance calibration using 1 thread
00:26:45 Initializing from normalized Laplacian + noise
00:26:46 Commencing optimization for 500 epochs, with 71994 positive edges
00:26:52 Optimization finished
[1] "49 0.06"00:26:52 UMAP embedding parameters a = 1.715 b = 0.8526
00:26:52 Read 1203 rows and found 38 numeric columns
00:26:52 Using Annoy for neighbor search, n_neighbors = 49
00:26:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:26:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710baa2e
00:26:53 Searching Annoy index using 1 thread, search_k = 4900
00:26:53 Annoy recall = 100%
00:26:56 Commencing smooth kNN distance calibration using 1 thread
00:27:03 Initializing from normalized Laplacian + noise
00:27:03 Commencing optimization for 500 epochs, with 71994 positive edges
00:27:10 Optimization finished
[1] "49 0.07"00:27:10 UMAP embedding parameters a = 1.68 b = 0.8631
00:27:10 Read 1203 rows and found 38 numeric columns
00:27:10 Using Annoy for neighbor search, n_neighbors = 49
00:27:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:27:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d2c7406
00:27:11 Searching Annoy index using 1 thread, search_k = 4900
00:27:11 Annoy recall = 100%
00:27:15 Commencing smooth kNN distance calibration using 1 thread
00:27:22 Initializing from normalized Laplacian + noise
00:27:22 Commencing optimization for 500 epochs, with 71994 positive edges
00:27:28 Optimization finished
[1] "49 0.08"00:27:28 UMAP embedding parameters a = 1.645 b = 0.8737
00:27:28 Read 1203 rows and found 38 numeric columns
00:27:28 Using Annoy for neighbor search, n_neighbors = 49
00:27:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:27:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b6a72a4
00:27:29 Searching Annoy index using 1 thread, search_k = 4900
00:27:29 Annoy recall = 100%
00:27:33 Commencing smooth kNN distance calibration using 1 thread
00:27:40 Initializing from normalized Laplacian + noise
00:27:40 Commencing optimization for 500 epochs, with 71994 positive edges
00:27:46 Optimization finished
[1] "49 0.09"00:27:46 UMAP embedding parameters a = 1.611 b = 0.8844
00:27:46 Read 1203 rows and found 38 numeric columns
00:27:46 Using Annoy for neighbor search, n_neighbors = 49
00:27:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:27:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738bbe179
00:27:47 Searching Annoy index using 1 thread, search_k = 4900
00:27:47 Annoy recall = 100%
00:27:51 Commencing smooth kNN distance calibration using 1 thread
00:27:58 Initializing from normalized Laplacian + noise
00:27:58 Commencing optimization for 500 epochs, with 71994 positive edges
00:28:04 Optimization finished
[1] "49 0.1"00:28:04 UMAP embedding parameters a = 1.577 b = 0.8951
00:28:04 Read 1203 rows and found 38 numeric columns
00:28:04 Using Annoy for neighbor search, n_neighbors = 49
00:28:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:28:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877793a390
00:28:05 Searching Annoy index using 1 thread, search_k = 4900
00:28:05 Annoy recall = 100%
00:28:09 Commencing smooth kNN distance calibration using 1 thread
00:28:16 Initializing from normalized Laplacian + noise
00:28:16 Commencing optimization for 500 epochs, with 71994 positive edges
00:28:22 Optimization finished
[1] "49 0.11"00:28:23 UMAP embedding parameters a = 1.544 b = 0.9058
00:28:23 Read 1203 rows and found 38 numeric columns
00:28:23 Using Annoy for neighbor search, n_neighbors = 49
00:28:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:28:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754d912d0
00:28:23 Searching Annoy index using 1 thread, search_k = 4900
00:28:23 Annoy recall = 100%
00:28:27 Commencing smooth kNN distance calibration using 1 thread
00:28:34 Initializing from normalized Laplacian + noise
00:28:34 Commencing optimization for 500 epochs, with 71994 positive edges
00:28:41 Optimization finished
[1] "49 0.12"00:28:41 UMAP embedding parameters a = 1.51 b = 0.9165
00:28:41 Read 1203 rows and found 38 numeric columns
00:28:41 Using Annoy for neighbor search, n_neighbors = 49
00:28:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:28:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c3e3349
00:28:41 Searching Annoy index using 1 thread, search_k = 4900
00:28:42 Annoy recall = 100%
00:28:45 Commencing smooth kNN distance calibration using 1 thread
00:28:52 Initializing from normalized Laplacian + noise
00:28:52 Commencing optimization for 500 epochs, with 71994 positive edges
00:28:59 Optimization finished
[1] "49 0.13"00:28:59 UMAP embedding parameters a = 1.478 b = 0.9272
00:28:59 Read 1203 rows and found 38 numeric columns
00:28:59 Using Annoy for neighbor search, n_neighbors = 49
00:28:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:28:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87135ab4ce
00:28:59 Searching Annoy index using 1 thread, search_k = 4900
00:29:00 Annoy recall = 100%
00:29:03 Commencing smooth kNN distance calibration using 1 thread
00:29:10 Initializing from normalized Laplacian + noise
00:29:10 Commencing optimization for 500 epochs, with 71994 positive edges
00:29:17 Optimization finished
[1] "49 0.14"00:29:17 UMAP embedding parameters a = 1.446 b = 0.938
00:29:17 Read 1203 rows and found 38 numeric columns
00:29:17 Using Annoy for neighbor search, n_neighbors = 49
00:29:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:29:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b240372
00:29:17 Searching Annoy index using 1 thread, search_k = 4900
00:29:18 Annoy recall = 100%
00:29:21 Commencing smooth kNN distance calibration using 1 thread
00:29:28 Initializing from normalized Laplacian + noise
00:29:28 Commencing optimization for 500 epochs, with 71994 positive edges
00:29:35 Optimization finished
[1] "49 0.15"00:29:35 UMAP embedding parameters a = 1.414 b = 0.9488
00:29:35 Read 1203 rows and found 38 numeric columns
00:29:35 Using Annoy for neighbor search, n_neighbors = 49
00:29:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:29:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771340009
00:29:35 Searching Annoy index using 1 thread, search_k = 4900
00:29:36 Annoy recall = 100%
00:29:39 Commencing smooth kNN distance calibration using 1 thread
00:29:46 Initializing from normalized Laplacian + noise
00:29:47 Commencing optimization for 500 epochs, with 71994 positive edges
00:29:53 Optimization finished
[1] "49 0.16"00:29:53 UMAP embedding parameters a = 1.383 b = 0.9596
00:29:53 Read 1203 rows and found 38 numeric columns
00:29:53 Using Annoy for neighbor search, n_neighbors = 49
00:29:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:29:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871918f729
00:29:54 Searching Annoy index using 1 thread, search_k = 4900
00:29:54 Annoy recall = 100%
00:29:57 Commencing smooth kNN distance calibration using 1 thread
00:30:05 Initializing from normalized Laplacian + noise
00:30:05 Commencing optimization for 500 epochs, with 71994 positive edges
00:30:11 Optimization finished
[1] "49 0.17"00:30:11 UMAP embedding parameters a = 1.352 b = 0.9704
00:30:11 Read 1203 rows and found 38 numeric columns
00:30:11 Using Annoy for neighbor search, n_neighbors = 49
00:30:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:30:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876afa3c1f
00:30:12 Searching Annoy index using 1 thread, search_k = 4900
00:30:12 Annoy recall = 100%
00:30:16 Commencing smooth kNN distance calibration using 1 thread
00:30:23 Initializing from normalized Laplacian + noise
00:30:23 Commencing optimization for 500 epochs, with 71994 positive edges
00:30:29 Optimization finished
[1] "49 0.18"00:30:30 UMAP embedding parameters a = 1.321 b = 0.9813
00:30:30 Read 1203 rows and found 38 numeric columns
00:30:30 Using Annoy for neighbor search, n_neighbors = 49
00:30:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:30:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87361221eb
00:30:30 Searching Annoy index using 1 thread, search_k = 4900
00:30:30 Annoy recall = 100%
00:30:34 Commencing smooth kNN distance calibration using 1 thread
00:30:41 Initializing from normalized Laplacian + noise
00:30:41 Commencing optimization for 500 epochs, with 71994 positive edges
00:30:48 Optimization finished
[1] "49 0.19"00:30:48 UMAP embedding parameters a = 1.292 b = 0.9921
00:30:48 Read 1203 rows and found 38 numeric columns
00:30:48 Using Annoy for neighbor search, n_neighbors = 49
00:30:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:30:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754fd64bc
00:30:48 Searching Annoy index using 1 thread, search_k = 4900
00:30:49 Annoy recall = 100%
00:30:52 Commencing smooth kNN distance calibration using 1 thread
00:30:59 Initializing from normalized Laplacian + noise
00:30:59 Commencing optimization for 500 epochs, with 71994 positive edges
00:31:06 Optimization finished
[1] "49 0.2"00:31:06 UMAP embedding parameters a = 1.262 b = 1.003
00:31:06 Read 1203 rows and found 38 numeric columns
00:31:06 Using Annoy for neighbor search, n_neighbors = 49
00:31:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:31:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744fb730b
00:31:06 Searching Annoy index using 1 thread, search_k = 4900
00:31:07 Annoy recall = 100%
00:31:10 Commencing smooth kNN distance calibration using 1 thread
00:31:17 Initializing from normalized Laplacian + noise
00:31:17 Commencing optimization for 500 epochs, with 71994 positive edges
00:31:24 Optimization finished
[1] "50 0"00:31:24 UMAP embedding parameters a = 1.933 b = 0.7905
00:31:24 Read 1203 rows and found 38 numeric columns
00:31:24 Using Annoy for neighbor search, n_neighbors = 50
00:31:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:31:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87694f7a3e
00:31:25 Searching Annoy index using 1 thread, search_k = 5000
00:31:25 Annoy recall = 100%
00:31:28 Commencing smooth kNN distance calibration using 1 thread
00:31:36 Initializing from normalized Laplacian + noise
00:31:36 Commencing optimization for 500 epochs, with 73404 positive edges
00:31:42 Optimization finished
[1] "50 0.01"00:31:43 UMAP embedding parameters a = 1.896 b = 0.8006
00:31:43 Read 1203 rows and found 38 numeric columns
00:31:43 Using Annoy for neighbor search, n_neighbors = 50
00:31:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:31:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87500e616a
00:31:43 Searching Annoy index using 1 thread, search_k = 5000
00:31:43 Annoy recall = 100%
00:31:47 Commencing smooth kNN distance calibration using 1 thread
00:31:54 Initializing from normalized Laplacian + noise
00:31:54 Commencing optimization for 500 epochs, with 73404 positive edges
00:32:00 Optimization finished
[1] "50 0.02"00:32:01 UMAP embedding parameters a = 1.859 b = 0.8109
00:32:01 Read 1203 rows and found 38 numeric columns
00:32:01 Using Annoy for neighbor search, n_neighbors = 50
00:32:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:32:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716e0035
00:32:01 Searching Annoy index using 1 thread, search_k = 5000
00:32:01 Annoy recall = 100%
00:32:05 Commencing smooth kNN distance calibration using 1 thread
00:32:12 Initializing from normalized Laplacian + noise
00:32:12 Commencing optimization for 500 epochs, with 73404 positive edges
00:32:19 Optimization finished
[1] "50 0.03"00:32:19 UMAP embedding parameters a = 1.822 b = 0.8212
00:32:19 Read 1203 rows and found 38 numeric columns
00:32:19 Using Annoy for neighbor search, n_neighbors = 50
00:32:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:32:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774e173af
00:32:19 Searching Annoy index using 1 thread, search_k = 5000
00:32:20 Annoy recall = 100%
00:32:23 Commencing smooth kNN distance calibration using 1 thread
00:32:30 Initializing from normalized Laplacian + noise
00:32:30 Commencing optimization for 500 epochs, with 73404 positive edges
00:32:37 Optimization finished
[1] "50 0.04"00:32:37 UMAP embedding parameters a = 1.786 b = 0.8316
00:32:37 Read 1203 rows and found 38 numeric columns
00:32:37 Using Annoy for neighbor search, n_neighbors = 50
00:32:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:32:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87623a3f24
00:32:38 Searching Annoy index using 1 thread, search_k = 5000
00:32:38 Annoy recall = 100%
00:32:42 Commencing smooth kNN distance calibration using 1 thread
00:32:49 Initializing from normalized Laplacian + noise
00:32:49 Commencing optimization for 500 epochs, with 73404 positive edges
00:32:55 Optimization finished
[1] "50 0.05"00:32:56 UMAP embedding parameters a = 1.75 b = 0.8421
00:32:56 Read 1203 rows and found 38 numeric columns
00:32:56 Using Annoy for neighbor search, n_neighbors = 50
00:32:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:32:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872eba8cde
00:32:56 Searching Annoy index using 1 thread, search_k = 5000
00:32:56 Annoy recall = 100%
00:33:00 Commencing smooth kNN distance calibration using 1 thread
00:33:07 Initializing from normalized Laplacian + noise
00:33:07 Commencing optimization for 500 epochs, with 73404 positive edges
00:33:14 Optimization finished
[1] "50 0.06"00:33:14 UMAP embedding parameters a = 1.715 b = 0.8526
00:33:14 Read 1203 rows and found 38 numeric columns
00:33:14 Using Annoy for neighbor search, n_neighbors = 50
00:33:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755e4cb3e
00:33:14 Searching Annoy index using 1 thread, search_k = 5000
00:33:15 Annoy recall = 100%
00:33:18 Commencing smooth kNN distance calibration using 1 thread
00:33:25 Initializing from normalized Laplacian + noise
00:33:25 Commencing optimization for 500 epochs, with 73404 positive edges
00:33:32 Optimization finished
[1] "50 0.07"00:33:32 UMAP embedding parameters a = 1.68 b = 0.8631
00:33:32 Read 1203 rows and found 38 numeric columns
00:33:32 Using Annoy for neighbor search, n_neighbors = 50
00:33:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87429f58c3
00:33:33 Searching Annoy index using 1 thread, search_k = 5000
00:33:33 Annoy recall = 100%
00:33:37 Commencing smooth kNN distance calibration using 1 thread
00:33:44 Initializing from normalized Laplacian + noise
00:33:44 Commencing optimization for 500 epochs, with 73404 positive edges
00:33:50 Optimization finished
[1] "50 0.08"00:33:51 UMAP embedding parameters a = 1.645 b = 0.8737
00:33:51 Read 1203 rows and found 38 numeric columns
00:33:51 Using Annoy for neighbor search, n_neighbors = 50
00:33:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733080afc
00:33:51 Searching Annoy index using 1 thread, search_k = 5000
00:33:51 Annoy recall = 100%
00:33:55 Commencing smooth kNN distance calibration using 1 thread
00:34:02 Initializing from normalized Laplacian + noise
00:34:02 Commencing optimization for 500 epochs, with 73404 positive edges
00:34:09 Optimization finished
[1] "50 0.09"00:34:09 UMAP embedding parameters a = 1.611 b = 0.8844
00:34:09 Read 1203 rows and found 38 numeric columns
00:34:09 Using Annoy for neighbor search, n_neighbors = 50
00:34:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:34:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c4c52c4
00:34:09 Searching Annoy index using 1 thread, search_k = 5000
00:34:10 Annoy recall = 100%
00:34:13 Commencing smooth kNN distance calibration using 1 thread
00:34:20 Initializing from normalized Laplacian + noise
00:34:20 Commencing optimization for 500 epochs, with 73404 positive edges
00:34:27 Optimization finished
[1] "50 0.1"00:34:27 UMAP embedding parameters a = 1.577 b = 0.8951
00:34:27 Read 1203 rows and found 38 numeric columns
00:34:27 Using Annoy for neighbor search, n_neighbors = 50
00:34:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:34:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873955a920
00:34:28 Searching Annoy index using 1 thread, search_k = 5000
00:34:28 Annoy recall = 100%
00:34:32 Commencing smooth kNN distance calibration using 1 thread
00:34:39 Initializing from normalized Laplacian + noise
00:34:39 Commencing optimization for 500 epochs, with 73404 positive edges
00:34:46 Optimization finished
[1] "50 0.11"00:34:46 UMAP embedding parameters a = 1.544 b = 0.9058
00:34:46 Read 1203 rows and found 38 numeric columns
00:34:46 Using Annoy for neighbor search, n_neighbors = 50
00:34:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:34:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f49e091
00:34:46 Searching Annoy index using 1 thread, search_k = 5000
00:34:47 Annoy recall = 100%
00:34:50 Commencing smooth kNN distance calibration using 1 thread
00:34:57 Initializing from normalized Laplacian + noise
00:34:57 Commencing optimization for 500 epochs, with 73404 positive edges
00:35:04 Optimization finished
[1] "50 0.12"00:35:04 UMAP embedding parameters a = 1.51 b = 0.9165
00:35:04 Read 1203 rows and found 38 numeric columns
00:35:04 Using Annoy for neighbor search, n_neighbors = 50
00:35:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:35:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740abc8ad
00:35:04 Searching Annoy index using 1 thread, search_k = 5000
00:35:05 Annoy recall = 100%
00:35:08 Commencing smooth kNN distance calibration using 1 thread
00:35:16 Initializing from normalized Laplacian + noise
00:35:16 Commencing optimization for 500 epochs, with 73404 positive edges
00:35:22 Optimization finished
[1] "50 0.13"00:35:22 UMAP embedding parameters a = 1.478 b = 0.9272
00:35:23 Read 1203 rows and found 38 numeric columns
00:35:23 Using Annoy for neighbor search, n_neighbors = 50
00:35:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:35:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87765a014a
00:35:23 Searching Annoy index using 1 thread, search_k = 5000
00:35:23 Annoy recall = 100%
00:35:27 Commencing smooth kNN distance calibration using 1 thread
00:35:34 Initializing from normalized Laplacian + noise
00:35:34 Commencing optimization for 500 epochs, with 73404 positive edges
00:35:41 Optimization finished
[1] "50 0.14"00:35:41 UMAP embedding parameters a = 1.446 b = 0.938
00:35:41 Read 1203 rows and found 38 numeric columns
00:35:41 Using Annoy for neighbor search, n_neighbors = 50
00:35:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:35:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87767e1d26
00:35:41 Searching Annoy index using 1 thread, search_k = 5000
00:35:42 Annoy recall = 100%
00:35:45 Commencing smooth kNN distance calibration using 1 thread
00:35:53 Initializing from normalized Laplacian + noise
00:35:53 Commencing optimization for 500 epochs, with 73404 positive edges
00:35:59 Optimization finished
[1] "50 0.15"00:35:59 UMAP embedding parameters a = 1.414 b = 0.9488
00:35:59 Read 1203 rows and found 38 numeric columns
00:35:59 Using Annoy for neighbor search, n_neighbors = 50
00:35:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:36:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b5f5062
00:36:00 Searching Annoy index using 1 thread, search_k = 5000
00:36:00 Annoy recall = 100%
00:36:04 Commencing smooth kNN distance calibration using 1 thread
00:36:11 Initializing from normalized Laplacian + noise
00:36:11 Commencing optimization for 500 epochs, with 73404 positive edges
00:36:18 Optimization finished
[1] "50 0.16"00:36:18 UMAP embedding parameters a = 1.383 b = 0.9596
00:36:18 Read 1203 rows and found 38 numeric columns
00:36:18 Using Annoy for neighbor search, n_neighbors = 50
00:36:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:36:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877765ab79
00:36:18 Searching Annoy index using 1 thread, search_k = 5000
00:36:19 Annoy recall = 100%
00:36:22 Commencing smooth kNN distance calibration using 1 thread
00:36:29 Initializing from normalized Laplacian + noise
00:36:29 Commencing optimization for 500 epochs, with 73404 positive edges
00:36:36 Optimization finished
[1] "50 0.17"00:36:36 UMAP embedding parameters a = 1.352 b = 0.9704
00:36:36 Read 1203 rows and found 38 numeric columns
00:36:36 Using Annoy for neighbor search, n_neighbors = 50
00:36:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:36:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753aa912c
00:36:37 Searching Annoy index using 1 thread, search_k = 5000
00:36:37 Annoy recall = 100%
00:36:41 Commencing smooth kNN distance calibration using 1 thread
00:36:48 Initializing from normalized Laplacian + noise
00:36:48 Commencing optimization for 500 epochs, with 73404 positive edges
00:36:55 Optimization finished
[1] "50 0.18"00:36:55 UMAP embedding parameters a = 1.321 b = 0.9813
00:36:55 Read 1203 rows and found 38 numeric columns
00:36:55 Using Annoy for neighbor search, n_neighbors = 50
00:36:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:36:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746c9c307
00:36:55 Searching Annoy index using 1 thread, search_k = 5000
00:36:56 Annoy recall = 100%
00:36:59 Commencing smooth kNN distance calibration using 1 thread
00:37:06 Initializing from normalized Laplacian + noise
00:37:06 Commencing optimization for 500 epochs, with 73404 positive edges
00:37:13 Optimization finished
[1] "50 0.19"00:37:13 UMAP embedding parameters a = 1.292 b = 0.9921
00:37:13 Read 1203 rows and found 38 numeric columns
00:37:13 Using Annoy for neighbor search, n_neighbors = 50
00:37:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:37:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730218cf2
00:37:14 Searching Annoy index using 1 thread, search_k = 5000
00:37:14 Annoy recall = 100%
00:37:18 Commencing smooth kNN distance calibration using 1 thread
00:37:25 Initializing from normalized Laplacian + noise
00:37:25 Commencing optimization for 500 epochs, with 73404 positive edges
00:37:32 Optimization finished
[1] "50 0.2"00:37:32 UMAP embedding parameters a = 1.262 b = 1.003
00:37:32 Read 1203 rows and found 38 numeric columns
00:37:32 Using Annoy for neighbor search, n_neighbors = 50
00:37:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:37:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b3e34bd
00:37:32 Searching Annoy index using 1 thread, search_k = 5000
00:37:33 Annoy recall = 100%
00:37:36 Commencing smooth kNN distance calibration using 1 thread
00:37:43 Initializing from normalized Laplacian + noise
00:37:43 Commencing optimization for 500 epochs, with 73404 positive edges
00:37:50 Optimization finished
[1] "51 0"00:37:50 UMAP embedding parameters a = 1.933 b = 0.7905
00:37:50 Read 1203 rows and found 38 numeric columns
00:37:50 Using Annoy for neighbor search, n_neighbors = 51
00:37:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:37:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ba2d5d7
00:37:51 Searching Annoy index using 1 thread, search_k = 5100
00:37:51 Annoy recall = 100%
00:37:55 Commencing smooth kNN distance calibration using 1 thread
00:38:02 Initializing from normalized Laplacian + noise
00:38:02 Commencing optimization for 500 epochs, with 74788 positive edges
00:38:09 Optimization finished
[1] "51 0.01"00:38:09 UMAP embedding parameters a = 1.896 b = 0.8006
00:38:09 Read 1203 rows and found 38 numeric columns
00:38:09 Using Annoy for neighbor search, n_neighbors = 51
00:38:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:38:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c5fc03b
00:38:09 Searching Annoy index using 1 thread, search_k = 5100
00:38:10 Annoy recall = 100%
00:38:13 Commencing smooth kNN distance calibration using 1 thread
00:38:20 Initializing from normalized Laplacian + noise
00:38:21 Commencing optimization for 500 epochs, with 74788 positive edges
00:38:27 Optimization finished
[1] "51 0.02"00:38:27 UMAP embedding parameters a = 1.859 b = 0.8109
00:38:27 Read 1203 rows and found 38 numeric columns
00:38:27 Using Annoy for neighbor search, n_neighbors = 51
00:38:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:38:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e98e98b
00:38:28 Searching Annoy index using 1 thread, search_k = 5100
00:38:28 Annoy recall = 100%
00:38:32 Commencing smooth kNN distance calibration using 1 thread
00:38:39 Initializing from normalized Laplacian + noise
00:38:39 Commencing optimization for 500 epochs, with 74788 positive edges
00:38:46 Optimization finished
[1] "51 0.03"00:38:46 UMAP embedding parameters a = 1.822 b = 0.8212
00:38:46 Read 1203 rows and found 38 numeric columns
00:38:46 Using Annoy for neighbor search, n_neighbors = 51
00:38:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:38:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756c6d949
00:38:46 Searching Annoy index using 1 thread, search_k = 5100
00:38:47 Annoy recall = 100%
00:38:50 Commencing smooth kNN distance calibration using 1 thread
00:38:57 Initializing from normalized Laplacian + noise
00:38:57 Commencing optimization for 500 epochs, with 74788 positive edges
00:39:04 Optimization finished
[1] "51 0.04"00:39:04 UMAP embedding parameters a = 1.786 b = 0.8316
00:39:04 Read 1203 rows and found 38 numeric columns
00:39:04 Using Annoy for neighbor search, n_neighbors = 51
00:39:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:39:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d93c044
00:39:05 Searching Annoy index using 1 thread, search_k = 5100
00:39:05 Annoy recall = 100%
00:39:09 Commencing smooth kNN distance calibration using 1 thread
00:39:16 Initializing from normalized Laplacian + noise
00:39:16 Commencing optimization for 500 epochs, with 74788 positive edges
00:39:23 Optimization finished
[1] "51 0.05"00:39:23 UMAP embedding parameters a = 1.75 b = 0.8421
00:39:23 Read 1203 rows and found 38 numeric columns
00:39:23 Using Annoy for neighbor search, n_neighbors = 51
00:39:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:39:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777b1e0b4
00:39:23 Searching Annoy index using 1 thread, search_k = 5100
00:39:24 Annoy recall = 100%
00:39:27 Commencing smooth kNN distance calibration using 1 thread
00:39:34 Initializing from normalized Laplacian + noise
00:39:34 Commencing optimization for 500 epochs, with 74788 positive edges
00:39:41 Optimization finished
[1] "51 0.06"00:39:41 UMAP embedding parameters a = 1.715 b = 0.8526
00:39:41 Read 1203 rows and found 38 numeric columns
00:39:41 Using Annoy for neighbor search, n_neighbors = 51
00:39:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:39:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741c11568
00:39:42 Searching Annoy index using 1 thread, search_k = 5100
00:39:42 Annoy recall = 100%
00:39:46 Commencing smooth kNN distance calibration using 1 thread
00:39:53 Initializing from normalized Laplacian + noise
00:39:53 Commencing optimization for 500 epochs, with 74788 positive edges
00:40:00 Optimization finished
[1] "51 0.07"00:40:00 UMAP embedding parameters a = 1.68 b = 0.8631
00:40:00 Read 1203 rows and found 38 numeric columns
00:40:00 Using Annoy for neighbor search, n_neighbors = 51
00:40:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:40:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713a5e230
00:40:00 Searching Annoy index using 1 thread, search_k = 5100
00:40:01 Annoy recall = 100%
00:40:04 Commencing smooth kNN distance calibration using 1 thread
00:40:11 Initializing from normalized Laplacian + noise
00:40:11 Commencing optimization for 500 epochs, with 74788 positive edges
00:40:18 Optimization finished
[1] "51 0.08"00:40:18 UMAP embedding parameters a = 1.645 b = 0.8737
00:40:18 Read 1203 rows and found 38 numeric columns
00:40:18 Using Annoy for neighbor search, n_neighbors = 51
00:40:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:40:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874caf4570
00:40:18 Searching Annoy index using 1 thread, search_k = 5100
00:40:19 Annoy recall = 100%
00:40:22 Commencing smooth kNN distance calibration using 1 thread
00:40:30 Initializing from normalized Laplacian + noise
00:40:30 Commencing optimization for 500 epochs, with 74788 positive edges
00:40:36 Optimization finished
[1] "51 0.09"00:40:37 UMAP embedding parameters a = 1.611 b = 0.8844
00:40:37 Read 1203 rows and found 38 numeric columns
00:40:37 Using Annoy for neighbor search, n_neighbors = 51
00:40:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:40:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bc8873
00:40:37 Searching Annoy index using 1 thread, search_k = 5100
00:40:37 Annoy recall = 100%
00:40:41 Commencing smooth kNN distance calibration using 1 thread
00:40:48 Initializing from normalized Laplacian + noise
00:40:48 Commencing optimization for 500 epochs, with 74788 positive edges
00:40:55 Optimization finished
[1] "51 0.1"00:40:55 UMAP embedding parameters a = 1.577 b = 0.8951
00:40:55 Read 1203 rows and found 38 numeric columns
00:40:55 Using Annoy for neighbor search, n_neighbors = 51
00:40:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:40:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877cf55c6e
00:40:56 Searching Annoy index using 1 thread, search_k = 5100
00:40:56 Annoy recall = 100%
00:41:00 Commencing smooth kNN distance calibration using 1 thread
00:41:07 Initializing from normalized Laplacian + noise
00:41:07 Commencing optimization for 500 epochs, with 74788 positive edges
00:41:13 Optimization finished
[1] "51 0.11"00:41:14 UMAP embedding parameters a = 1.544 b = 0.9058
00:41:14 Read 1203 rows and found 38 numeric columns
00:41:14 Using Annoy for neighbor search, n_neighbors = 51
00:41:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:41:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871cbda6da
00:41:14 Searching Annoy index using 1 thread, search_k = 5100
00:41:14 Annoy recall = 100%
00:41:18 Commencing smooth kNN distance calibration using 1 thread
00:41:25 Initializing from normalized Laplacian + noise
00:41:25 Commencing optimization for 500 epochs, with 74788 positive edges
00:41:32 Optimization finished
[1] "51 0.12"00:41:32 UMAP embedding parameters a = 1.51 b = 0.9165
00:41:32 Read 1203 rows and found 38 numeric columns
00:41:32 Using Annoy for neighbor search, n_neighbors = 51
00:41:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:41:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8782a88a8
00:41:32 Searching Annoy index using 1 thread, search_k = 5100
00:41:33 Annoy recall = 100%
00:41:36 Commencing smooth kNN distance calibration using 1 thread
00:41:44 Initializing from normalized Laplacian + noise
00:41:44 Commencing optimization for 500 epochs, with 74788 positive edges
00:41:50 Optimization finished
[1] "51 0.13"00:41:50 UMAP embedding parameters a = 1.478 b = 0.9272
00:41:50 Read 1203 rows and found 38 numeric columns
00:41:50 Using Annoy for neighbor search, n_neighbors = 51
00:41:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:41:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771d6d01e
00:41:51 Searching Annoy index using 1 thread, search_k = 5100
00:41:51 Annoy recall = 100%
00:41:55 Commencing smooth kNN distance calibration using 1 thread
00:42:02 Initializing from normalized Laplacian + noise
00:42:02 Commencing optimization for 500 epochs, with 74788 positive edges
00:42:09 Optimization finished
[1] "51 0.14"00:42:09 UMAP embedding parameters a = 1.446 b = 0.938
00:42:09 Read 1203 rows and found 38 numeric columns
00:42:09 Using Annoy for neighbor search, n_neighbors = 51
00:42:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:42:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ef7e5fe
00:42:09 Searching Annoy index using 1 thread, search_k = 5100
00:42:10 Annoy recall = 100%
00:42:13 Commencing smooth kNN distance calibration using 1 thread
00:42:20 Initializing from normalized Laplacian + noise
00:42:20 Commencing optimization for 500 epochs, with 74788 positive edges
00:42:27 Optimization finished
[1] "51 0.15"00:42:27 UMAP embedding parameters a = 1.414 b = 0.9488
00:42:27 Read 1203 rows and found 38 numeric columns
00:42:27 Using Annoy for neighbor search, n_neighbors = 51
00:42:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:42:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736e51587
00:42:27 Searching Annoy index using 1 thread, search_k = 5100
00:42:28 Annoy recall = 100%
00:42:31 Commencing smooth kNN distance calibration using 1 thread
00:42:39 Initializing from normalized Laplacian + noise
00:42:39 Commencing optimization for 500 epochs, with 74788 positive edges
00:42:45 Optimization finished
[1] "51 0.16"00:42:46 UMAP embedding parameters a = 1.383 b = 0.9596
00:42:46 Read 1203 rows and found 38 numeric columns
00:42:46 Using Annoy for neighbor search, n_neighbors = 51
00:42:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:42:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747bb9b5c
00:42:46 Searching Annoy index using 1 thread, search_k = 5100
00:42:46 Annoy recall = 100%
00:42:50 Commencing smooth kNN distance calibration using 1 thread
00:42:57 Initializing from normalized Laplacian + noise
00:42:57 Commencing optimization for 500 epochs, with 74788 positive edges
00:43:04 Optimization finished
[1] "51 0.17"00:43:04 UMAP embedding parameters a = 1.352 b = 0.9704
00:43:04 Read 1203 rows and found 38 numeric columns
00:43:04 Using Annoy for neighbor search, n_neighbors = 51
00:43:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:43:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741973ec2
00:43:04 Searching Annoy index using 1 thread, search_k = 5100
00:43:05 Annoy recall = 100%
00:43:08 Commencing smooth kNN distance calibration using 1 thread
00:43:15 Initializing from normalized Laplacian + noise
00:43:16 Commencing optimization for 500 epochs, with 74788 positive edges
00:43:22 Optimization finished
[1] "51 0.18"00:43:22 UMAP embedding parameters a = 1.321 b = 0.9813
00:43:22 Read 1203 rows and found 38 numeric columns
00:43:22 Using Annoy for neighbor search, n_neighbors = 51
00:43:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:43:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769ed2083
00:43:23 Searching Annoy index using 1 thread, search_k = 5100
00:43:23 Annoy recall = 100%
00:43:27 Commencing smooth kNN distance calibration using 1 thread
00:43:34 Initializing from normalized Laplacian + noise
00:43:34 Commencing optimization for 500 epochs, with 74788 positive edges
00:43:41 Optimization finished
[1] "51 0.19"00:43:41 UMAP embedding parameters a = 1.292 b = 0.9921
00:43:41 Read 1203 rows and found 38 numeric columns
00:43:41 Using Annoy for neighbor search, n_neighbors = 51
00:43:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:43:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874407ee20
00:43:41 Searching Annoy index using 1 thread, search_k = 5100
00:43:42 Annoy recall = 100%
00:43:45 Commencing smooth kNN distance calibration using 1 thread
00:43:52 Initializing from normalized Laplacian + noise
00:43:52 Commencing optimization for 500 epochs, with 74788 positive edges
00:43:59 Optimization finished
[1] "51 0.2"00:43:59 UMAP embedding parameters a = 1.262 b = 1.003
00:43:59 Read 1203 rows and found 38 numeric columns
00:43:59 Using Annoy for neighbor search, n_neighbors = 51
00:43:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:44:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877aece7e2
00:44:00 Searching Annoy index using 1 thread, search_k = 5100
00:44:00 Annoy recall = 100%
00:44:04 Commencing smooth kNN distance calibration using 1 thread
00:44:11 Initializing from normalized Laplacian + noise
00:44:11 Commencing optimization for 500 epochs, with 74788 positive edges
00:44:18 Optimization finished
[1] "52 0"00:44:18 UMAP embedding parameters a = 1.933 b = 0.7905
00:44:18 Read 1203 rows and found 38 numeric columns
00:44:18 Using Annoy for neighbor search, n_neighbors = 52
00:44:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:44:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879370114
00:44:18 Searching Annoy index using 1 thread, search_k = 5200
00:44:18 Annoy recall = 100%
00:44:22 Commencing smooth kNN distance calibration using 1 thread
00:44:29 Initializing from normalized Laplacian + noise
00:44:29 Commencing optimization for 500 epochs, with 76168 positive edges
00:44:36 Optimization finished
[1] "52 0.01"00:44:36 UMAP embedding parameters a = 1.896 b = 0.8006
00:44:36 Read 1203 rows and found 38 numeric columns
00:44:36 Using Annoy for neighbor search, n_neighbors = 52
00:44:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:44:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b3b6cd
00:44:37 Searching Annoy index using 1 thread, search_k = 5200
00:44:37 Annoy recall = 100%
00:44:41 Commencing smooth kNN distance calibration using 1 thread
00:44:48 Initializing from normalized Laplacian + noise
00:44:48 Commencing optimization for 500 epochs, with 76168 positive edges
00:44:55 Optimization finished
[1] "52 0.02"00:44:55 UMAP embedding parameters a = 1.859 b = 0.8109
00:44:55 Read 1203 rows and found 38 numeric columns
00:44:55 Using Annoy for neighbor search, n_neighbors = 52
00:44:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:44:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877146e92c
00:44:55 Searching Annoy index using 1 thread, search_k = 5200
00:44:56 Annoy recall = 100%
00:44:59 Commencing smooth kNN distance calibration using 1 thread
00:45:06 Initializing from normalized Laplacian + noise
00:45:06 Commencing optimization for 500 epochs, with 76168 positive edges
00:45:13 Optimization finished
[1] "52 0.03"00:45:13 UMAP embedding parameters a = 1.822 b = 0.8212
00:45:13 Read 1203 rows and found 38 numeric columns
00:45:13 Using Annoy for neighbor search, n_neighbors = 52
00:45:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:45:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fb51e3a
00:45:14 Searching Annoy index using 1 thread, search_k = 5200
00:45:14 Annoy recall = 100%
00:45:18 Commencing smooth kNN distance calibration using 1 thread
00:45:25 Initializing from normalized Laplacian + noise
00:45:25 Commencing optimization for 500 epochs, with 76168 positive edges
00:45:32 Optimization finished
[1] "52 0.04"00:45:32 UMAP embedding parameters a = 1.786 b = 0.8316
00:45:32 Read 1203 rows and found 38 numeric columns
00:45:32 Using Annoy for neighbor search, n_neighbors = 52
00:45:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:45:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770130730
00:45:32 Searching Annoy index using 1 thread, search_k = 5200
00:45:33 Annoy recall = 100%
00:45:36 Commencing smooth kNN distance calibration using 1 thread
00:45:43 Initializing from normalized Laplacian + noise
00:45:43 Commencing optimization for 500 epochs, with 76168 positive edges
00:45:50 Optimization finished
[1] "52 0.05"00:45:50 UMAP embedding parameters a = 1.75 b = 0.8421
00:45:50 Read 1203 rows and found 38 numeric columns
00:45:50 Using Annoy for neighbor search, n_neighbors = 52
00:45:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:45:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768ac94a5
00:45:51 Searching Annoy index using 1 thread, search_k = 5200
00:45:51 Annoy recall = 100%
00:45:55 Commencing smooth kNN distance calibration using 1 thread
00:46:02 Initializing from normalized Laplacian + noise
00:46:02 Commencing optimization for 500 epochs, with 76168 positive edges
00:46:09 Optimization finished
[1] "52 0.06"00:46:09 UMAP embedding parameters a = 1.715 b = 0.8526
00:46:09 Read 1203 rows and found 38 numeric columns
00:46:09 Using Annoy for neighbor search, n_neighbors = 52
00:46:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:46:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87535faf67
00:46:09 Searching Annoy index using 1 thread, search_k = 5200
00:46:10 Annoy recall = 100%
00:46:13 Commencing smooth kNN distance calibration using 1 thread
00:46:21 Initializing from normalized Laplacian + noise
00:46:21 Commencing optimization for 500 epochs, with 76168 positive edges
00:46:27 Optimization finished
[1] "52 0.07"00:46:28 UMAP embedding parameters a = 1.68 b = 0.8631
00:46:28 Read 1203 rows and found 38 numeric columns
00:46:28 Using Annoy for neighbor search, n_neighbors = 52
00:46:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:46:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736dcca37
00:46:28 Searching Annoy index using 1 thread, search_k = 5200
00:46:28 Annoy recall = 100%
00:46:32 Commencing smooth kNN distance calibration using 1 thread
00:46:39 Initializing from normalized Laplacian + noise
00:46:39 Commencing optimization for 500 epochs, with 76168 positive edges
00:46:46 Optimization finished
[1] "52 0.08"00:46:46 UMAP embedding parameters a = 1.645 b = 0.8737
00:46:46 Read 1203 rows and found 38 numeric columns
00:46:46 Using Annoy for neighbor search, n_neighbors = 52
00:46:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:46:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718ce2197
00:46:46 Searching Annoy index using 1 thread, search_k = 5200
00:46:47 Annoy recall = 100%
00:46:50 Commencing smooth kNN distance calibration using 1 thread
00:46:58 Initializing from normalized Laplacian + noise
00:46:58 Commencing optimization for 500 epochs, with 76168 positive edges
00:47:05 Optimization finished
[1] "52 0.09"00:47:05 UMAP embedding parameters a = 1.611 b = 0.8844
00:47:05 Read 1203 rows and found 38 numeric columns
00:47:05 Using Annoy for neighbor search, n_neighbors = 52
00:47:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e9de424
00:47:05 Searching Annoy index using 1 thread, search_k = 5200
00:47:06 Annoy recall = 100%
00:47:09 Commencing smooth kNN distance calibration using 1 thread
00:47:17 Initializing from normalized Laplacian + noise
00:47:17 Commencing optimization for 500 epochs, with 76168 positive edges
00:47:23 Optimization finished
[1] "52 0.1"00:47:24 UMAP embedding parameters a = 1.577 b = 0.8951
00:47:24 Read 1203 rows and found 38 numeric columns
00:47:24 Using Annoy for neighbor search, n_neighbors = 52
00:47:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87527fa00e
00:47:24 Searching Annoy index using 1 thread, search_k = 5200
00:47:24 Annoy recall = 100%
00:47:28 Commencing smooth kNN distance calibration using 1 thread
00:47:35 Initializing from normalized Laplacian + noise
00:47:35 Commencing optimization for 500 epochs, with 76168 positive edges
00:47:42 Optimization finished
[1] "52 0.11"00:47:42 UMAP embedding parameters a = 1.544 b = 0.9058
00:47:42 Read 1203 rows and found 38 numeric columns
00:47:42 Using Annoy for neighbor search, n_neighbors = 52
00:47:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752de1d3
00:47:42 Searching Annoy index using 1 thread, search_k = 5200
00:47:43 Annoy recall = 100%
00:47:46 Commencing smooth kNN distance calibration using 1 thread
00:47:54 Initializing from normalized Laplacian + noise
00:47:54 Commencing optimization for 500 epochs, with 76168 positive edges
00:48:00 Optimization finished
[1] "52 0.12"00:48:01 UMAP embedding parameters a = 1.51 b = 0.9165
00:48:01 Read 1203 rows and found 38 numeric columns
00:48:01 Using Annoy for neighbor search, n_neighbors = 52
00:48:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d36cdaf
00:48:01 Searching Annoy index using 1 thread, search_k = 5200
00:48:01 Annoy recall = 100%
00:48:05 Commencing smooth kNN distance calibration using 1 thread
00:48:12 Initializing from normalized Laplacian + noise
00:48:12 Commencing optimization for 500 epochs, with 76168 positive edges
00:48:19 Optimization finished
[1] "52 0.13"00:48:19 UMAP embedding parameters a = 1.478 b = 0.9272
00:48:19 Read 1203 rows and found 38 numeric columns
00:48:19 Using Annoy for neighbor search, n_neighbors = 52
00:48:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729467957
00:48:20 Searching Annoy index using 1 thread, search_k = 5200
00:48:20 Annoy recall = 100%
00:48:24 Commencing smooth kNN distance calibration using 1 thread
00:48:31 Initializing from normalized Laplacian + noise
00:48:31 Commencing optimization for 500 epochs, with 76168 positive edges
00:48:38 Optimization finished
[1] "52 0.14"00:48:38 UMAP embedding parameters a = 1.446 b = 0.938
00:48:38 Read 1203 rows and found 38 numeric columns
00:48:38 Using Annoy for neighbor search, n_neighbors = 52
00:48:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762c1a217
00:48:38 Searching Annoy index using 1 thread, search_k = 5200
00:48:39 Annoy recall = 100%
00:48:42 Commencing smooth kNN distance calibration using 1 thread
00:48:50 Initializing from normalized Laplacian + noise
00:48:50 Commencing optimization for 500 epochs, with 76168 positive edges
00:48:56 Optimization finished
[1] "52 0.15"00:48:57 UMAP embedding parameters a = 1.414 b = 0.9488
00:48:57 Read 1203 rows and found 38 numeric columns
00:48:57 Using Annoy for neighbor search, n_neighbors = 52
00:48:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774e8ae63
00:48:57 Searching Annoy index using 1 thread, search_k = 5200
00:48:57 Annoy recall = 100%
00:49:01 Commencing smooth kNN distance calibration using 1 thread
00:49:08 Initializing from normalized Laplacian + noise
00:49:08 Commencing optimization for 500 epochs, with 76168 positive edges
00:49:15 Optimization finished
[1] "52 0.16"00:49:15 UMAP embedding parameters a = 1.383 b = 0.9596
00:49:15 Read 1203 rows and found 38 numeric columns
00:49:15 Using Annoy for neighbor search, n_neighbors = 52
00:49:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b078ec0
00:49:16 Searching Annoy index using 1 thread, search_k = 5200
00:49:16 Annoy recall = 100%
00:49:20 Commencing smooth kNN distance calibration using 1 thread
00:49:27 Initializing from normalized Laplacian + noise
00:49:27 Commencing optimization for 500 epochs, with 76168 positive edges
00:49:34 Optimization finished
[1] "52 0.17"00:49:34 UMAP embedding parameters a = 1.352 b = 0.9704
00:49:34 Read 1203 rows and found 38 numeric columns
00:49:34 Using Annoy for neighbor search, n_neighbors = 52
00:49:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776678447
00:49:34 Searching Annoy index using 1 thread, search_k = 5200
00:49:35 Annoy recall = 100%
00:49:38 Commencing smooth kNN distance calibration using 1 thread
00:49:46 Initializing from normalized Laplacian + noise
00:49:46 Commencing optimization for 500 epochs, with 76168 positive edges
00:49:52 Optimization finished
[1] "52 0.18"00:49:53 UMAP embedding parameters a = 1.321 b = 0.9813
00:49:53 Read 1203 rows and found 38 numeric columns
00:49:53 Using Annoy for neighbor search, n_neighbors = 52
00:49:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874197f3d3
00:49:53 Searching Annoy index using 1 thread, search_k = 5200
00:49:53 Annoy recall = 100%
00:49:57 Commencing smooth kNN distance calibration using 1 thread
00:50:04 Initializing from normalized Laplacian + noise
00:50:04 Commencing optimization for 500 epochs, with 76168 positive edges
00:50:11 Optimization finished
[1] "52 0.19"00:50:11 UMAP embedding parameters a = 1.292 b = 0.9921
00:50:11 Read 1203 rows and found 38 numeric columns
00:50:11 Using Annoy for neighbor search, n_neighbors = 52
00:50:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:50:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771c41733
00:50:12 Searching Annoy index using 1 thread, search_k = 5200
00:50:12 Annoy recall = 100%
00:50:16 Commencing smooth kNN distance calibration using 1 thread
00:50:23 Initializing from normalized Laplacian + noise
00:50:23 Commencing optimization for 500 epochs, with 76168 positive edges
00:50:30 Optimization finished
[1] "52 0.2"00:50:30 UMAP embedding parameters a = 1.262 b = 1.003
00:50:30 Read 1203 rows and found 38 numeric columns
00:50:30 Using Annoy for neighbor search, n_neighbors = 52
00:50:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:50:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87735ce0b6
00:50:30 Searching Annoy index using 1 thread, search_k = 5200
00:50:31 Annoy recall = 100%
00:50:34 Commencing smooth kNN distance calibration using 1 thread
00:50:42 Initializing from normalized Laplacian + noise
00:50:42 Commencing optimization for 500 epochs, with 76168 positive edges
00:50:49 Optimization finished
[1] "53 0"00:50:49 UMAP embedding parameters a = 1.933 b = 0.7905
00:50:49 Read 1203 rows and found 38 numeric columns
00:50:49 Using Annoy for neighbor search, n_neighbors = 53
00:50:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:50:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e559aae
00:50:49 Searching Annoy index using 1 thread, search_k = 5300
00:50:50 Annoy recall = 100%
00:50:53 Commencing smooth kNN distance calibration using 1 thread
00:51:01 Initializing from normalized Laplacian + noise
00:51:01 Commencing optimization for 500 epochs, with 77574 positive edges
00:51:07 Optimization finished
[1] "53 0.01"00:51:08 UMAP embedding parameters a = 1.896 b = 0.8006
00:51:08 Read 1203 rows and found 38 numeric columns
00:51:08 Using Annoy for neighbor search, n_neighbors = 53
00:51:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:51:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779ee9fdc
00:51:08 Searching Annoy index using 1 thread, search_k = 5300
00:51:08 Annoy recall = 100%
00:51:12 Commencing smooth kNN distance calibration using 1 thread
00:51:19 Initializing from normalized Laplacian + noise
00:51:19 Commencing optimization for 500 epochs, with 77574 positive edges
00:51:26 Optimization finished
[1] "53 0.02"00:51:26 UMAP embedding parameters a = 1.859 b = 0.8109
00:51:26 Read 1203 rows and found 38 numeric columns
00:51:26 Using Annoy for neighbor search, n_neighbors = 53
00:51:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:51:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876533b0d4
00:51:27 Searching Annoy index using 1 thread, search_k = 5300
00:51:27 Annoy recall = 100%
00:51:31 Commencing smooth kNN distance calibration using 1 thread
00:51:38 Initializing from normalized Laplacian + noise
00:51:38 Commencing optimization for 500 epochs, with 77574 positive edges
00:51:45 Optimization finished
[1] "53 0.03"00:51:45 UMAP embedding parameters a = 1.822 b = 0.8212
00:51:45 Read 1203 rows and found 38 numeric columns
00:51:45 Using Annoy for neighbor search, n_neighbors = 53
00:51:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:51:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d4d80ac
00:51:45 Searching Annoy index using 1 thread, search_k = 5300
00:51:46 Annoy recall = 100%
00:51:50 Commencing smooth kNN distance calibration using 1 thread
00:51:57 Initializing from normalized Laplacian + noise
00:51:57 Commencing optimization for 500 epochs, with 77574 positive edges
00:52:04 Optimization finished
[1] "53 0.04"00:52:04 UMAP embedding parameters a = 1.786 b = 0.8316
00:52:04 Read 1203 rows and found 38 numeric columns
00:52:04 Using Annoy for neighbor search, n_neighbors = 53
00:52:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:52:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730d3b563
00:52:04 Searching Annoy index using 1 thread, search_k = 5300
00:52:05 Annoy recall = 100%
00:52:08 Commencing smooth kNN distance calibration using 1 thread
00:52:16 Initializing from normalized Laplacian + noise
00:52:16 Commencing optimization for 500 epochs, with 77574 positive edges
00:52:23 Optimization finished
[1] "53 0.05"00:52:23 UMAP embedding parameters a = 1.75 b = 0.8421
00:52:23 Read 1203 rows and found 38 numeric columns
00:52:23 Using Annoy for neighbor search, n_neighbors = 53
00:52:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:52:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cef4c30
00:52:23 Searching Annoy index using 1 thread, search_k = 5300
00:52:24 Annoy recall = 100%
00:52:27 Commencing smooth kNN distance calibration using 1 thread
00:52:35 Initializing from normalized Laplacian + noise
00:52:35 Commencing optimization for 500 epochs, with 77574 positive edges
00:52:41 Optimization finished
[1] "53 0.06"00:52:42 UMAP embedding parameters a = 1.715 b = 0.8526
00:52:42 Read 1203 rows and found 38 numeric columns
00:52:42 Using Annoy for neighbor search, n_neighbors = 53
00:52:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:52:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ee4bf6e
00:52:42 Searching Annoy index using 1 thread, search_k = 5300
00:52:42 Annoy recall = 100%
00:52:46 Commencing smooth kNN distance calibration using 1 thread
00:52:53 Initializing from normalized Laplacian + noise
00:52:53 Commencing optimization for 500 epochs, with 77574 positive edges
00:53:00 Optimization finished
[1] "53 0.07"00:53:00 UMAP embedding parameters a = 1.68 b = 0.8631
00:53:00 Read 1203 rows and found 38 numeric columns
00:53:00 Using Annoy for neighbor search, n_neighbors = 53
00:53:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:53:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ac0d5e6
00:53:01 Searching Annoy index using 1 thread, search_k = 5300
00:53:01 Annoy recall = 100%
00:53:05 Commencing smooth kNN distance calibration using 1 thread
00:53:12 Initializing from normalized Laplacian + noise
00:53:12 Commencing optimization for 500 epochs, with 77574 positive edges
00:53:19 Optimization finished
[1] "53 0.08"00:53:19 UMAP embedding parameters a = 1.645 b = 0.8737
00:53:19 Read 1203 rows and found 38 numeric columns
00:53:19 Using Annoy for neighbor search, n_neighbors = 53
00:53:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:53:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770f73a50
00:53:20 Searching Annoy index using 1 thread, search_k = 5300
00:53:20 Annoy recall = 100%
00:53:24 Commencing smooth kNN distance calibration using 1 thread
00:53:31 Initializing from normalized Laplacian + noise
00:53:31 Commencing optimization for 500 epochs, with 77574 positive edges
00:53:38 Optimization finished
[1] "53 0.09"00:53:38 UMAP embedding parameters a = 1.611 b = 0.8844
00:53:38 Read 1203 rows and found 38 numeric columns
00:53:38 Using Annoy for neighbor search, n_neighbors = 53
00:53:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:53:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719d1a750
00:53:39 Searching Annoy index using 1 thread, search_k = 5300
00:53:39 Annoy recall = 100%
00:53:43 Commencing smooth kNN distance calibration using 1 thread
00:53:50 Initializing from normalized Laplacian + noise
00:53:50 Commencing optimization for 500 epochs, with 77574 positive edges
00:53:57 Optimization finished
[1] "53 0.1"00:53:57 UMAP embedding parameters a = 1.577 b = 0.8951
00:53:57 Read 1203 rows and found 38 numeric columns
00:53:57 Using Annoy for neighbor search, n_neighbors = 53
00:53:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:53:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723f7d6fb
00:53:57 Searching Annoy index using 1 thread, search_k = 5300
00:53:58 Annoy recall = 100%
00:54:02 Commencing smooth kNN distance calibration using 1 thread
00:54:09 Initializing from normalized Laplacian + noise
00:54:09 Commencing optimization for 500 epochs, with 77574 positive edges
00:54:16 Optimization finished
[1] "53 0.11"00:54:16 UMAP embedding parameters a = 1.544 b = 0.9058
00:54:16 Read 1203 rows and found 38 numeric columns
00:54:16 Using Annoy for neighbor search, n_neighbors = 53
00:54:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:54:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775aaf11e
00:54:16 Searching Annoy index using 1 thread, search_k = 5300
00:54:17 Annoy recall = 100%
00:54:20 Commencing smooth kNN distance calibration using 1 thread
00:54:28 Initializing from normalized Laplacian + noise
00:54:28 Commencing optimization for 500 epochs, with 77574 positive edges
00:54:35 Optimization finished
[1] "53 0.12"00:54:35 UMAP embedding parameters a = 1.51 b = 0.9165
00:54:35 Read 1203 rows and found 38 numeric columns
00:54:35 Using Annoy for neighbor search, n_neighbors = 53
00:54:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:54:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b18907d
00:54:35 Searching Annoy index using 1 thread, search_k = 5300
00:54:36 Annoy recall = 100%
00:54:39 Commencing smooth kNN distance calibration using 1 thread
00:54:47 Initializing from normalized Laplacian + noise
00:54:47 Commencing optimization for 500 epochs, with 77574 positive edges
00:54:53 Optimization finished
[1] "53 0.13"00:54:54 UMAP embedding parameters a = 1.478 b = 0.9272
00:54:54 Read 1203 rows and found 38 numeric columns
00:54:54 Using Annoy for neighbor search, n_neighbors = 53
00:54:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:54:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723acf535
00:54:54 Searching Annoy index using 1 thread, search_k = 5300
00:54:54 Annoy recall = 100%
00:54:58 Commencing smooth kNN distance calibration using 1 thread
00:55:06 Initializing from normalized Laplacian + noise
00:55:06 Commencing optimization for 500 epochs, with 77574 positive edges
00:55:12 Optimization finished
[1] "53 0.14"00:55:13 UMAP embedding parameters a = 1.446 b = 0.938
00:55:13 Read 1203 rows and found 38 numeric columns
00:55:13 Using Annoy for neighbor search, n_neighbors = 53
00:55:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:55:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765bdf84e
00:55:13 Searching Annoy index using 1 thread, search_k = 5300
00:55:13 Annoy recall = 100%
00:55:17 Commencing smooth kNN distance calibration using 1 thread
00:55:24 Initializing from normalized Laplacian + noise
00:55:25 Commencing optimization for 500 epochs, with 77574 positive edges
00:55:31 Optimization finished
[1] "53 0.15"00:55:32 UMAP embedding parameters a = 1.414 b = 0.9488
00:55:32 Read 1203 rows and found 38 numeric columns
00:55:32 Using Annoy for neighbor search, n_neighbors = 53
00:55:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:55:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773c52522
00:55:32 Searching Annoy index using 1 thread, search_k = 5300
00:55:32 Annoy recall = 100%
00:55:36 Commencing smooth kNN distance calibration using 1 thread
00:55:43 Initializing from normalized Laplacian + noise
00:55:43 Commencing optimization for 500 epochs, with 77574 positive edges
00:55:50 Optimization finished
[1] "53 0.16"00:55:50 UMAP embedding parameters a = 1.383 b = 0.9596
00:55:50 Read 1203 rows and found 38 numeric columns
00:55:50 Using Annoy for neighbor search, n_neighbors = 53
00:55:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:55:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87770ca49c
00:55:51 Searching Annoy index using 1 thread, search_k = 5300
00:55:51 Annoy recall = 100%
00:55:55 Commencing smooth kNN distance calibration using 1 thread
00:56:02 Initializing from normalized Laplacian + noise
00:56:02 Commencing optimization for 500 epochs, with 77574 positive edges
00:56:09 Optimization finished
[1] "53 0.17"00:56:09 UMAP embedding parameters a = 1.352 b = 0.9704
00:56:09 Read 1203 rows and found 38 numeric columns
00:56:09 Using Annoy for neighbor search, n_neighbors = 53
00:56:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:56:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c9ac285
00:56:10 Searching Annoy index using 1 thread, search_k = 5300
00:56:10 Annoy recall = 100%
00:56:14 Commencing smooth kNN distance calibration using 1 thread
00:56:21 Initializing from normalized Laplacian + noise
00:56:21 Commencing optimization for 500 epochs, with 77574 positive edges
00:56:28 Optimization finished
[1] "53 0.18"00:56:28 UMAP embedding parameters a = 1.321 b = 0.9813
00:56:28 Read 1203 rows and found 38 numeric columns
00:56:28 Using Annoy for neighbor search, n_neighbors = 53
00:56:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:56:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c9346ba
00:56:29 Searching Annoy index using 1 thread, search_k = 5300
00:56:29 Annoy recall = 100%
00:56:33 Commencing smooth kNN distance calibration using 1 thread
00:56:40 Initializing from normalized Laplacian + noise
00:56:40 Commencing optimization for 500 epochs, with 77574 positive edges
00:56:47 Optimization finished
[1] "53 0.19"00:56:47 UMAP embedding parameters a = 1.292 b = 0.9921
00:56:47 Read 1203 rows and found 38 numeric columns
00:56:47 Using Annoy for neighbor search, n_neighbors = 53
00:56:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:56:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715aa88c0
00:56:47 Searching Annoy index using 1 thread, search_k = 5300
00:56:48 Annoy recall = 100%
00:56:52 Commencing smooth kNN distance calibration using 1 thread
00:56:59 Initializing from normalized Laplacian + noise
00:56:59 Commencing optimization for 500 epochs, with 77574 positive edges
00:57:06 Optimization finished
[1] "53 0.2"00:57:06 UMAP embedding parameters a = 1.262 b = 1.003
00:57:06 Read 1203 rows and found 38 numeric columns
00:57:06 Using Annoy for neighbor search, n_neighbors = 53
00:57:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:57:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f1a6293
00:57:06 Searching Annoy index using 1 thread, search_k = 5300
00:57:07 Annoy recall = 100%
00:57:11 Commencing smooth kNN distance calibration using 1 thread
00:57:18 Initializing from normalized Laplacian + noise
00:57:18 Commencing optimization for 500 epochs, with 77574 positive edges
00:57:25 Optimization finished
[1] "54 0"00:57:25 UMAP embedding parameters a = 1.933 b = 0.7905
00:57:25 Read 1203 rows and found 38 numeric columns
00:57:25 Using Annoy for neighbor search, n_neighbors = 54
00:57:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:57:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711c1288d
00:57:26 Searching Annoy index using 1 thread, search_k = 5400
00:57:26 Annoy recall = 100%
00:57:30 Commencing smooth kNN distance calibration using 1 thread
00:57:37 Initializing from normalized Laplacian + noise
00:57:37 Commencing optimization for 500 epochs, with 78886 positive edges
00:57:44 Optimization finished
[1] "54 0.01"00:57:44 UMAP embedding parameters a = 1.896 b = 0.8006
00:57:44 Read 1203 rows and found 38 numeric columns
00:57:44 Using Annoy for neighbor search, n_neighbors = 54
00:57:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:57:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712e1566f
00:57:44 Searching Annoy index using 1 thread, search_k = 5400
00:57:45 Annoy recall = 100%
00:57:49 Commencing smooth kNN distance calibration using 1 thread
00:57:56 Initializing from normalized Laplacian + noise
00:57:56 Commencing optimization for 500 epochs, with 78886 positive edges
00:58:03 Optimization finished
[1] "54 0.02"00:58:03 UMAP embedding parameters a = 1.859 b = 0.8109
00:58:03 Read 1203 rows and found 38 numeric columns
00:58:03 Using Annoy for neighbor search, n_neighbors = 54
00:58:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:58:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871860dbea
00:58:03 Searching Annoy index using 1 thread, search_k = 5400
00:58:04 Annoy recall = 100%
00:58:08 Commencing smooth kNN distance calibration using 1 thread
00:58:15 Initializing from normalized Laplacian + noise
00:58:15 Commencing optimization for 500 epochs, with 78886 positive edges
00:58:22 Optimization finished
[1] "54 0.03"00:58:22 UMAP embedding parameters a = 1.822 b = 0.8212
00:58:22 Read 1203 rows and found 38 numeric columns
00:58:22 Using Annoy for neighbor search, n_neighbors = 54
00:58:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:58:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877482caa4
00:58:23 Searching Annoy index using 1 thread, search_k = 5400
00:58:23 Annoy recall = 100%
00:58:27 Commencing smooth kNN distance calibration using 1 thread
00:58:34 Initializing from normalized Laplacian + noise
00:58:34 Commencing optimization for 500 epochs, with 78886 positive edges
00:58:41 Optimization finished
[1] "54 0.04"00:58:41 UMAP embedding parameters a = 1.786 b = 0.8316
00:58:41 Read 1203 rows and found 38 numeric columns
00:58:41 Using Annoy for neighbor search, n_neighbors = 54
00:58:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:58:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ca04d2
00:58:42 Searching Annoy index using 1 thread, search_k = 5400
00:58:42 Annoy recall = 100%
00:58:46 Commencing smooth kNN distance calibration using 1 thread
00:58:53 Initializing from normalized Laplacian + noise
00:58:53 Commencing optimization for 500 epochs, with 78886 positive edges
00:59:00 Optimization finished
[1] "54 0.05"00:59:00 UMAP embedding parameters a = 1.75 b = 0.8421
00:59:00 Read 1203 rows and found 38 numeric columns
00:59:00 Using Annoy for neighbor search, n_neighbors = 54
00:59:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:59:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873686aaa
00:59:01 Searching Annoy index using 1 thread, search_k = 5400
00:59:01 Annoy recall = 100%
00:59:05 Commencing smooth kNN distance calibration using 1 thread
00:59:12 Initializing from normalized Laplacian + noise
00:59:12 Commencing optimization for 500 epochs, with 78886 positive edges
00:59:19 Optimization finished
[1] "54 0.06"00:59:19 UMAP embedding parameters a = 1.715 b = 0.8526
00:59:19 Read 1203 rows and found 38 numeric columns
00:59:19 Using Annoy for neighbor search, n_neighbors = 54
00:59:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:59:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aea4eec
00:59:20 Searching Annoy index using 1 thread, search_k = 5400
00:59:20 Annoy recall = 100%
00:59:24 Commencing smooth kNN distance calibration using 1 thread
00:59:31 Initializing from normalized Laplacian + noise
00:59:31 Commencing optimization for 500 epochs, with 78886 positive edges
00:59:38 Optimization finished
[1] "54 0.07"00:59:38 UMAP embedding parameters a = 1.68 b = 0.8631
00:59:38 Read 1203 rows and found 38 numeric columns
00:59:38 Using Annoy for neighbor search, n_neighbors = 54
00:59:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:59:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874961f8a6
00:59:39 Searching Annoy index using 1 thread, search_k = 5400
00:59:39 Annoy recall = 100%
00:59:43 Commencing smooth kNN distance calibration using 1 thread
00:59:50 Initializing from normalized Laplacian + noise
00:59:50 Commencing optimization for 500 epochs, with 78886 positive edges
00:59:57 Optimization finished
[1] "54 0.08"00:59:58 UMAP embedding parameters a = 1.645 b = 0.8737
00:59:58 Read 1203 rows and found 38 numeric columns
00:59:58 Using Annoy for neighbor search, n_neighbors = 54
00:59:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:59:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87752c81de
00:59:58 Searching Annoy index using 1 thread, search_k = 5400
00:59:58 Annoy recall = 100%
01:00:02 Commencing smooth kNN distance calibration using 1 thread
01:00:10 Initializing from normalized Laplacian + noise
01:00:10 Commencing optimization for 500 epochs, with 78886 positive edges
01:00:16 Optimization finished
[1] "54 0.09"01:00:17 UMAP embedding parameters a = 1.611 b = 0.8844
01:00:17 Read 1203 rows and found 38 numeric columns
01:00:17 Using Annoy for neighbor search, n_neighbors = 54
01:00:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:00:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e472fa2
01:00:17 Searching Annoy index using 1 thread, search_k = 5400
01:00:17 Annoy recall = 100%
01:00:21 Commencing smooth kNN distance calibration using 1 thread
01:00:29 Initializing from normalized Laplacian + noise
01:00:29 Commencing optimization for 500 epochs, with 78886 positive edges
01:00:36 Optimization finished
[1] "54 0.1"01:00:36 UMAP embedding parameters a = 1.577 b = 0.8951
01:00:36 Read 1203 rows and found 38 numeric columns
01:00:36 Using Annoy for neighbor search, n_neighbors = 54
01:00:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:00:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727b79354
01:00:36 Searching Annoy index using 1 thread, search_k = 5400
01:00:37 Annoy recall = 100%
01:00:40 Commencing smooth kNN distance calibration using 1 thread
01:00:48 Initializing from normalized Laplacian + noise
01:00:48 Commencing optimization for 500 epochs, with 78886 positive edges
01:00:55 Optimization finished
[1] "54 0.11"01:00:55 UMAP embedding parameters a = 1.544 b = 0.9058
01:00:55 Read 1203 rows and found 38 numeric columns
01:00:55 Using Annoy for neighbor search, n_neighbors = 54
01:00:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:00:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f1b21ba
01:00:55 Searching Annoy index using 1 thread, search_k = 5400
01:00:56 Annoy recall = 100%
01:00:59 Commencing smooth kNN distance calibration using 1 thread
01:01:07 Initializing from normalized Laplacian + noise
01:01:07 Commencing optimization for 500 epochs, with 78886 positive edges
01:01:14 Optimization finished
[1] "54 0.12"01:01:14 UMAP embedding parameters a = 1.51 b = 0.9165
01:01:14 Read 1203 rows and found 38 numeric columns
01:01:14 Using Annoy for neighbor search, n_neighbors = 54
01:01:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:01:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87437ae076
01:01:14 Searching Annoy index using 1 thread, search_k = 5400
01:01:15 Annoy recall = 100%
01:01:19 Commencing smooth kNN distance calibration using 1 thread
01:01:26 Initializing from normalized Laplacian + noise
01:01:26 Commencing optimization for 500 epochs, with 78886 positive edges
01:01:33 Optimization finished
[1] "54 0.13"01:01:33 UMAP embedding parameters a = 1.478 b = 0.9272
01:01:33 Read 1203 rows and found 38 numeric columns
01:01:33 Using Annoy for neighbor search, n_neighbors = 54
01:01:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:01:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875051400
01:01:34 Searching Annoy index using 1 thread, search_k = 5400
01:01:34 Annoy recall = 100%
01:01:38 Commencing smooth kNN distance calibration using 1 thread
01:01:45 Initializing from normalized Laplacian + noise
01:01:45 Commencing optimization for 500 epochs, with 78886 positive edges
01:01:52 Optimization finished
[1] "54 0.14"01:01:52 UMAP embedding parameters a = 1.446 b = 0.938
01:01:52 Read 1203 rows and found 38 numeric columns
01:01:52 Using Annoy for neighbor search, n_neighbors = 54
01:01:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:01:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871feed71d
01:01:53 Searching Annoy index using 1 thread, search_k = 5400
01:01:53 Annoy recall = 100%
01:01:57 Commencing smooth kNN distance calibration using 1 thread
01:02:05 Initializing from normalized Laplacian + noise
01:02:05 Commencing optimization for 500 epochs, with 78886 positive edges
01:02:11 Optimization finished
[1] "54 0.15"01:02:12 UMAP embedding parameters a = 1.414 b = 0.9488
01:02:12 Read 1203 rows and found 38 numeric columns
01:02:12 Using Annoy for neighbor search, n_neighbors = 54
01:02:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:02:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87706a2ca6
01:02:12 Searching Annoy index using 1 thread, search_k = 5400
01:02:12 Annoy recall = 100%
01:02:16 Commencing smooth kNN distance calibration using 1 thread
01:02:24 Initializing from normalized Laplacian + noise
01:02:24 Commencing optimization for 500 epochs, with 78886 positive edges
01:02:31 Optimization finished
[1] "54 0.16"01:02:31 UMAP embedding parameters a = 1.383 b = 0.9596
01:02:31 Read 1203 rows and found 38 numeric columns
01:02:31 Using Annoy for neighbor search, n_neighbors = 54
01:02:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:02:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723e9d36f
01:02:31 Searching Annoy index using 1 thread, search_k = 5400
01:02:32 Annoy recall = 100%
01:02:35 Commencing smooth kNN distance calibration using 1 thread
01:02:43 Initializing from normalized Laplacian + noise
01:02:43 Commencing optimization for 500 epochs, with 78886 positive edges
01:02:50 Optimization finished
[1] "54 0.17"01:02:50 UMAP embedding parameters a = 1.352 b = 0.9704
01:02:50 Read 1203 rows and found 38 numeric columns
01:02:50 Using Annoy for neighbor search, n_neighbors = 54
01:02:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:02:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873aafad03
01:02:50 Searching Annoy index using 1 thread, search_k = 5400
01:02:51 Annoy recall = 100%
01:02:55 Commencing smooth kNN distance calibration using 1 thread
01:03:02 Initializing from normalized Laplacian + noise
01:03:02 Commencing optimization for 500 epochs, with 78886 positive edges
01:03:09 Optimization finished
[1] "54 0.18"01:03:09 UMAP embedding parameters a = 1.321 b = 0.9813
01:03:09 Read 1203 rows and found 38 numeric columns
01:03:09 Using Annoy for neighbor search, n_neighbors = 54
01:03:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:03:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87616166f6
01:03:10 Searching Annoy index using 1 thread, search_k = 5400
01:03:10 Annoy recall = 100%
01:03:14 Commencing smooth kNN distance calibration using 1 thread
01:03:21 Initializing from normalized Laplacian + noise
01:03:21 Commencing optimization for 500 epochs, with 78886 positive edges
01:03:28 Optimization finished
[1] "54 0.19"01:03:29 UMAP embedding parameters a = 1.292 b = 0.9921
01:03:29 Read 1203 rows and found 38 numeric columns
01:03:29 Using Annoy for neighbor search, n_neighbors = 54
01:03:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:03:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873dbb7abf
01:03:29 Searching Annoy index using 1 thread, search_k = 5400
01:03:29 Annoy recall = 100%
01:03:33 Commencing smooth kNN distance calibration using 1 thread
01:03:41 Initializing from normalized Laplacian + noise
01:03:41 Commencing optimization for 500 epochs, with 78886 positive edges
01:03:48 Optimization finished
[1] "54 0.2"01:03:48 UMAP embedding parameters a = 1.262 b = 1.003
01:03:48 Read 1203 rows and found 38 numeric columns
01:03:48 Using Annoy for neighbor search, n_neighbors = 54
01:03:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:03:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ea783fe
01:03:48 Searching Annoy index using 1 thread, search_k = 5400
01:03:49 Annoy recall = 100%
01:03:52 Commencing smooth kNN distance calibration using 1 thread
01:04:00 Initializing from normalized Laplacian + noise
01:04:00 Commencing optimization for 500 epochs, with 78886 positive edges
01:04:07 Optimization finished
[1] "55 0"01:04:07 UMAP embedding parameters a = 1.933 b = 0.7905
01:04:07 Read 1203 rows and found 38 numeric columns
01:04:07 Using Annoy for neighbor search, n_neighbors = 55
01:04:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:04:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87570c5814
01:04:07 Searching Annoy index using 1 thread, search_k = 5500
01:04:08 Annoy recall = 100%
01:04:12 Commencing smooth kNN distance calibration using 1 thread
01:04:19 Initializing from normalized Laplacian + noise
01:04:19 Commencing optimization for 500 epochs, with 80286 positive edges
01:04:26 Optimization finished
[1] "55 0.01"01:04:26 UMAP embedding parameters a = 1.896 b = 0.8006
01:04:26 Read 1203 rows and found 38 numeric columns
01:04:26 Using Annoy for neighbor search, n_neighbors = 55
01:04:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:04:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748d40b3c
01:04:27 Searching Annoy index using 1 thread, search_k = 5500
01:04:27 Annoy recall = 100%
01:04:31 Commencing smooth kNN distance calibration using 1 thread
01:04:39 Initializing from normalized Laplacian + noise
01:04:39 Commencing optimization for 500 epochs, with 80286 positive edges
01:04:46 Optimization finished
[1] "55 0.02"01:04:46 UMAP embedding parameters a = 1.859 b = 0.8109
01:04:46 Read 1203 rows and found 38 numeric columns
01:04:46 Using Annoy for neighbor search, n_neighbors = 55
01:04:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:04:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872547934
01:04:46 Searching Annoy index using 1 thread, search_k = 5500
01:04:47 Annoy recall = 100%
01:04:50 Commencing smooth kNN distance calibration using 1 thread
01:04:58 Initializing from normalized Laplacian + noise
01:04:58 Commencing optimization for 500 epochs, with 80286 positive edges
01:05:05 Optimization finished
[1] "55 0.03"01:05:05 UMAP embedding parameters a = 1.822 b = 0.8212
01:05:05 Read 1203 rows and found 38 numeric columns
01:05:05 Using Annoy for neighbor search, n_neighbors = 55
01:05:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:05:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cca5062
01:05:05 Searching Annoy index using 1 thread, search_k = 5500
01:05:06 Annoy recall = 100%
01:05:10 Commencing smooth kNN distance calibration using 1 thread
01:05:17 Initializing from normalized Laplacian + noise
01:05:17 Commencing optimization for 500 epochs, with 80286 positive edges
01:05:24 Optimization finished
[1] "55 0.04"01:05:24 UMAP embedding parameters a = 1.786 b = 0.8316
01:05:24 Read 1203 rows and found 38 numeric columns
01:05:24 Using Annoy for neighbor search, n_neighbors = 55
01:05:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:05:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c99305f
01:05:25 Searching Annoy index using 1 thread, search_k = 5500
01:05:25 Annoy recall = 100%
01:05:29 Commencing smooth kNN distance calibration using 1 thread
01:05:37 Initializing from normalized Laplacian + noise
01:05:37 Commencing optimization for 500 epochs, with 80286 positive edges
01:05:44 Optimization finished
[1] "55 0.05"01:05:44 UMAP embedding parameters a = 1.75 b = 0.8421
01:05:44 Read 1203 rows and found 38 numeric columns
01:05:44 Using Annoy for neighbor search, n_neighbors = 55
01:05:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:05:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779611dd0
01:05:44 Searching Annoy index using 1 thread, search_k = 5500
01:05:45 Annoy recall = 100%
01:05:48 Commencing smooth kNN distance calibration using 1 thread
01:05:56 Initializing from normalized Laplacian + noise
01:05:56 Commencing optimization for 500 epochs, with 80286 positive edges
01:06:03 Optimization finished
[1] "55 0.06"01:06:03 UMAP embedding parameters a = 1.715 b = 0.8526
01:06:03 Read 1203 rows and found 38 numeric columns
01:06:03 Using Annoy for neighbor search, n_neighbors = 55
01:06:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:06:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87596512e7
01:06:03 Searching Annoy index using 1 thread, search_k = 5500
01:06:04 Annoy recall = 100%
01:06:08 Commencing smooth kNN distance calibration using 1 thread
01:06:15 Initializing from normalized Laplacian + noise
01:06:15 Commencing optimization for 500 epochs, with 80286 positive edges
01:06:22 Optimization finished
[1] "55 0.07"01:06:22 UMAP embedding parameters a = 1.68 b = 0.8631
01:06:22 Read 1203 rows and found 38 numeric columns
01:06:22 Using Annoy for neighbor search, n_neighbors = 55
01:06:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:06:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87492c7719
01:06:23 Searching Annoy index using 1 thread, search_k = 5500
01:06:23 Annoy recall = 100%
01:06:27 Commencing smooth kNN distance calibration using 1 thread
01:06:35 Initializing from normalized Laplacian + noise
01:06:35 Commencing optimization for 500 epochs, with 80286 positive edges
01:06:42 Optimization finished
[1] "55 0.08"01:06:42 UMAP embedding parameters a = 1.645 b = 0.8737
01:06:42 Read 1203 rows and found 38 numeric columns
01:06:42 Using Annoy for neighbor search, n_neighbors = 55
01:06:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:06:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f0ba691
01:06:42 Searching Annoy index using 1 thread, search_k = 5500
01:06:43 Annoy recall = 100%
01:06:47 Commencing smooth kNN distance calibration using 1 thread
01:06:54 Initializing from normalized Laplacian + noise
01:06:54 Commencing optimization for 500 epochs, with 80286 positive edges
01:07:01 Optimization finished
[1] "55 0.09"01:07:01 UMAP embedding parameters a = 1.611 b = 0.8844
01:07:01 Read 1203 rows and found 38 numeric columns
01:07:01 Using Annoy for neighbor search, n_neighbors = 55
01:07:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:07:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87487f757a
01:07:02 Searching Annoy index using 1 thread, search_k = 5500
01:07:02 Annoy recall = 100%
01:07:06 Commencing smooth kNN distance calibration using 1 thread
01:07:13 Initializing from normalized Laplacian + noise
01:07:14 Commencing optimization for 500 epochs, with 80286 positive edges
01:07:20 Optimization finished
[1] "55 0.1"01:07:21 UMAP embedding parameters a = 1.577 b = 0.8951
01:07:21 Read 1203 rows and found 38 numeric columns
01:07:21 Using Annoy for neighbor search, n_neighbors = 55
01:07:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:07:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875aed9fa6
01:07:21 Searching Annoy index using 1 thread, search_k = 5500
01:07:21 Annoy recall = 100%
01:07:25 Commencing smooth kNN distance calibration using 1 thread
01:07:33 Initializing from normalized Laplacian + noise
01:07:33 Commencing optimization for 500 epochs, with 80286 positive edges
01:07:40 Optimization finished
[1] "55 0.11"01:07:40 UMAP embedding parameters a = 1.544 b = 0.9058
01:07:40 Read 1203 rows and found 38 numeric columns
01:07:40 Using Annoy for neighbor search, n_neighbors = 55
01:07:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:07:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721ecfd00
01:07:41 Searching Annoy index using 1 thread, search_k = 5500
01:07:41 Annoy recall = 100%
01:07:45 Commencing smooth kNN distance calibration using 1 thread
01:07:52 Initializing from normalized Laplacian + noise
01:07:52 Commencing optimization for 500 epochs, with 80286 positive edges
01:07:59 Optimization finished
[1] "55 0.12"01:08:00 UMAP embedding parameters a = 1.51 b = 0.9165
01:08:00 Read 1203 rows and found 38 numeric columns
01:08:00 Using Annoy for neighbor search, n_neighbors = 55
01:08:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:08:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760e05165
01:08:00 Searching Annoy index using 1 thread, search_k = 5500
01:08:00 Annoy recall = 100%
01:08:04 Commencing smooth kNN distance calibration using 1 thread
01:08:12 Initializing from normalized Laplacian + noise
01:08:12 Commencing optimization for 500 epochs, with 80286 positive edges
01:08:19 Optimization finished
[1] "55 0.13"01:08:19 UMAP embedding parameters a = 1.478 b = 0.9272
01:08:19 Read 1203 rows and found 38 numeric columns
01:08:19 Using Annoy for neighbor search, n_neighbors = 55
01:08:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:08:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f706a4a
01:08:19 Searching Annoy index using 1 thread, search_k = 5500
01:08:20 Annoy recall = 100%
01:08:24 Commencing smooth kNN distance calibration using 1 thread
01:08:31 Initializing from normalized Laplacian + noise
01:08:31 Commencing optimization for 500 epochs, with 80286 positive edges
01:08:38 Optimization finished
[1] "55 0.14"01:08:39 UMAP embedding parameters a = 1.446 b = 0.938
01:08:39 Read 1203 rows and found 38 numeric columns
01:08:39 Using Annoy for neighbor search, n_neighbors = 55
01:08:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:08:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729b701d3
01:08:39 Searching Annoy index using 1 thread, search_k = 5500
01:08:39 Annoy recall = 100%
01:08:43 Commencing smooth kNN distance calibration using 1 thread
01:08:51 Initializing from normalized Laplacian + noise
01:08:51 Commencing optimization for 500 epochs, with 80286 positive edges
01:08:58 Optimization finished
[1] "55 0.15"01:08:58 UMAP embedding parameters a = 1.414 b = 0.9488
01:08:58 Read 1203 rows and found 38 numeric columns
01:08:58 Using Annoy for neighbor search, n_neighbors = 55
01:08:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:08:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876448bc0f
01:08:58 Searching Annoy index using 1 thread, search_k = 5500
01:08:59 Annoy recall = 100%
01:09:03 Commencing smooth kNN distance calibration using 1 thread
01:09:10 Initializing from normalized Laplacian + noise
01:09:10 Commencing optimization for 500 epochs, with 80286 positive edges
01:09:17 Optimization finished
[1] "55 0.16"01:09:17 UMAP embedding parameters a = 1.383 b = 0.9596
01:09:17 Read 1203 rows and found 38 numeric columns
01:09:17 Using Annoy for neighbor search, n_neighbors = 55
01:09:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:09:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a5ab936
01:09:18 Searching Annoy index using 1 thread, search_k = 5500
01:09:18 Annoy recall = 100%
01:09:22 Commencing smooth kNN distance calibration using 1 thread
01:09:30 Initializing from normalized Laplacian + noise
01:09:30 Commencing optimization for 500 epochs, with 80286 positive edges
01:09:37 Optimization finished
[1] "55 0.17"01:09:37 UMAP embedding parameters a = 1.352 b = 0.9704
01:09:37 Read 1203 rows and found 38 numeric columns
01:09:37 Using Annoy for neighbor search, n_neighbors = 55
01:09:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:09:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877318fa79
01:09:37 Searching Annoy index using 1 thread, search_k = 5500
01:09:38 Annoy recall = 100%
01:09:42 Commencing smooth kNN distance calibration using 1 thread
01:09:49 Initializing from normalized Laplacian + noise
01:09:49 Commencing optimization for 500 epochs, with 80286 positive edges
01:09:56 Optimization finished
[1] "55 0.18"01:09:57 UMAP embedding parameters a = 1.321 b = 0.9813
01:09:57 Read 1203 rows and found 38 numeric columns
01:09:57 Using Annoy for neighbor search, n_neighbors = 55
01:09:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:09:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759753ded
01:09:57 Searching Annoy index using 1 thread, search_k = 5500
01:09:57 Annoy recall = 100%
01:10:01 Commencing smooth kNN distance calibration using 1 thread
01:10:09 Initializing from normalized Laplacian + noise
01:10:09 Commencing optimization for 500 epochs, with 80286 positive edges
01:10:16 Optimization finished
[1] "55 0.19"01:10:16 UMAP embedding parameters a = 1.292 b = 0.9921
01:10:16 Read 1203 rows and found 38 numeric columns
01:10:16 Using Annoy for neighbor search, n_neighbors = 55
01:10:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:10:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718a1e8d8
01:10:16 Searching Annoy index using 1 thread, search_k = 5500
01:10:17 Annoy recall = 100%
01:10:21 Commencing smooth kNN distance calibration using 1 thread
01:10:28 Initializing from normalized Laplacian + noise
01:10:28 Commencing optimization for 500 epochs, with 80286 positive edges
01:10:35 Optimization finished
[1] "55 0.2"01:10:36 UMAP embedding parameters a = 1.262 b = 1.003
01:10:36 Read 1203 rows and found 38 numeric columns
01:10:36 Using Annoy for neighbor search, n_neighbors = 55
01:10:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:10:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ad08dcd
01:10:36 Searching Annoy index using 1 thread, search_k = 5500
01:10:36 Annoy recall = 100%
01:10:40 Commencing smooth kNN distance calibration using 1 thread
01:10:48 Initializing from normalized Laplacian + noise
01:10:48 Commencing optimization for 500 epochs, with 80286 positive edges
01:10:55 Optimization finished
[1] "56 0"01:10:55 UMAP embedding parameters a = 1.933 b = 0.7905
01:10:55 Read 1203 rows and found 38 numeric columns
01:10:55 Using Annoy for neighbor search, n_neighbors = 56
01:10:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:10:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748905fa7
01:10:56 Searching Annoy index using 1 thread, search_k = 5600
01:10:56 Annoy recall = 100%
01:11:00 Commencing smooth kNN distance calibration using 1 thread
01:11:07 Initializing from normalized Laplacian + noise
01:11:07 Commencing optimization for 500 epochs, with 81636 positive edges
01:11:14 Optimization finished
[1] "56 0.01"01:11:15 UMAP embedding parameters a = 1.896 b = 0.8006
01:11:15 Read 1203 rows and found 38 numeric columns
01:11:15 Using Annoy for neighbor search, n_neighbors = 56
01:11:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:11:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c1cc94e
01:11:15 Searching Annoy index using 1 thread, search_k = 5600
01:11:15 Annoy recall = 100%
01:11:19 Commencing smooth kNN distance calibration using 1 thread
01:11:27 Initializing from normalized Laplacian + noise
01:11:27 Commencing optimization for 500 epochs, with 81636 positive edges
01:11:34 Optimization finished
[1] "56 0.02"01:11:34 UMAP embedding parameters a = 1.859 b = 0.8109
01:11:34 Read 1203 rows and found 38 numeric columns
01:11:34 Using Annoy for neighbor search, n_neighbors = 56
01:11:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:11:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871fd5a1cd
01:11:35 Searching Annoy index using 1 thread, search_k = 5600
01:11:35 Annoy recall = 100%
01:11:39 Commencing smooth kNN distance calibration using 1 thread
01:11:47 Initializing from normalized Laplacian + noise
01:11:47 Commencing optimization for 500 epochs, with 81636 positive edges
01:11:54 Optimization finished
[1] "56 0.03"01:11:54 UMAP embedding parameters a = 1.822 b = 0.8212
01:11:54 Read 1203 rows and found 38 numeric columns
01:11:54 Using Annoy for neighbor search, n_neighbors = 56
01:11:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:11:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87687f36c4
01:11:54 Searching Annoy index using 1 thread, search_k = 5600
01:11:55 Annoy recall = 100%
01:11:59 Commencing smooth kNN distance calibration using 1 thread
01:12:06 Initializing from normalized Laplacian + noise
01:12:06 Commencing optimization for 500 epochs, with 81636 positive edges
01:12:13 Optimization finished
[1] "56 0.04"01:12:14 UMAP embedding parameters a = 1.786 b = 0.8316
01:12:14 Read 1203 rows and found 38 numeric columns
01:12:14 Using Annoy for neighbor search, n_neighbors = 56
01:12:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:12:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c86f5f4
01:12:14 Searching Annoy index using 1 thread, search_k = 5600
01:12:14 Annoy recall = 100%
01:12:18 Commencing smooth kNN distance calibration using 1 thread
01:12:26 Initializing from normalized Laplacian + noise
01:12:26 Commencing optimization for 500 epochs, with 81636 positive edges
01:12:33 Optimization finished
[1] "56 0.05"01:12:33 UMAP embedding parameters a = 1.75 b = 0.8421
01:12:33 Read 1203 rows and found 38 numeric columns
01:12:33 Using Annoy for neighbor search, n_neighbors = 56
01:12:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:12:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743bf753c
01:12:34 Searching Annoy index using 1 thread, search_k = 5600
01:12:34 Annoy recall = 100%
01:12:38 Commencing smooth kNN distance calibration using 1 thread
01:12:46 Initializing from normalized Laplacian + noise
01:12:46 Commencing optimization for 500 epochs, with 81636 positive edges
01:12:53 Optimization finished
[1] "56 0.06"01:12:53 UMAP embedding parameters a = 1.715 b = 0.8526
01:12:53 Read 1203 rows and found 38 numeric columns
01:12:53 Using Annoy for neighbor search, n_neighbors = 56
01:12:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:12:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87232ee3c8
01:12:53 Searching Annoy index using 1 thread, search_k = 5600
01:12:54 Annoy recall = 100%
01:12:58 Commencing smooth kNN distance calibration using 1 thread
01:13:05 Initializing from normalized Laplacian + noise
01:13:06 Commencing optimization for 500 epochs, with 81636 positive edges
01:13:13 Optimization finished
[1] "56 0.07"01:13:13 UMAP embedding parameters a = 1.68 b = 0.8631
01:13:13 Read 1203 rows and found 38 numeric columns
01:13:13 Using Annoy for neighbor search, n_neighbors = 56
01:13:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:13:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872de85ceb
01:13:13 Searching Annoy index using 1 thread, search_k = 5600
01:13:14 Annoy recall = 100%
01:13:17 Commencing smooth kNN distance calibration using 1 thread
01:13:25 Initializing from normalized Laplacian + noise
01:13:25 Commencing optimization for 500 epochs, with 81636 positive edges
01:13:32 Optimization finished
[1] "56 0.08"01:13:33 UMAP embedding parameters a = 1.645 b = 0.8737
01:13:33 Read 1203 rows and found 38 numeric columns
01:13:33 Using Annoy for neighbor search, n_neighbors = 56
01:13:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:13:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717aeffc
01:13:33 Searching Annoy index using 1 thread, search_k = 5600
01:13:33 Annoy recall = 100%
01:13:37 Commencing smooth kNN distance calibration using 1 thread
01:13:45 Initializing from normalized Laplacian + noise
01:13:45 Commencing optimization for 500 epochs, with 81636 positive edges
01:13:52 Optimization finished
[1] "56 0.09"01:13:52 UMAP embedding parameters a = 1.611 b = 0.8844
01:13:52 Read 1203 rows and found 38 numeric columns
01:13:52 Using Annoy for neighbor search, n_neighbors = 56
01:13:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:13:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d667c6
01:13:53 Searching Annoy index using 1 thread, search_k = 5600
01:13:53 Annoy recall = 100%
01:13:57 Commencing smooth kNN distance calibration using 1 thread
01:14:05 Initializing from normalized Laplacian + noise
01:14:05 Commencing optimization for 500 epochs, with 81636 positive edges
01:14:12 Optimization finished
[1] "56 0.1"01:14:12 UMAP embedding parameters a = 1.577 b = 0.8951
01:14:12 Read 1203 rows and found 38 numeric columns
01:14:12 Using Annoy for neighbor search, n_neighbors = 56
01:14:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:14:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f4b4ff
01:14:12 Searching Annoy index using 1 thread, search_k = 5600
01:14:13 Annoy recall = 100%
01:14:17 Commencing smooth kNN distance calibration using 1 thread
01:14:25 Initializing from normalized Laplacian + noise
01:14:25 Commencing optimization for 500 epochs, with 81636 positive edges
01:14:32 Optimization finished
[1] "56 0.11"01:14:32 UMAP embedding parameters a = 1.544 b = 0.9058
01:14:32 Read 1203 rows and found 38 numeric columns
01:14:32 Using Annoy for neighbor search, n_neighbors = 56
01:14:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:14:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a4efb38
01:14:32 Searching Annoy index using 1 thread, search_k = 5600
01:14:33 Annoy recall = 100%
01:14:37 Commencing smooth kNN distance calibration using 1 thread
01:14:44 Initializing from normalized Laplacian + noise
01:14:44 Commencing optimization for 500 epochs, with 81636 positive edges
01:14:52 Optimization finished
[1] "56 0.12"01:14:52 UMAP embedding parameters a = 1.51 b = 0.9165
01:14:52 Read 1203 rows and found 38 numeric columns
01:14:52 Using Annoy for neighbor search, n_neighbors = 56
01:14:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:14:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742ae0fa
01:14:52 Searching Annoy index using 1 thread, search_k = 5600
01:14:53 Annoy recall = 100%
01:14:57 Commencing smooth kNN distance calibration using 1 thread
01:15:04 Initializing from normalized Laplacian + noise
01:15:04 Commencing optimization for 500 epochs, with 81636 positive edges
01:15:11 Optimization finished
[1] "56 0.13"01:15:12 UMAP embedding parameters a = 1.478 b = 0.9272
01:15:12 Read 1203 rows and found 38 numeric columns
01:15:12 Using Annoy for neighbor search, n_neighbors = 56
01:15:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:15:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741bf0562
01:15:12 Searching Annoy index using 1 thread, search_k = 5600
01:15:12 Annoy recall = 100%
01:15:16 Commencing smooth kNN distance calibration using 1 thread
01:15:24 Initializing from normalized Laplacian + noise
01:15:24 Commencing optimization for 500 epochs, with 81636 positive edges
01:15:31 Optimization finished
[1] "56 0.14"01:15:31 UMAP embedding parameters a = 1.446 b = 0.938
01:15:31 Read 1203 rows and found 38 numeric columns
01:15:31 Using Annoy for neighbor search, n_neighbors = 56
01:15:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:15:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e82b97
01:15:32 Searching Annoy index using 1 thread, search_k = 5600
01:15:32 Annoy recall = 100%
01:15:36 Commencing smooth kNN distance calibration using 1 thread
01:15:44 Initializing from normalized Laplacian + noise
01:15:44 Commencing optimization for 500 epochs, with 81636 positive edges
01:15:51 Optimization finished
[1] "56 0.15"01:15:51 UMAP embedding parameters a = 1.414 b = 0.9488
01:15:51 Read 1203 rows and found 38 numeric columns
01:15:51 Using Annoy for neighbor search, n_neighbors = 56
01:15:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:15:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d8bfecb
01:15:52 Searching Annoy index using 1 thread, search_k = 5600
01:15:52 Annoy recall = 100%
01:15:56 Commencing smooth kNN distance calibration using 1 thread
01:16:04 Initializing from normalized Laplacian + noise
01:16:04 Commencing optimization for 500 epochs, with 81636 positive edges
01:16:11 Optimization finished
[1] "56 0.16"01:16:11 UMAP embedding parameters a = 1.383 b = 0.9596
01:16:11 Read 1203 rows and found 38 numeric columns
01:16:11 Using Annoy for neighbor search, n_neighbors = 56
01:16:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:16:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b241849
01:16:12 Searching Annoy index using 1 thread, search_k = 5600
01:16:12 Annoy recall = 100%
01:16:16 Commencing smooth kNN distance calibration using 1 thread
01:16:24 Initializing from normalized Laplacian + noise
01:16:24 Commencing optimization for 500 epochs, with 81636 positive edges
01:16:31 Optimization finished
[1] "56 0.17"01:16:31 UMAP embedding parameters a = 1.352 b = 0.9704
01:16:31 Read 1203 rows and found 38 numeric columns
01:16:31 Using Annoy for neighbor search, n_neighbors = 56
01:16:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:16:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875014a2b0
01:16:31 Searching Annoy index using 1 thread, search_k = 5600
01:16:32 Annoy recall = 100%
01:16:36 Commencing smooth kNN distance calibration using 1 thread
01:16:44 Initializing from normalized Laplacian + noise
01:16:44 Commencing optimization for 500 epochs, with 81636 positive edges
01:16:51 Optimization finished
[1] "56 0.18"01:16:51 UMAP embedding parameters a = 1.321 b = 0.9813
01:16:51 Read 1203 rows and found 38 numeric columns
01:16:51 Using Annoy for neighbor search, n_neighbors = 56
01:16:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:16:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c97a55c
01:16:51 Searching Annoy index using 1 thread, search_k = 5600
01:16:52 Annoy recall = 100%
01:16:56 Commencing smooth kNN distance calibration using 1 thread
01:17:04 Initializing from normalized Laplacian + noise
01:17:04 Commencing optimization for 500 epochs, with 81636 positive edges
01:17:11 Optimization finished
[1] "56 0.19"01:17:11 UMAP embedding parameters a = 1.292 b = 0.9921
01:17:11 Read 1203 rows and found 38 numeric columns
01:17:11 Using Annoy for neighbor search, n_neighbors = 56
01:17:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:17:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763a38dc4
01:17:11 Searching Annoy index using 1 thread, search_k = 5600
01:17:12 Annoy recall = 100%
01:17:16 Commencing smooth kNN distance calibration using 1 thread
01:17:23 Initializing from normalized Laplacian + noise
01:17:23 Commencing optimization for 500 epochs, with 81636 positive edges
01:17:31 Optimization finished
[1] "56 0.2"01:17:31 UMAP embedding parameters a = 1.262 b = 1.003
01:17:31 Read 1203 rows and found 38 numeric columns
01:17:31 Using Annoy for neighbor search, n_neighbors = 56
01:17:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:17:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b024256
01:17:31 Searching Annoy index using 1 thread, search_k = 5600
01:17:32 Annoy recall = 100%
01:17:35 Commencing smooth kNN distance calibration using 1 thread
01:17:43 Initializing from normalized Laplacian + noise
01:17:43 Commencing optimization for 500 epochs, with 81636 positive edges
01:17:51 Optimization finished
[1] "57 0"01:17:51 UMAP embedding parameters a = 1.933 b = 0.7905
01:17:51 Read 1203 rows and found 38 numeric columns
01:17:51 Using Annoy for neighbor search, n_neighbors = 57
01:17:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:17:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e84a25c
01:17:51 Searching Annoy index using 1 thread, search_k = 5700
01:17:52 Annoy recall = 100%
01:17:55 Commencing smooth kNN distance calibration using 1 thread
01:18:03 Initializing from normalized Laplacian + noise
01:18:03 Commencing optimization for 500 epochs, with 83058 positive edges
01:18:11 Optimization finished
[1] "57 0.01"01:18:11 UMAP embedding parameters a = 1.896 b = 0.8006
01:18:11 Read 1203 rows and found 38 numeric columns
01:18:11 Using Annoy for neighbor search, n_neighbors = 57
01:18:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:18:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874483df29
01:18:11 Searching Annoy index using 1 thread, search_k = 5700
01:18:12 Annoy recall = 100%
01:18:15 Commencing smooth kNN distance calibration using 1 thread
01:18:23 Initializing from normalized Laplacian + noise
01:18:23 Commencing optimization for 500 epochs, with 83058 positive edges
01:18:30 Optimization finished
[1] "57 0.02"01:18:31 UMAP embedding parameters a = 1.859 b = 0.8109
01:18:31 Read 1203 rows and found 38 numeric columns
01:18:31 Using Annoy for neighbor search, n_neighbors = 57
01:18:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:18:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a72aca1
01:18:31 Searching Annoy index using 1 thread, search_k = 5700
01:18:31 Annoy recall = 100%
01:18:35 Commencing smooth kNN distance calibration using 1 thread
01:18:43 Initializing from normalized Laplacian + noise
01:18:43 Commencing optimization for 500 epochs, with 83058 positive edges
01:18:50 Optimization finished
[1] "57 0.03"01:18:51 UMAP embedding parameters a = 1.822 b = 0.8212
01:18:51 Read 1203 rows and found 38 numeric columns
01:18:51 Using Annoy for neighbor search, n_neighbors = 57
01:18:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:18:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87583ba42f
01:18:51 Searching Annoy index using 1 thread, search_k = 5700
01:18:51 Annoy recall = 100%
01:18:55 Commencing smooth kNN distance calibration using 1 thread
01:19:03 Initializing from normalized Laplacian + noise
01:19:03 Commencing optimization for 500 epochs, with 83058 positive edges
01:19:11 Optimization finished
[1] "57 0.04"01:19:11 UMAP embedding parameters a = 1.786 b = 0.8316
01:19:11 Read 1203 rows and found 38 numeric columns
01:19:11 Using Annoy for neighbor search, n_neighbors = 57
01:19:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:19:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728cc9b38
01:19:11 Searching Annoy index using 1 thread, search_k = 5700
01:19:12 Annoy recall = 100%
01:19:15 Commencing smooth kNN distance calibration using 1 thread
01:19:23 Initializing from normalized Laplacian + noise
01:19:23 Commencing optimization for 500 epochs, with 83058 positive edges
01:19:30 Optimization finished
[1] "57 0.05"01:19:31 UMAP embedding parameters a = 1.75 b = 0.8421
01:19:31 Read 1203 rows and found 38 numeric columns
01:19:31 Using Annoy for neighbor search, n_neighbors = 57
01:19:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:19:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734cd65d7
01:19:31 Searching Annoy index using 1 thread, search_k = 5700
01:19:31 Annoy recall = 100%
01:19:35 Commencing smooth kNN distance calibration using 1 thread
01:19:43 Initializing from normalized Laplacian + noise
01:19:43 Commencing optimization for 500 epochs, with 83058 positive edges
01:19:51 Optimization finished
[1] "57 0.06"01:19:51 UMAP embedding parameters a = 1.715 b = 0.8526
01:19:51 Read 1203 rows and found 38 numeric columns
01:19:51 Using Annoy for neighbor search, n_neighbors = 57
01:19:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:19:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b549ea8
01:19:51 Searching Annoy index using 1 thread, search_k = 5700
01:19:52 Annoy recall = 100%
01:19:56 Commencing smooth kNN distance calibration using 1 thread
01:20:03 Initializing from normalized Laplacian + noise
01:20:04 Commencing optimization for 500 epochs, with 83058 positive edges
01:20:11 Optimization finished
[1] "57 0.07"01:20:11 UMAP embedding parameters a = 1.68 b = 0.8631
01:20:11 Read 1203 rows and found 38 numeric columns
01:20:11 Using Annoy for neighbor search, n_neighbors = 57
01:20:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:20:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87241d926
01:20:11 Searching Annoy index using 1 thread, search_k = 5700
01:20:12 Annoy recall = 100%
01:20:16 Commencing smooth kNN distance calibration using 1 thread
01:20:23 Initializing from normalized Laplacian + noise
01:20:23 Commencing optimization for 500 epochs, with 83058 positive edges
01:20:31 Optimization finished
[1] "57 0.08"01:20:31 UMAP embedding parameters a = 1.645 b = 0.8737
01:20:31 Read 1203 rows and found 38 numeric columns
01:20:31 Using Annoy for neighbor search, n_neighbors = 57
01:20:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:20:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d6f4eb0
01:20:31 Searching Annoy index using 1 thread, search_k = 5700
01:20:32 Annoy recall = 100%
01:20:36 Commencing smooth kNN distance calibration using 1 thread
01:20:43 Initializing from normalized Laplacian + noise
01:20:44 Commencing optimization for 500 epochs, with 83058 positive edges
01:20:51 Optimization finished
[1] "57 0.09"01:20:51 UMAP embedding parameters a = 1.611 b = 0.8844
01:20:51 Read 1203 rows and found 38 numeric columns
01:20:51 Using Annoy for neighbor search, n_neighbors = 57
01:20:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:20:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766252c75
01:20:51 Searching Annoy index using 1 thread, search_k = 5700
01:20:52 Annoy recall = 100%
01:20:56 Commencing smooth kNN distance calibration using 1 thread
01:21:04 Initializing from normalized Laplacian + noise
01:21:04 Commencing optimization for 500 epochs, with 83058 positive edges
01:21:11 Optimization finished
[1] "57 0.1"01:21:11 UMAP embedding parameters a = 1.577 b = 0.8951
01:21:11 Read 1203 rows and found 38 numeric columns
01:21:11 Using Annoy for neighbor search, n_neighbors = 57
01:21:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:21:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ad238cd
01:21:11 Searching Annoy index using 1 thread, search_k = 5700
01:21:12 Annoy recall = 100%
01:21:16 Commencing smooth kNN distance calibration using 1 thread
01:21:24 Initializing from normalized Laplacian + noise
01:21:24 Commencing optimization for 500 epochs, with 83058 positive edges
01:21:31 Optimization finished
[1] "57 0.11"01:21:31 UMAP embedding parameters a = 1.544 b = 0.9058
01:21:31 Read 1203 rows and found 38 numeric columns
01:21:31 Using Annoy for neighbor search, n_neighbors = 57
01:21:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:21:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87298c17fe
01:21:31 Searching Annoy index using 1 thread, search_k = 5700
01:21:32 Annoy recall = 100%
01:21:36 Commencing smooth kNN distance calibration using 1 thread
01:21:44 Initializing from normalized Laplacian + noise
01:21:44 Commencing optimization for 500 epochs, with 83058 positive edges
01:21:51 Optimization finished
[1] "57 0.12"01:21:51 UMAP embedding parameters a = 1.51 b = 0.9165
01:21:51 Read 1203 rows and found 38 numeric columns
01:21:51 Using Annoy for neighbor search, n_neighbors = 57
01:21:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:21:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875face43
01:21:52 Searching Annoy index using 1 thread, search_k = 5700
01:21:52 Annoy recall = 100%
01:21:56 Commencing smooth kNN distance calibration using 1 thread
01:22:04 Initializing from normalized Laplacian + noise
01:22:04 Commencing optimization for 500 epochs, with 83058 positive edges
01:22:11 Optimization finished
[1] "57 0.13"01:22:12 UMAP embedding parameters a = 1.478 b = 0.9272
01:22:12 Read 1203 rows and found 38 numeric columns
01:22:12 Using Annoy for neighbor search, n_neighbors = 57
01:22:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:22:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733516f92
01:22:12 Searching Annoy index using 1 thread, search_k = 5700
01:22:12 Annoy recall = 100%
01:22:16 Commencing smooth kNN distance calibration using 1 thread
01:22:24 Initializing from normalized Laplacian + noise
01:22:24 Commencing optimization for 500 epochs, with 83058 positive edges
01:22:31 Optimization finished
[1] "57 0.14"01:22:32 UMAP embedding parameters a = 1.446 b = 0.938
01:22:32 Read 1203 rows and found 38 numeric columns
01:22:32 Using Annoy for neighbor search, n_neighbors = 57
01:22:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:22:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776130df3
01:22:32 Searching Annoy index using 1 thread, search_k = 5700
01:22:32 Annoy recall = 100%
01:22:36 Commencing smooth kNN distance calibration using 1 thread
01:22:44 Initializing from normalized Laplacian + noise
01:22:44 Commencing optimization for 500 epochs, with 83058 positive edges
01:22:52 Optimization finished
[1] "57 0.15"01:22:52 UMAP embedding parameters a = 1.414 b = 0.9488
01:22:52 Read 1203 rows and found 38 numeric columns
01:22:52 Using Annoy for neighbor search, n_neighbors = 57
01:22:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:22:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749ba437f
01:22:52 Searching Annoy index using 1 thread, search_k = 5700
01:22:53 Annoy recall = 100%
01:22:57 Commencing smooth kNN distance calibration using 1 thread
01:23:05 Initializing from normalized Laplacian + noise
01:23:05 Commencing optimization for 500 epochs, with 83058 positive edges
01:23:12 Optimization finished
[1] "57 0.16"01:23:12 UMAP embedding parameters a = 1.383 b = 0.9596
01:23:12 Read 1203 rows and found 38 numeric columns
01:23:12 Using Annoy for neighbor search, n_neighbors = 57
01:23:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:23:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875680535a
01:23:12 Searching Annoy index using 1 thread, search_k = 5700
01:23:13 Annoy recall = 100%
01:23:17 Commencing smooth kNN distance calibration using 1 thread
01:23:25 Initializing from normalized Laplacian + noise
01:23:25 Commencing optimization for 500 epochs, with 83058 positive edges
01:23:32 Optimization finished
[1] "57 0.17"01:23:32 UMAP embedding parameters a = 1.352 b = 0.9704
01:23:32 Read 1203 rows and found 38 numeric columns
01:23:32 Using Annoy for neighbor search, n_neighbors = 57
01:23:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:23:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723fb6ade
01:23:33 Searching Annoy index using 1 thread, search_k = 5700
01:23:33 Annoy recall = 100%
01:23:37 Commencing smooth kNN distance calibration using 1 thread
01:23:45 Initializing from normalized Laplacian + noise
01:23:45 Commencing optimization for 500 epochs, with 83058 positive edges
01:23:52 Optimization finished
[1] "57 0.18"01:23:52 UMAP embedding parameters a = 1.321 b = 0.9813
01:23:52 Read 1203 rows and found 38 numeric columns
01:23:52 Using Annoy for neighbor search, n_neighbors = 57
01:23:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:23:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b35337b
01:23:53 Searching Annoy index using 1 thread, search_k = 5700
01:23:53 Annoy recall = 100%
01:23:57 Commencing smooth kNN distance calibration using 1 thread
01:24:05 Initializing from normalized Laplacian + noise
01:24:05 Commencing optimization for 500 epochs, with 83058 positive edges
01:24:13 Optimization finished
[1] "57 0.19"01:24:13 UMAP embedding parameters a = 1.292 b = 0.9921
01:24:13 Read 1203 rows and found 38 numeric columns
01:24:13 Using Annoy for neighbor search, n_neighbors = 57
01:24:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:24:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875856bb20
01:24:13 Searching Annoy index using 1 thread, search_k = 5700
01:24:14 Annoy recall = 100%
01:24:18 Commencing smooth kNN distance calibration using 1 thread
01:24:26 Initializing from normalized Laplacian + noise
01:24:26 Commencing optimization for 500 epochs, with 83058 positive edges
01:24:33 Optimization finished
[1] "57 0.2"01:24:33 UMAP embedding parameters a = 1.262 b = 1.003
01:24:33 Read 1203 rows and found 38 numeric columns
01:24:33 Using Annoy for neighbor search, n_neighbors = 57
01:24:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:24:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728f01fdd
01:24:33 Searching Annoy index using 1 thread, search_k = 5700
01:24:34 Annoy recall = 100%
01:24:38 Commencing smooth kNN distance calibration using 1 thread
01:24:46 Initializing from normalized Laplacian + noise
01:24:46 Commencing optimization for 500 epochs, with 83058 positive edges
01:24:53 Optimization finished
[1] "58 0"01:24:53 UMAP embedding parameters a = 1.933 b = 0.7905
01:24:53 Read 1203 rows and found 38 numeric columns
01:24:53 Using Annoy for neighbor search, n_neighbors = 58
01:24:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:24:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715842eb4
01:24:54 Searching Annoy index using 1 thread, search_k = 5800
01:24:54 Annoy recall = 100%
01:24:58 Commencing smooth kNN distance calibration using 1 thread
01:25:06 Initializing from normalized Laplacian + noise
01:25:06 Commencing optimization for 500 epochs, with 84444 positive edges
01:25:13 Optimization finished
[1] "58 0.01"01:25:14 UMAP embedding parameters a = 1.896 b = 0.8006
01:25:14 Read 1203 rows and found 38 numeric columns
01:25:14 Using Annoy for neighbor search, n_neighbors = 58
01:25:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:25:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c819c1b
01:25:14 Searching Annoy index using 1 thread, search_k = 5800
01:25:14 Annoy recall = 100%
01:25:19 Commencing smooth kNN distance calibration using 1 thread
01:25:27 Initializing from normalized Laplacian + noise
01:25:27 Commencing optimization for 500 epochs, with 84444 positive edges
01:25:34 Optimization finished
[1] "58 0.02"01:25:34 UMAP embedding parameters a = 1.859 b = 0.8109
01:25:34 Read 1203 rows and found 38 numeric columns
01:25:34 Using Annoy for neighbor search, n_neighbors = 58
01:25:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:25:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aaf253f
01:25:35 Searching Annoy index using 1 thread, search_k = 5800
01:25:35 Annoy recall = 100%
01:25:39 Commencing smooth kNN distance calibration using 1 thread
01:25:47 Initializing from normalized Laplacian + noise
01:25:48 Commencing optimization for 500 epochs, with 84444 positive edges
01:25:55 Optimization finished
[1] "58 0.03"01:25:55 UMAP embedding parameters a = 1.822 b = 0.8212
01:25:55 Read 1203 rows and found 38 numeric columns
01:25:55 Using Annoy for neighbor search, n_neighbors = 58
01:25:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:25:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c6c5a4b
01:25:55 Searching Annoy index using 1 thread, search_k = 5800
01:25:56 Annoy recall = 100%
01:26:00 Commencing smooth kNN distance calibration using 1 thread
01:26:08 Initializing from normalized Laplacian + noise
01:26:08 Commencing optimization for 500 epochs, with 84444 positive edges
01:26:16 Optimization finished
[1] "58 0.04"01:26:16 UMAP embedding parameters a = 1.786 b = 0.8316
01:26:16 Read 1203 rows and found 38 numeric columns
01:26:16 Using Annoy for neighbor search, n_neighbors = 58
01:26:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:26:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a0d9ae6
01:26:16 Searching Annoy index using 1 thread, search_k = 5800
01:26:17 Annoy recall = 100%
01:26:21 Commencing smooth kNN distance calibration using 1 thread
01:26:29 Initializing from normalized Laplacian + noise
01:26:29 Commencing optimization for 500 epochs, with 84444 positive edges
01:26:36 Optimization finished
[1] "58 0.05"01:26:37 UMAP embedding parameters a = 1.75 b = 0.8421
01:26:37 Read 1203 rows and found 38 numeric columns
01:26:37 Using Annoy for neighbor search, n_neighbors = 58
01:26:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:26:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d33d89
01:26:37 Searching Annoy index using 1 thread, search_k = 5800
01:26:37 Annoy recall = 100%
01:26:41 Commencing smooth kNN distance calibration using 1 thread
01:26:50 Initializing from normalized Laplacian + noise
01:26:50 Commencing optimization for 500 epochs, with 84444 positive edges
01:26:57 Optimization finished
[1] "58 0.06"01:26:57 UMAP embedding parameters a = 1.715 b = 0.8526
01:26:57 Read 1203 rows and found 38 numeric columns
01:26:57 Using Annoy for neighbor search, n_neighbors = 58
01:26:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:26:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c80fcfc
01:26:58 Searching Annoy index using 1 thread, search_k = 5800
01:26:58 Annoy recall = 100%
01:27:02 Commencing smooth kNN distance calibration using 1 thread
01:27:11 Initializing from normalized Laplacian + noise
01:27:11 Commencing optimization for 500 epochs, with 84444 positive edges
01:27:18 Optimization finished
[1] "58 0.07"01:27:18 UMAP embedding parameters a = 1.68 b = 0.8631
01:27:18 Read 1203 rows and found 38 numeric columns
01:27:18 Using Annoy for neighbor search, n_neighbors = 58
01:27:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:27:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766a54042
01:27:19 Searching Annoy index using 1 thread, search_k = 5800
01:27:19 Annoy recall = 100%
01:27:23 Commencing smooth kNN distance calibration using 1 thread
01:27:31 Initializing from normalized Laplacian + noise
01:27:31 Commencing optimization for 500 epochs, with 84444 positive edges
01:27:39 Optimization finished
[1] "58 0.08"01:27:39 UMAP embedding parameters a = 1.645 b = 0.8737
01:27:39 Read 1203 rows and found 38 numeric columns
01:27:39 Using Annoy for neighbor search, n_neighbors = 58
01:27:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:27:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876976cb4d
01:27:39 Searching Annoy index using 1 thread, search_k = 5800
01:27:40 Annoy recall = 100%
01:27:44 Commencing smooth kNN distance calibration using 1 thread
01:27:52 Initializing from normalized Laplacian + noise
01:27:52 Commencing optimization for 500 epochs, with 84444 positive edges
01:28:00 Optimization finished
[1] "58 0.09"01:28:00 UMAP embedding parameters a = 1.611 b = 0.8844
01:28:00 Read 1203 rows and found 38 numeric columns
01:28:00 Using Annoy for neighbor search, n_neighbors = 58
01:28:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:28:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717833f52
01:28:00 Searching Annoy index using 1 thread, search_k = 5800
01:28:01 Annoy recall = 100%
01:28:05 Commencing smooth kNN distance calibration using 1 thread
01:28:13 Initializing from normalized Laplacian + noise
01:28:13 Commencing optimization for 500 epochs, with 84444 positive edges
01:28:21 Optimization finished
[1] "58 0.1"01:28:21 UMAP embedding parameters a = 1.577 b = 0.8951
01:28:21 Read 1203 rows and found 38 numeric columns
01:28:21 Using Annoy for neighbor search, n_neighbors = 58
01:28:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:28:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871529e29e
01:28:21 Searching Annoy index using 1 thread, search_k = 5800
01:28:22 Annoy recall = 100%
01:28:26 Commencing smooth kNN distance calibration using 1 thread
01:28:34 Initializing from normalized Laplacian + noise
01:28:34 Commencing optimization for 500 epochs, with 84444 positive edges
01:28:41 Optimization finished
[1] "58 0.11"01:28:41 UMAP embedding parameters a = 1.544 b = 0.9058
01:28:42 Read 1203 rows and found 38 numeric columns
01:28:42 Using Annoy for neighbor search, n_neighbors = 58
01:28:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:28:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dfaaa76
01:28:42 Searching Annoy index using 1 thread, search_k = 5800
01:28:42 Annoy recall = 100%
01:28:46 Commencing smooth kNN distance calibration using 1 thread
01:28:55 Initializing from normalized Laplacian + noise
01:28:55 Commencing optimization for 500 epochs, with 84444 positive edges
01:29:02 Optimization finished
[1] "58 0.12"01:29:02 UMAP embedding parameters a = 1.51 b = 0.9165
01:29:02 Read 1203 rows and found 38 numeric columns
01:29:02 Using Annoy for neighbor search, n_neighbors = 58
01:29:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:29:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711f5ebf3
01:29:03 Searching Annoy index using 1 thread, search_k = 5800
01:29:03 Annoy recall = 100%
01:29:07 Commencing smooth kNN distance calibration using 1 thread
01:29:16 Initializing from normalized Laplacian + noise
01:29:16 Commencing optimization for 500 epochs, with 84444 positive edges
01:29:23 Optimization finished
[1] "58 0.13"01:29:23 UMAP embedding parameters a = 1.478 b = 0.9272
01:29:23 Read 1203 rows and found 38 numeric columns
01:29:23 Using Annoy for neighbor search, n_neighbors = 58
01:29:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:29:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d6586ce
01:29:24 Searching Annoy index using 1 thread, search_k = 5800
01:29:24 Annoy recall = 100%
01:29:28 Commencing smooth kNN distance calibration using 1 thread
01:29:37 Initializing from normalized Laplacian + noise
01:29:37 Commencing optimization for 500 epochs, with 84444 positive edges
01:29:44 Optimization finished
[1] "58 0.14"01:29:44 UMAP embedding parameters a = 1.446 b = 0.938
01:29:44 Read 1203 rows and found 38 numeric columns
01:29:44 Using Annoy for neighbor search, n_neighbors = 58
01:29:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:29:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756c745ae
01:29:45 Searching Annoy index using 1 thread, search_k = 5800
01:29:45 Annoy recall = 100%
01:29:49 Commencing smooth kNN distance calibration using 1 thread
01:29:57 Initializing from normalized Laplacian + noise
01:29:57 Commencing optimization for 500 epochs, with 84444 positive edges
01:30:05 Optimization finished
[1] "58 0.15"01:30:05 UMAP embedding parameters a = 1.414 b = 0.9488
01:30:05 Read 1203 rows and found 38 numeric columns
01:30:05 Using Annoy for neighbor search, n_neighbors = 58
01:30:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:30:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746c351cb
01:30:05 Searching Annoy index using 1 thread, search_k = 5800
01:30:06 Annoy recall = 100%
01:30:10 Commencing smooth kNN distance calibration using 1 thread
01:30:18 Initializing from normalized Laplacian + noise
01:30:18 Commencing optimization for 500 epochs, with 84444 positive edges
01:30:26 Optimization finished
[1] "58 0.16"01:30:26 UMAP embedding parameters a = 1.383 b = 0.9596
01:30:26 Read 1203 rows and found 38 numeric columns
01:30:26 Using Annoy for neighbor search, n_neighbors = 58
01:30:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:30:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738ba2576
01:30:26 Searching Annoy index using 1 thread, search_k = 5800
01:30:27 Annoy recall = 100%
01:30:31 Commencing smooth kNN distance calibration using 1 thread
01:30:39 Initializing from normalized Laplacian + noise
01:30:39 Commencing optimization for 500 epochs, with 84444 positive edges
01:30:47 Optimization finished
[1] "58 0.17"01:30:47 UMAP embedding parameters a = 1.352 b = 0.9704
01:30:47 Read 1203 rows and found 38 numeric columns
01:30:47 Using Annoy for neighbor search, n_neighbors = 58
01:30:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:30:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759091ed4
01:30:47 Searching Annoy index using 1 thread, search_k = 5800
01:30:48 Annoy recall = 100%
01:30:52 Commencing smooth kNN distance calibration using 1 thread
01:31:00 Initializing from normalized Laplacian + noise
01:31:00 Commencing optimization for 500 epochs, with 84444 positive edges
01:31:08 Optimization finished
[1] "58 0.18"01:31:08 UMAP embedding parameters a = 1.321 b = 0.9813
01:31:08 Read 1203 rows and found 38 numeric columns
01:31:08 Using Annoy for neighbor search, n_neighbors = 58
01:31:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:31:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871432a07b
01:31:08 Searching Annoy index using 1 thread, search_k = 5800
01:31:09 Annoy recall = 100%
01:31:13 Commencing smooth kNN distance calibration using 1 thread
01:31:21 Initializing from normalized Laplacian + noise
01:31:21 Commencing optimization for 500 epochs, with 84444 positive edges
01:31:29 Optimization finished
[1] "58 0.19"01:31:29 UMAP embedding parameters a = 1.292 b = 0.9921
01:31:29 Read 1203 rows and found 38 numeric columns
01:31:29 Using Annoy for neighbor search, n_neighbors = 58
01:31:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:31:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871edf51ec
01:31:29 Searching Annoy index using 1 thread, search_k = 5800
01:31:30 Annoy recall = 100%
01:31:34 Commencing smooth kNN distance calibration using 1 thread
01:31:42 Initializing from normalized Laplacian + noise
01:31:42 Commencing optimization for 500 epochs, with 84444 positive edges
01:31:49 Optimization finished
[1] "58 0.2"01:31:50 UMAP embedding parameters a = 1.262 b = 1.003
01:31:50 Read 1203 rows and found 38 numeric columns
01:31:50 Using Annoy for neighbor search, n_neighbors = 58
01:31:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:31:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723db57a2
01:31:50 Searching Annoy index using 1 thread, search_k = 5800
01:31:51 Annoy recall = 100%
01:31:55 Commencing smooth kNN distance calibration using 1 thread
01:32:03 Initializing from normalized Laplacian + noise
01:32:03 Commencing optimization for 500 epochs, with 84444 positive edges
01:32:10 Optimization finished
[1] "59 0"01:32:11 UMAP embedding parameters a = 1.933 b = 0.7905
01:32:11 Read 1203 rows and found 38 numeric columns
01:32:11 Using Annoy for neighbor search, n_neighbors = 59
01:32:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:32:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873dbeb879
01:32:11 Searching Annoy index using 1 thread, search_k = 5900
01:32:12 Annoy recall = 100%
01:32:16 Commencing smooth kNN distance calibration using 1 thread
01:32:24 Initializing from normalized Laplacian + noise
01:32:24 Commencing optimization for 500 epochs, with 85804 positive edges
01:32:32 Optimization finished
[1] "59 0.01"01:32:32 UMAP embedding parameters a = 1.896 b = 0.8006
01:32:32 Read 1203 rows and found 38 numeric columns
01:32:32 Using Annoy for neighbor search, n_neighbors = 59
01:32:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:32:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724da202f
01:32:32 Searching Annoy index using 1 thread, search_k = 5900
01:32:33 Annoy recall = 100%
01:32:37 Commencing smooth kNN distance calibration using 1 thread
01:32:45 Initializing from normalized Laplacian + noise
01:32:45 Commencing optimization for 500 epochs, with 85804 positive edges
01:32:53 Optimization finished
[1] "59 0.02"01:32:53 UMAP embedding parameters a = 1.859 b = 0.8109
01:32:53 Read 1203 rows and found 38 numeric columns
01:32:53 Using Annoy for neighbor search, n_neighbors = 59
01:32:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:32:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87572cc734
01:32:53 Searching Annoy index using 1 thread, search_k = 5900
01:32:54 Annoy recall = 100%
01:32:58 Commencing smooth kNN distance calibration using 1 thread
01:33:06 Initializing from normalized Laplacian + noise
01:33:06 Commencing optimization for 500 epochs, with 85804 positive edges
01:33:14 Optimization finished
[1] "59 0.03"01:33:14 UMAP embedding parameters a = 1.822 b = 0.8212
01:33:14 Read 1203 rows and found 38 numeric columns
01:33:14 Using Annoy for neighbor search, n_neighbors = 59
01:33:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:33:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733d1c66c
01:33:14 Searching Annoy index using 1 thread, search_k = 5900
01:33:15 Annoy recall = 100%
01:33:19 Commencing smooth kNN distance calibration using 1 thread
01:33:27 Initializing from normalized Laplacian + noise
01:33:27 Commencing optimization for 500 epochs, with 85804 positive edges
01:33:35 Optimization finished
[1] "59 0.04"01:33:35 UMAP embedding parameters a = 1.786 b = 0.8316
01:33:35 Read 1203 rows and found 38 numeric columns
01:33:35 Using Annoy for neighbor search, n_neighbors = 59
01:33:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:33:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e9463ae
01:33:35 Searching Annoy index using 1 thread, search_k = 5900
01:33:36 Annoy recall = 100%
01:33:40 Commencing smooth kNN distance calibration using 1 thread
01:33:48 Initializing from normalized Laplacian + noise
01:33:48 Commencing optimization for 500 epochs, with 85804 positive edges
01:33:56 Optimization finished
[1] "59 0.05"01:33:56 UMAP embedding parameters a = 1.75 b = 0.8421
01:33:56 Read 1203 rows and found 38 numeric columns
01:33:56 Using Annoy for neighbor search, n_neighbors = 59
01:33:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:33:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dad1a8e
01:33:56 Searching Annoy index using 1 thread, search_k = 5900
01:33:57 Annoy recall = 100%
01:34:01 Commencing smooth kNN distance calibration using 1 thread
01:34:09 Initializing from normalized Laplacian + noise
01:34:09 Commencing optimization for 500 epochs, with 85804 positive edges
01:34:17 Optimization finished
[1] "59 0.06"01:34:17 UMAP embedding parameters a = 1.715 b = 0.8526
01:34:17 Read 1203 rows and found 38 numeric columns
01:34:17 Using Annoy for neighbor search, n_neighbors = 59
01:34:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:34:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757cd314a
01:34:17 Searching Annoy index using 1 thread, search_k = 5900
01:34:18 Annoy recall = 100%
01:34:22 Commencing smooth kNN distance calibration using 1 thread
01:34:31 Initializing from normalized Laplacian + noise
01:34:31 Commencing optimization for 500 epochs, with 85804 positive edges
01:34:38 Optimization finished
[1] "59 0.07"01:34:38 UMAP embedding parameters a = 1.68 b = 0.8631
01:34:38 Read 1203 rows and found 38 numeric columns
01:34:38 Using Annoy for neighbor search, n_neighbors = 59
01:34:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:34:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739c9972a
01:34:39 Searching Annoy index using 1 thread, search_k = 5900
01:34:39 Annoy recall = 100%
01:34:43 Commencing smooth kNN distance calibration using 1 thread
01:34:52 Initializing from normalized Laplacian + noise
01:34:52 Commencing optimization for 500 epochs, with 85804 positive edges
01:34:59 Optimization finished
[1] "59 0.08"01:34:59 UMAP embedding parameters a = 1.645 b = 0.8737
01:34:59 Read 1203 rows and found 38 numeric columns
01:34:59 Using Annoy for neighbor search, n_neighbors = 59
01:34:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:35:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87603d5ae
01:35:00 Searching Annoy index using 1 thread, search_k = 5900
01:35:00 Annoy recall = 100%
01:35:04 Commencing smooth kNN distance calibration using 1 thread
01:35:13 Initializing from normalized Laplacian + noise
01:35:13 Commencing optimization for 500 epochs, with 85804 positive edges
01:35:20 Optimization finished
[1] "59 0.09"01:35:20 UMAP embedding parameters a = 1.611 b = 0.8844
01:35:20 Read 1203 rows and found 38 numeric columns
01:35:20 Using Annoy for neighbor search, n_neighbors = 59
01:35:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:35:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bd5128
01:35:21 Searching Annoy index using 1 thread, search_k = 5900
01:35:21 Annoy recall = 100%
01:35:25 Commencing smooth kNN distance calibration using 1 thread
01:35:34 Initializing from normalized Laplacian + noise
01:35:34 Commencing optimization for 500 epochs, with 85804 positive edges
01:35:41 Optimization finished
[1] "59 0.1"01:35:42 UMAP embedding parameters a = 1.577 b = 0.8951
01:35:42 Read 1203 rows and found 38 numeric columns
01:35:42 Using Annoy for neighbor search, n_neighbors = 59
01:35:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:35:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f4dc5de
01:35:42 Searching Annoy index using 1 thread, search_k = 5900
01:35:42 Annoy recall = 100%
01:35:47 Commencing smooth kNN distance calibration using 1 thread
01:35:55 Initializing from normalized Laplacian + noise
01:35:55 Commencing optimization for 500 epochs, with 85804 positive edges
01:36:02 Optimization finished
[1] "59 0.11"01:36:03 UMAP embedding parameters a = 1.544 b = 0.9058
01:36:03 Read 1203 rows and found 38 numeric columns
01:36:03 Using Annoy for neighbor search, n_neighbors = 59
01:36:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:36:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87628571c9
01:36:03 Searching Annoy index using 1 thread, search_k = 5900
01:36:04 Annoy recall = 100%
01:36:08 Commencing smooth kNN distance calibration using 1 thread
01:36:16 Initializing from normalized Laplacian + noise
01:36:16 Commencing optimization for 500 epochs, with 85804 positive edges
01:36:24 Optimization finished
[1] "59 0.12"01:36:24 UMAP embedding parameters a = 1.51 b = 0.9165
01:36:24 Read 1203 rows and found 38 numeric columns
01:36:24 Using Annoy for neighbor search, n_neighbors = 59
01:36:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:36:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b6c7667
01:36:24 Searching Annoy index using 1 thread, search_k = 5900
01:36:25 Annoy recall = 100%
01:36:29 Commencing smooth kNN distance calibration using 1 thread
01:36:37 Initializing from normalized Laplacian + noise
01:36:37 Commencing optimization for 500 epochs, with 85804 positive edges
01:36:45 Optimization finished
[1] "59 0.13"01:36:45 UMAP embedding parameters a = 1.478 b = 0.9272
01:36:45 Read 1203 rows and found 38 numeric columns
01:36:45 Using Annoy for neighbor search, n_neighbors = 59
01:36:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:36:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bba2029
01:36:45 Searching Annoy index using 1 thread, search_k = 5900
01:36:46 Annoy recall = 100%
01:36:50 Commencing smooth kNN distance calibration using 1 thread
01:36:58 Initializing from normalized Laplacian + noise
01:36:59 Commencing optimization for 500 epochs, with 85804 positive edges
01:37:06 Optimization finished
[1] "59 0.14"01:37:06 UMAP embedding parameters a = 1.446 b = 0.938
01:37:06 Read 1203 rows and found 38 numeric columns
01:37:06 Using Annoy for neighbor search, n_neighbors = 59
01:37:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:37:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c930caf
01:37:07 Searching Annoy index using 1 thread, search_k = 5900
01:37:07 Annoy recall = 100%
01:37:11 Commencing smooth kNN distance calibration using 1 thread
01:37:20 Initializing from normalized Laplacian + noise
01:37:20 Commencing optimization for 500 epochs, with 85804 positive edges
01:37:27 Optimization finished
[1] "59 0.15"01:37:27 UMAP embedding parameters a = 1.414 b = 0.9488
01:37:27 Read 1203 rows and found 38 numeric columns
01:37:27 Using Annoy for neighbor search, n_neighbors = 59
01:37:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:37:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87713fb3f0
01:37:28 Searching Annoy index using 1 thread, search_k = 5900
01:37:28 Annoy recall = 100%
01:37:33 Commencing smooth kNN distance calibration using 1 thread
01:37:41 Initializing from normalized Laplacian + noise
01:37:41 Commencing optimization for 500 epochs, with 85804 positive edges
01:37:48 Optimization finished
[1] "59 0.16"01:37:49 UMAP embedding parameters a = 1.383 b = 0.9596
01:37:49 Read 1203 rows and found 38 numeric columns
01:37:49 Using Annoy for neighbor search, n_neighbors = 59
01:37:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:37:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87583b1d25
01:37:49 Searching Annoy index using 1 thread, search_k = 5900
01:37:50 Annoy recall = 100%
01:37:54 Commencing smooth kNN distance calibration using 1 thread
01:38:02 Initializing from normalized Laplacian + noise
01:38:02 Commencing optimization for 500 epochs, with 85804 positive edges
01:38:10 Optimization finished
[1] "59 0.17"01:38:10 UMAP embedding parameters a = 1.352 b = 0.9704
01:38:10 Read 1203 rows and found 38 numeric columns
01:38:10 Using Annoy for neighbor search, n_neighbors = 59
01:38:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:38:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723384cf1
01:38:10 Searching Annoy index using 1 thread, search_k = 5900
01:38:11 Annoy recall = 100%
01:38:15 Commencing smooth kNN distance calibration using 1 thread
01:38:23 Initializing from normalized Laplacian + noise
01:38:23 Commencing optimization for 500 epochs, with 85804 positive edges
01:38:31 Optimization finished
[1] "59 0.18"01:38:31 UMAP embedding parameters a = 1.321 b = 0.9813
01:38:31 Read 1203 rows and found 38 numeric columns
01:38:31 Using Annoy for neighbor search, n_neighbors = 59
01:38:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:38:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ab67f3d
01:38:32 Searching Annoy index using 1 thread, search_k = 5900
01:38:32 Annoy recall = 100%
01:38:36 Commencing smooth kNN distance calibration using 1 thread
01:38:45 Initializing from normalized Laplacian + noise
01:38:45 Commencing optimization for 500 epochs, with 85804 positive edges
01:38:52 Optimization finished
[1] "59 0.19"01:38:52 UMAP embedding parameters a = 1.292 b = 0.9921
01:38:52 Read 1203 rows and found 38 numeric columns
01:38:52 Using Annoy for neighbor search, n_neighbors = 59
01:38:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:38:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876fbe5c78
01:38:53 Searching Annoy index using 1 thread, search_k = 5900
01:38:53 Annoy recall = 100%
01:38:57 Commencing smooth kNN distance calibration using 1 thread
01:39:06 Initializing from normalized Laplacian + noise
01:39:06 Commencing optimization for 500 epochs, with 85804 positive edges
01:39:13 Optimization finished
[1] "59 0.2"01:39:14 UMAP embedding parameters a = 1.262 b = 1.003
01:39:14 Read 1203 rows and found 38 numeric columns
01:39:14 Using Annoy for neighbor search, n_neighbors = 59
01:39:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:39:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738622f90
01:39:14 Searching Annoy index using 1 thread, search_k = 5900
01:39:14 Annoy recall = 100%
01:39:19 Commencing smooth kNN distance calibration using 1 thread
01:39:27 Initializing from normalized Laplacian + noise
01:39:27 Commencing optimization for 500 epochs, with 85804 positive edges
01:39:35 Optimization finished
[1] "60 0"01:39:35 UMAP embedding parameters a = 1.933 b = 0.7905
01:39:35 Read 1203 rows and found 38 numeric columns
01:39:35 Using Annoy for neighbor search, n_neighbors = 60
01:39:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:39:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878b129b3
01:39:35 Searching Annoy index using 1 thread, search_k = 6000
01:39:36 Annoy recall = 100%
01:39:40 Commencing smooth kNN distance calibration using 1 thread
01:39:49 Initializing from normalized Laplacian + noise
01:39:49 Commencing optimization for 500 epochs, with 87124 positive edges
01:39:56 Optimization finished
[1] "60 0.01"01:39:56 UMAP embedding parameters a = 1.896 b = 0.8006
01:39:56 Read 1203 rows and found 38 numeric columns
01:39:56 Using Annoy for neighbor search, n_neighbors = 60
01:39:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:39:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b4486b
01:39:57 Searching Annoy index using 1 thread, search_k = 6000
01:39:57 Annoy recall = 100%
01:40:01 Commencing smooth kNN distance calibration using 1 thread
01:40:10 Initializing from normalized Laplacian + noise
01:40:10 Commencing optimization for 500 epochs, with 87124 positive edges
01:40:17 Optimization finished
[1] "60 0.02"01:40:18 UMAP embedding parameters a = 1.859 b = 0.8109
01:40:18 Read 1203 rows and found 38 numeric columns
01:40:18 Using Annoy for neighbor search, n_neighbors = 60
01:40:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:40:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725c7b65e
01:40:18 Searching Annoy index using 1 thread, search_k = 6000
01:40:18 Annoy recall = 100%
01:40:23 Commencing smooth kNN distance calibration using 1 thread
01:40:31 Initializing from normalized Laplacian + noise
01:40:31 Commencing optimization for 500 epochs, with 87124 positive edges
01:40:39 Optimization finished
[1] "60 0.03"01:40:39 UMAP embedding parameters a = 1.822 b = 0.8212
01:40:39 Read 1203 rows and found 38 numeric columns
01:40:39 Using Annoy for neighbor search, n_neighbors = 60
01:40:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:40:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f786f62
01:40:39 Searching Annoy index using 1 thread, search_k = 6000
01:40:40 Annoy recall = 100%
01:40:44 Commencing smooth kNN distance calibration using 1 thread
01:40:53 Initializing from normalized Laplacian + noise
01:40:53 Commencing optimization for 500 epochs, with 87124 positive edges
01:41:00 Optimization finished
[1] "60 0.04"01:41:00 UMAP embedding parameters a = 1.786 b = 0.8316
01:41:00 Read 1203 rows and found 38 numeric columns
01:41:00 Using Annoy for neighbor search, n_neighbors = 60
01:41:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:41:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748779a36
01:41:01 Searching Annoy index using 1 thread, search_k = 6000
01:41:01 Annoy recall = 100%
01:41:05 Commencing smooth kNN distance calibration using 1 thread
01:41:14 Initializing from normalized Laplacian + noise
01:41:14 Commencing optimization for 500 epochs, with 87124 positive edges
01:41:21 Optimization finished
[1] "60 0.05"01:41:22 UMAP embedding parameters a = 1.75 b = 0.8421
01:41:22 Read 1203 rows and found 38 numeric columns
01:41:22 Using Annoy for neighbor search, n_neighbors = 60
01:41:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:41:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e81dbd4
01:41:22 Searching Annoy index using 1 thread, search_k = 6000
01:41:22 Annoy recall = 100%
01:41:27 Commencing smooth kNN distance calibration using 1 thread
01:41:35 Initializing from normalized Laplacian + noise
01:41:35 Commencing optimization for 500 epochs, with 87124 positive edges
01:41:43 Optimization finished
[1] "60 0.06"01:41:43 UMAP embedding parameters a = 1.715 b = 0.8526
01:41:43 Read 1203 rows and found 38 numeric columns
01:41:43 Using Annoy for neighbor search, n_neighbors = 60
01:41:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:41:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738818e36
01:41:43 Searching Annoy index using 1 thread, search_k = 6000
01:41:44 Annoy recall = 100%
01:41:48 Commencing smooth kNN distance calibration using 1 thread
01:41:56 Initializing from normalized Laplacian + noise
01:41:57 Commencing optimization for 500 epochs, with 87124 positive edges
01:42:04 Optimization finished
[1] "60 0.07"01:42:04 UMAP embedding parameters a = 1.68 b = 0.8631
01:42:04 Read 1203 rows and found 38 numeric columns
01:42:04 Using Annoy for neighbor search, n_neighbors = 60
01:42:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:42:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875caa3ab1
01:42:05 Searching Annoy index using 1 thread, search_k = 6000
01:42:05 Annoy recall = 100%
01:42:09 Commencing smooth kNN distance calibration using 1 thread
01:42:18 Initializing from normalized Laplacian + noise
01:42:18 Commencing optimization for 500 epochs, with 87124 positive edges
01:42:25 Optimization finished
[1] "60 0.08"01:42:26 UMAP embedding parameters a = 1.645 b = 0.8737
01:42:26 Read 1203 rows and found 38 numeric columns
01:42:26 Using Annoy for neighbor search, n_neighbors = 60
01:42:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:42:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d612dc0
01:42:26 Searching Annoy index using 1 thread, search_k = 6000
01:42:26 Annoy recall = 100%
01:42:31 Commencing smooth kNN distance calibration using 1 thread
01:42:39 Initializing from normalized Laplacian + noise
01:42:39 Commencing optimization for 500 epochs, with 87124 positive edges
01:42:47 Optimization finished
[1] "60 0.09"01:42:47 UMAP embedding parameters a = 1.611 b = 0.8844
01:42:47 Read 1203 rows and found 38 numeric columns
01:42:47 Using Annoy for neighbor search, n_neighbors = 60
01:42:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:42:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c5ce5d8
01:42:47 Searching Annoy index using 1 thread, search_k = 6000
01:42:48 Annoy recall = 100%
01:42:52 Commencing smooth kNN distance calibration using 1 thread
01:43:00 Initializing from normalized Laplacian + noise
01:43:01 Commencing optimization for 500 epochs, with 87124 positive edges
01:43:08 Optimization finished
[1] "60 0.1"01:43:08 UMAP embedding parameters a = 1.577 b = 0.8951
01:43:08 Read 1203 rows and found 38 numeric columns
01:43:08 Using Annoy for neighbor search, n_neighbors = 60
01:43:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:43:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a68f32b
01:43:09 Searching Annoy index using 1 thread, search_k = 6000
01:43:09 Annoy recall = 100%
01:43:13 Commencing smooth kNN distance calibration using 1 thread
01:43:22 Initializing from normalized Laplacian + noise
01:43:22 Commencing optimization for 500 epochs, with 87124 positive edges
01:43:29 Optimization finished
[1] "60 0.11"01:43:30 UMAP embedding parameters a = 1.544 b = 0.9058
01:43:30 Read 1203 rows and found 38 numeric columns
01:43:30 Using Annoy for neighbor search, n_neighbors = 60
01:43:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:43:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87223b4def
01:43:30 Searching Annoy index using 1 thread, search_k = 6000
01:43:30 Annoy recall = 100%
01:43:35 Commencing smooth kNN distance calibration using 1 thread
01:43:43 Initializing from normalized Laplacian + noise
01:43:43 Commencing optimization for 500 epochs, with 87124 positive edges
01:43:51 Optimization finished
[1] "60 0.12"01:43:51 UMAP embedding parameters a = 1.51 b = 0.9165
01:43:51 Read 1203 rows and found 38 numeric columns
01:43:51 Using Annoy for neighbor search, n_neighbors = 60
01:43:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:43:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873389ad0c
01:43:51 Searching Annoy index using 1 thread, search_k = 6000
01:43:52 Annoy recall = 100%
01:43:56 Commencing smooth kNN distance calibration using 1 thread
01:44:05 Initializing from normalized Laplacian + noise
01:44:05 Commencing optimization for 500 epochs, with 87124 positive edges
01:44:12 Optimization finished
[1] "60 0.13"01:44:12 UMAP embedding parameters a = 1.478 b = 0.9272
01:44:12 Read 1203 rows and found 38 numeric columns
01:44:12 Using Annoy for neighbor search, n_neighbors = 60
01:44:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:44:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e3ab997
01:44:13 Searching Annoy index using 1 thread, search_k = 6000
01:44:13 Annoy recall = 100%
01:44:17 Commencing smooth kNN distance calibration using 1 thread
01:44:26 Initializing from normalized Laplacian + noise
01:44:26 Commencing optimization for 500 epochs, with 87124 positive edges
01:44:34 Optimization finished
[1] "60 0.14"01:44:34 UMAP embedding parameters a = 1.446 b = 0.938
01:44:34 Read 1203 rows and found 38 numeric columns
01:44:34 Using Annoy for neighbor search, n_neighbors = 60
01:44:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:44:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710cfb19e
01:44:34 Searching Annoy index using 1 thread, search_k = 6000
01:44:35 Annoy recall = 100%
01:44:39 Commencing smooth kNN distance calibration using 1 thread
01:44:47 Initializing from normalized Laplacian + noise
01:44:48 Commencing optimization for 500 epochs, with 87124 positive edges
01:44:55 Optimization finished
[1] "60 0.15"01:44:55 UMAP embedding parameters a = 1.414 b = 0.9488
01:44:55 Read 1203 rows and found 38 numeric columns
01:44:55 Using Annoy for neighbor search, n_neighbors = 60
01:44:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:44:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876136c79a
01:44:56 Searching Annoy index using 1 thread, search_k = 6000
01:44:56 Annoy recall = 100%
01:45:00 Commencing smooth kNN distance calibration using 1 thread
01:45:09 Initializing from normalized Laplacian + noise
01:45:09 Commencing optimization for 500 epochs, with 87124 positive edges
01:45:16 Optimization finished
[1] "60 0.16"01:45:17 UMAP embedding parameters a = 1.383 b = 0.9596
01:45:17 Read 1203 rows and found 38 numeric columns
01:45:17 Using Annoy for neighbor search, n_neighbors = 60
01:45:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:45:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872607eae2
01:45:17 Searching Annoy index using 1 thread, search_k = 6000
01:45:17 Annoy recall = 100%
01:45:22 Commencing smooth kNN distance calibration using 1 thread
01:45:30 Initializing from normalized Laplacian + noise
01:45:30 Commencing optimization for 500 epochs, with 87124 positive edges
01:45:38 Optimization finished
[1] "60 0.17"01:45:38 UMAP embedding parameters a = 1.352 b = 0.9704
01:45:38 Read 1203 rows and found 38 numeric columns
01:45:38 Using Annoy for neighbor search, n_neighbors = 60
01:45:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:45:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a9948c8
01:45:38 Searching Annoy index using 1 thread, search_k = 6000
01:45:39 Annoy recall = 100%
01:45:43 Commencing smooth kNN distance calibration using 1 thread
01:45:52 Initializing from normalized Laplacian + noise
01:45:52 Commencing optimization for 500 epochs, with 87124 positive edges
01:45:59 Optimization finished
[1] "60 0.18"01:46:00 UMAP embedding parameters a = 1.321 b = 0.9813
01:46:00 Read 1203 rows and found 38 numeric columns
01:46:00 Using Annoy for neighbor search, n_neighbors = 60
01:46:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:46:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87673a9d49
01:46:00 Searching Annoy index using 1 thread, search_k = 6000
01:46:00 Annoy recall = 100%
01:46:05 Commencing smooth kNN distance calibration using 1 thread
01:46:13 Initializing from normalized Laplacian + noise
01:46:13 Commencing optimization for 500 epochs, with 87124 positive edges
01:46:20 Optimization finished
[1] "60 0.19"01:46:21 UMAP embedding parameters a = 1.292 b = 0.9921
01:46:21 Read 1203 rows and found 38 numeric columns
01:46:21 Using Annoy for neighbor search, n_neighbors = 60
01:46:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:46:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726c53c0a
01:46:21 Searching Annoy index using 1 thread, search_k = 6000
01:46:21 Annoy recall = 100%
01:46:26 Commencing smooth kNN distance calibration using 1 thread
01:46:34 Initializing from normalized Laplacian + noise
01:46:34 Commencing optimization for 500 epochs, with 87124 positive edges
01:46:41 Optimization finished
[1] "60 0.2"01:46:42 UMAP embedding parameters a = 1.262 b = 1.003
01:46:42 Read 1203 rows and found 38 numeric columns
01:46:42 Using Annoy for neighbor search, n_neighbors = 60
01:46:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:46:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719e70ea6
01:46:42 Searching Annoy index using 1 thread, search_k = 6000
01:46:43 Annoy recall = 100%
01:46:47 Commencing smooth kNN distance calibration using 1 thread
01:46:55 Initializing from normalized Laplacian + noise
01:46:55 Commencing optimization for 500 epochs, with 87124 positive edges
01:47:03 Optimization finished
[1] "61 0"01:47:03 UMAP embedding parameters a = 1.933 b = 0.7905
01:47:03 Read 1203 rows and found 38 numeric columns
01:47:03 Using Annoy for neighbor search, n_neighbors = 61
01:47:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:47:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749c00f12
01:47:03 Searching Annoy index using 1 thread, search_k = 6100
01:47:04 Annoy recall = 100%
01:47:08 Commencing smooth kNN distance calibration using 1 thread
01:47:16 Initializing from normalized Laplacian + noise
01:47:16 Commencing optimization for 500 epochs, with 88500 positive edges
01:47:24 Optimization finished
[1] "61 0.01"01:47:24 UMAP embedding parameters a = 1.896 b = 0.8006
01:47:24 Read 1203 rows and found 38 numeric columns
01:47:24 Using Annoy for neighbor search, n_neighbors = 61
01:47:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:47:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871231b271
01:47:24 Searching Annoy index using 1 thread, search_k = 6100
01:47:25 Annoy recall = 100%
01:47:29 Commencing smooth kNN distance calibration using 1 thread
01:47:37 Initializing from normalized Laplacian + noise
01:47:37 Commencing optimization for 500 epochs, with 88500 positive edges
01:47:45 Optimization finished
[1] "61 0.02"01:47:45 UMAP embedding parameters a = 1.859 b = 0.8109
01:47:45 Read 1203 rows and found 38 numeric columns
01:47:45 Using Annoy for neighbor search, n_neighbors = 61
01:47:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:47:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a12ecf
01:47:45 Searching Annoy index using 1 thread, search_k = 6100
01:47:46 Annoy recall = 100%
01:47:50 Commencing smooth kNN distance calibration using 1 thread
01:47:59 Initializing from normalized Laplacian + noise
01:47:59 Commencing optimization for 500 epochs, with 88500 positive edges
01:48:06 Optimization finished
[1] "61 0.03"01:48:06 UMAP embedding parameters a = 1.822 b = 0.8212
01:48:06 Read 1203 rows and found 38 numeric columns
01:48:06 Using Annoy for neighbor search, n_neighbors = 61
01:48:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:48:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876531bc2
01:48:07 Searching Annoy index using 1 thread, search_k = 6100
01:48:07 Annoy recall = 100%
01:48:11 Commencing smooth kNN distance calibration using 1 thread
01:48:20 Initializing from normalized Laplacian + noise
01:48:20 Commencing optimization for 500 epochs, with 88500 positive edges
01:48:27 Optimization finished
[1] "61 0.04"01:48:27 UMAP embedding parameters a = 1.786 b = 0.8316
01:48:27 Read 1203 rows and found 38 numeric columns
01:48:27 Using Annoy for neighbor search, n_neighbors = 61
01:48:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:48:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873716662
01:48:28 Searching Annoy index using 1 thread, search_k = 6100
01:48:28 Annoy recall = 100%
01:48:33 Commencing smooth kNN distance calibration using 1 thread
01:48:41 Initializing from normalized Laplacian + noise
01:48:41 Commencing optimization for 500 epochs, with 88500 positive edges
01:48:49 Optimization finished
[1] "61 0.05"01:48:49 UMAP embedding parameters a = 1.75 b = 0.8421
01:48:49 Read 1203 rows and found 38 numeric columns
01:48:49 Using Annoy for neighbor search, n_neighbors = 61
01:48:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:48:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ddc4bf5
01:48:49 Searching Annoy index using 1 thread, search_k = 6100
01:48:50 Annoy recall = 100%
01:48:54 Commencing smooth kNN distance calibration using 1 thread
01:49:03 Initializing from normalized Laplacian + noise
01:49:03 Commencing optimization for 500 epochs, with 88500 positive edges
01:49:10 Optimization finished
[1] "61 0.06"01:49:11 UMAP embedding parameters a = 1.715 b = 0.8526
01:49:11 Read 1203 rows and found 38 numeric columns
01:49:11 Using Annoy for neighbor search, n_neighbors = 61
01:49:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:49:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87298b68b3
01:49:11 Searching Annoy index using 1 thread, search_k = 6100
01:49:11 Annoy recall = 100%
01:49:16 Commencing smooth kNN distance calibration using 1 thread
01:49:24 Initializing from normalized Laplacian + noise
01:49:24 Commencing optimization for 500 epochs, with 88500 positive edges
01:49:32 Optimization finished
[1] "61 0.07"01:49:32 UMAP embedding parameters a = 1.68 b = 0.8631
01:49:32 Read 1203 rows and found 38 numeric columns
01:49:32 Using Annoy for neighbor search, n_neighbors = 61
01:49:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:49:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e27e59f
01:49:32 Searching Annoy index using 1 thread, search_k = 6100
01:49:33 Annoy recall = 100%
01:49:37 Commencing smooth kNN distance calibration using 1 thread
01:49:46 Initializing from normalized Laplacian + noise
01:49:46 Commencing optimization for 500 epochs, with 88500 positive edges
01:49:54 Optimization finished
[1] "61 0.08"01:49:54 UMAP embedding parameters a = 1.645 b = 0.8737
01:49:54 Read 1203 rows and found 38 numeric columns
01:49:54 Using Annoy for neighbor search, n_neighbors = 61
01:49:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:49:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d9aa86d
01:49:54 Searching Annoy index using 1 thread, search_k = 6100
01:49:55 Annoy recall = 100%
01:49:59 Commencing smooth kNN distance calibration using 1 thread
01:50:08 Initializing from normalized Laplacian + noise
01:50:08 Commencing optimization for 500 epochs, with 88500 positive edges
01:50:15 Optimization finished
[1] "61 0.09"01:50:15 UMAP embedding parameters a = 1.611 b = 0.8844
01:50:15 Read 1203 rows and found 38 numeric columns
01:50:15 Using Annoy for neighbor search, n_neighbors = 61
01:50:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:50:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761ed9843
01:50:16 Searching Annoy index using 1 thread, search_k = 6100
01:50:16 Annoy recall = 100%
01:50:20 Commencing smooth kNN distance calibration using 1 thread
01:50:29 Initializing from normalized Laplacian + noise
01:50:29 Commencing optimization for 500 epochs, with 88500 positive edges
01:50:37 Optimization finished
[1] "61 0.1"01:50:37 UMAP embedding parameters a = 1.577 b = 0.8951
01:50:37 Read 1203 rows and found 38 numeric columns
01:50:37 Using Annoy for neighbor search, n_neighbors = 61
01:50:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:50:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766d90f53
01:50:37 Searching Annoy index using 1 thread, search_k = 6100
01:50:38 Annoy recall = 100%
01:50:42 Commencing smooth kNN distance calibration using 1 thread
01:50:51 Initializing from normalized Laplacian + noise
01:50:51 Commencing optimization for 500 epochs, with 88500 positive edges
01:50:58 Optimization finished
[1] "61 0.11"01:50:59 UMAP embedding parameters a = 1.544 b = 0.9058
01:50:59 Read 1203 rows and found 38 numeric columns
01:50:59 Using Annoy for neighbor search, n_neighbors = 61
01:50:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:50:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f4ef0d8
01:50:59 Searching Annoy index using 1 thread, search_k = 6100
01:51:00 Annoy recall = 100%
01:51:04 Commencing smooth kNN distance calibration using 1 thread
01:51:12 Initializing from normalized Laplacian + noise
01:51:12 Commencing optimization for 500 epochs, with 88500 positive edges
01:51:20 Optimization finished
[1] "61 0.12"01:51:20 UMAP embedding parameters a = 1.51 b = 0.9165
01:51:20 Read 1203 rows and found 38 numeric columns
01:51:20 Using Annoy for neighbor search, n_neighbors = 61
01:51:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:51:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b54ea1
01:51:21 Searching Annoy index using 1 thread, search_k = 6100
01:51:21 Annoy recall = 100%
01:51:25 Commencing smooth kNN distance calibration using 1 thread
01:51:34 Initializing from normalized Laplacian + noise
01:51:34 Commencing optimization for 500 epochs, with 88500 positive edges
01:51:42 Optimization finished
[1] "61 0.13"01:51:42 UMAP embedding parameters a = 1.478 b = 0.9272
01:51:42 Read 1203 rows and found 38 numeric columns
01:51:42 Using Annoy for neighbor search, n_neighbors = 61
01:51:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:51:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746517eb5
01:51:42 Searching Annoy index using 1 thread, search_k = 6100
01:51:43 Annoy recall = 100%
01:51:47 Commencing smooth kNN distance calibration using 1 thread
01:51:56 Initializing from normalized Laplacian + noise
01:51:56 Commencing optimization for 500 epochs, with 88500 positive edges
01:52:03 Optimization finished
[1] "61 0.14"01:52:04 UMAP embedding parameters a = 1.446 b = 0.938
01:52:04 Read 1203 rows and found 38 numeric columns
01:52:04 Using Annoy for neighbor search, n_neighbors = 61
01:52:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:52:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717c68b0f
01:52:04 Searching Annoy index using 1 thread, search_k = 6100
01:52:05 Annoy recall = 100%
01:52:09 Commencing smooth kNN distance calibration using 1 thread
01:52:17 Initializing from normalized Laplacian + noise
01:52:17 Commencing optimization for 500 epochs, with 88500 positive edges
01:52:25 Optimization finished
[1] "61 0.15"01:52:25 UMAP embedding parameters a = 1.414 b = 0.9488
01:52:25 Read 1203 rows and found 38 numeric columns
01:52:25 Using Annoy for neighbor search, n_neighbors = 61
01:52:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:52:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766372a76
01:52:26 Searching Annoy index using 1 thread, search_k = 6100
01:52:26 Annoy recall = 100%
01:52:30 Commencing smooth kNN distance calibration using 1 thread
01:52:39 Initializing from normalized Laplacian + noise
01:52:39 Commencing optimization for 500 epochs, with 88500 positive edges
01:52:47 Optimization finished
[1] "61 0.16"01:52:47 UMAP embedding parameters a = 1.383 b = 0.9596
01:52:47 Read 1203 rows and found 38 numeric columns
01:52:47 Using Annoy for neighbor search, n_neighbors = 61
01:52:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:52:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ed30ceb
01:52:47 Searching Annoy index using 1 thread, search_k = 6100
01:52:48 Annoy recall = 100%
01:52:52 Commencing smooth kNN distance calibration using 1 thread
01:53:01 Initializing from normalized Laplacian + noise
01:53:01 Commencing optimization for 500 epochs, with 88500 positive edges
01:53:09 Optimization finished
[1] "61 0.17"01:53:09 UMAP embedding parameters a = 1.352 b = 0.9704
01:53:09 Read 1203 rows and found 38 numeric columns
01:53:09 Using Annoy for neighbor search, n_neighbors = 61
01:53:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:53:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877470c5c0
01:53:09 Searching Annoy index using 1 thread, search_k = 6100
01:53:10 Annoy recall = 100%
01:53:14 Commencing smooth kNN distance calibration using 1 thread
01:53:23 Initializing from normalized Laplacian + noise
01:53:23 Commencing optimization for 500 epochs, with 88500 positive edges
01:53:30 Optimization finished
[1] "61 0.18"01:53:30 UMAP embedding parameters a = 1.321 b = 0.9813
01:53:30 Read 1203 rows and found 38 numeric columns
01:53:30 Using Annoy for neighbor search, n_neighbors = 61
01:53:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:53:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763985836
01:53:31 Searching Annoy index using 1 thread, search_k = 6100
01:53:31 Annoy recall = 100%
01:53:36 Commencing smooth kNN distance calibration using 1 thread
01:53:44 Initializing from normalized Laplacian + noise
01:53:44 Commencing optimization for 500 epochs, with 88500 positive edges
01:53:52 Optimization finished
[1] "61 0.19"01:53:52 UMAP embedding parameters a = 1.292 b = 0.9921
01:53:52 Read 1203 rows and found 38 numeric columns
01:53:52 Using Annoy for neighbor search, n_neighbors = 61
01:53:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:53:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b2ff2c4
01:53:53 Searching Annoy index using 1 thread, search_k = 6100
01:53:53 Annoy recall = 100%
01:53:57 Commencing smooth kNN distance calibration using 1 thread
01:54:06 Initializing from normalized Laplacian + noise
01:54:06 Commencing optimization for 500 epochs, with 88500 positive edges
01:54:14 Optimization finished
[1] "61 0.2"01:54:14 UMAP embedding parameters a = 1.262 b = 1.003
01:54:14 Read 1203 rows and found 38 numeric columns
01:54:14 Using Annoy for neighbor search, n_neighbors = 61
01:54:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:54:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ed9b8eb
01:54:14 Searching Annoy index using 1 thread, search_k = 6100
01:54:15 Annoy recall = 100%
01:54:19 Commencing smooth kNN distance calibration using 1 thread
01:54:28 Initializing from normalized Laplacian + noise
01:54:28 Commencing optimization for 500 epochs, with 88500 positive edges
01:54:35 Optimization finished
[1] "62 0"01:54:36 UMAP embedding parameters a = 1.933 b = 0.7905
01:54:36 Read 1203 rows and found 38 numeric columns
01:54:36 Using Annoy for neighbor search, n_neighbors = 62
01:54:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:54:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d3a626
01:54:36 Searching Annoy index using 1 thread, search_k = 6200
01:54:37 Annoy recall = 100%
01:54:41 Commencing smooth kNN distance calibration using 1 thread
01:54:50 Initializing from normalized Laplacian + noise
01:54:50 Commencing optimization for 500 epochs, with 89812 positive edges
01:54:57 Optimization finished
[1] "62 0.01"01:54:58 UMAP embedding parameters a = 1.896 b = 0.8006
01:54:58 Read 1203 rows and found 38 numeric columns
01:54:58 Using Annoy for neighbor search, n_neighbors = 62
01:54:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:54:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87eb99fd0
01:54:58 Searching Annoy index using 1 thread, search_k = 6200
01:54:58 Annoy recall = 100%
01:55:03 Commencing smooth kNN distance calibration using 1 thread
01:55:12 Initializing from normalized Laplacian + noise
01:55:12 Commencing optimization for 500 epochs, with 89812 positive edges
01:55:19 Optimization finished
[1] "62 0.02"01:55:19 UMAP embedding parameters a = 1.859 b = 0.8109
01:55:19 Read 1203 rows and found 38 numeric columns
01:55:19 Using Annoy for neighbor search, n_neighbors = 62
01:55:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:55:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d147283
01:55:20 Searching Annoy index using 1 thread, search_k = 6200
01:55:20 Annoy recall = 100%
01:55:25 Commencing smooth kNN distance calibration using 1 thread
01:55:33 Initializing from normalized Laplacian + noise
01:55:33 Commencing optimization for 500 epochs, with 89812 positive edges
01:55:41 Optimization finished
[1] "62 0.03"01:55:41 UMAP embedding parameters a = 1.822 b = 0.8212
01:55:41 Read 1203 rows and found 38 numeric columns
01:55:41 Using Annoy for neighbor search, n_neighbors = 62
01:55:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:55:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716a357c4
01:55:42 Searching Annoy index using 1 thread, search_k = 6200
01:55:42 Annoy recall = 100%
01:55:46 Commencing smooth kNN distance calibration using 1 thread
01:55:55 Initializing from normalized Laplacian + noise
01:55:55 Commencing optimization for 500 epochs, with 89812 positive edges
01:56:03 Optimization finished
[1] "62 0.04"01:56:03 UMAP embedding parameters a = 1.786 b = 0.8316
01:56:03 Read 1203 rows and found 38 numeric columns
01:56:03 Using Annoy for neighbor search, n_neighbors = 62
01:56:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:56:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ff0676b
01:56:03 Searching Annoy index using 1 thread, search_k = 6200
01:56:04 Annoy recall = 100%
01:56:08 Commencing smooth kNN distance calibration using 1 thread
01:56:17 Initializing from normalized Laplacian + noise
01:56:17 Commencing optimization for 500 epochs, with 89812 positive edges
01:56:25 Optimization finished
[1] "62 0.05"01:56:25 UMAP embedding parameters a = 1.75 b = 0.8421
01:56:25 Read 1203 rows and found 38 numeric columns
01:56:25 Using Annoy for neighbor search, n_neighbors = 62
01:56:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:56:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731c5d65
01:56:25 Searching Annoy index using 1 thread, search_k = 6200
01:56:26 Annoy recall = 100%
01:56:30 Commencing smooth kNN distance calibration using 1 thread
01:56:39 Initializing from normalized Laplacian + noise
01:56:39 Commencing optimization for 500 epochs, with 89812 positive edges
01:56:47 Optimization finished
[1] "62 0.06"01:56:47 UMAP embedding parameters a = 1.715 b = 0.8526
01:56:47 Read 1203 rows and found 38 numeric columns
01:56:47 Using Annoy for neighbor search, n_neighbors = 62
01:56:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:56:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87613ca08c
01:56:47 Searching Annoy index using 1 thread, search_k = 6200
01:56:48 Annoy recall = 100%
01:56:52 Commencing smooth kNN distance calibration using 1 thread
01:57:01 Initializing from normalized Laplacian + noise
01:57:01 Commencing optimization for 500 epochs, with 89812 positive edges
01:57:09 Optimization finished
[1] "62 0.07"01:57:09 UMAP embedding parameters a = 1.68 b = 0.8631
01:57:09 Read 1203 rows and found 38 numeric columns
01:57:09 Using Annoy for neighbor search, n_neighbors = 62
01:57:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:57:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87572b04b4
01:57:09 Searching Annoy index using 1 thread, search_k = 6200
01:57:10 Annoy recall = 100%
01:57:14 Commencing smooth kNN distance calibration using 1 thread
01:57:23 Initializing from normalized Laplacian + noise
01:57:23 Commencing optimization for 500 epochs, with 89812 positive edges
01:57:30 Optimization finished
[1] "62 0.08"01:57:31 UMAP embedding parameters a = 1.645 b = 0.8737
01:57:31 Read 1203 rows and found 38 numeric columns
01:57:31 Using Annoy for neighbor search, n_neighbors = 62
01:57:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:57:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729e1996f
01:57:31 Searching Annoy index using 1 thread, search_k = 6200
01:57:31 Annoy recall = 100%
01:57:36 Commencing smooth kNN distance calibration using 1 thread
01:57:45 Initializing from normalized Laplacian + noise
01:57:45 Commencing optimization for 500 epochs, with 89812 positive edges
01:57:52 Optimization finished
[1] "62 0.09"01:57:52 UMAP embedding parameters a = 1.611 b = 0.8844
01:57:52 Read 1203 rows and found 38 numeric columns
01:57:52 Using Annoy for neighbor search, n_neighbors = 62
01:57:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:57:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b23af32
01:57:53 Searching Annoy index using 1 thread, search_k = 6200
01:57:53 Annoy recall = 100%
01:57:58 Commencing smooth kNN distance calibration using 1 thread
01:58:07 Initializing from normalized Laplacian + noise
01:58:07 Commencing optimization for 500 epochs, with 89812 positive edges
01:58:14 Optimization finished
[1] "62 0.1"01:58:14 UMAP embedding parameters a = 1.577 b = 0.8951
01:58:14 Read 1203 rows and found 38 numeric columns
01:58:14 Using Annoy for neighbor search, n_neighbors = 62
01:58:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:58:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720eb13c6
01:58:15 Searching Annoy index using 1 thread, search_k = 6200
01:58:15 Annoy recall = 100%
01:58:20 Commencing smooth kNN distance calibration using 1 thread
01:58:28 Initializing from normalized Laplacian + noise
01:58:28 Commencing optimization for 500 epochs, with 89812 positive edges
01:58:36 Optimization finished
[1] "62 0.11"01:58:36 UMAP embedding parameters a = 1.544 b = 0.9058
01:58:36 Read 1203 rows and found 38 numeric columns
01:58:36 Using Annoy for neighbor search, n_neighbors = 62
01:58:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:58:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c134be0
01:58:37 Searching Annoy index using 1 thread, search_k = 6200
01:58:37 Annoy recall = 100%
01:58:42 Commencing smooth kNN distance calibration using 1 thread
01:58:50 Initializing from normalized Laplacian + noise
01:58:50 Commencing optimization for 500 epochs, with 89812 positive edges
01:58:58 Optimization finished
[1] "62 0.12"01:58:58 UMAP embedding parameters a = 1.51 b = 0.9165
01:58:58 Read 1203 rows and found 38 numeric columns
01:58:58 Using Annoy for neighbor search, n_neighbors = 62
01:58:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:58:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c4de01
01:58:59 Searching Annoy index using 1 thread, search_k = 6200
01:58:59 Annoy recall = 100%
01:59:04 Commencing smooth kNN distance calibration using 1 thread
01:59:12 Initializing from normalized Laplacian + noise
01:59:12 Commencing optimization for 500 epochs, with 89812 positive edges
01:59:20 Optimization finished
[1] "62 0.13"01:59:20 UMAP embedding parameters a = 1.478 b = 0.9272
01:59:20 Read 1203 rows and found 38 numeric columns
01:59:20 Using Annoy for neighbor search, n_neighbors = 62
01:59:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:59:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87273e2f88
01:59:21 Searching Annoy index using 1 thread, search_k = 6200
01:59:21 Annoy recall = 100%
01:59:25 Commencing smooth kNN distance calibration using 1 thread
01:59:34 Initializing from normalized Laplacian + noise
01:59:34 Commencing optimization for 500 epochs, with 89812 positive edges
01:59:42 Optimization finished
[1] "62 0.14"01:59:42 UMAP embedding parameters a = 1.446 b = 0.938
01:59:42 Read 1203 rows and found 38 numeric columns
01:59:42 Using Annoy for neighbor search, n_neighbors = 62
01:59:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:59:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f84b242
01:59:43 Searching Annoy index using 1 thread, search_k = 6200
01:59:43 Annoy recall = 100%
01:59:47 Commencing smooth kNN distance calibration using 1 thread
01:59:56 Initializing from normalized Laplacian + noise
01:59:56 Commencing optimization for 500 epochs, with 89812 positive edges
02:00:04 Optimization finished
[1] "62 0.15"02:00:04 UMAP embedding parameters a = 1.414 b = 0.9488
02:00:04 Read 1203 rows and found 38 numeric columns
02:00:04 Using Annoy for neighbor search, n_neighbors = 62
02:00:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:00:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ea129f6
02:00:05 Searching Annoy index using 1 thread, search_k = 6200
02:00:05 Annoy recall = 100%
02:00:09 Commencing smooth kNN distance calibration using 1 thread
02:00:18 Initializing from normalized Laplacian + noise
02:00:18 Commencing optimization for 500 epochs, with 89812 positive edges
02:00:26 Optimization finished
[1] "62 0.16"02:00:26 UMAP embedding parameters a = 1.383 b = 0.9596
02:00:26 Read 1203 rows and found 38 numeric columns
02:00:26 Using Annoy for neighbor search, n_neighbors = 62
02:00:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:00:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750c9983c
02:00:27 Searching Annoy index using 1 thread, search_k = 6200
02:00:27 Annoy recall = 100%
02:00:31 Commencing smooth kNN distance calibration using 1 thread
02:00:40 Initializing from normalized Laplacian + noise
02:00:40 Commencing optimization for 500 epochs, with 89812 positive edges
02:00:48 Optimization finished
[1] "62 0.17"02:00:48 UMAP embedding parameters a = 1.352 b = 0.9704
02:00:48 Read 1203 rows and found 38 numeric columns
02:00:48 Using Annoy for neighbor search, n_neighbors = 62
02:00:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:00:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871dac97e2
02:00:49 Searching Annoy index using 1 thread, search_k = 6200
02:00:49 Annoy recall = 100%
02:00:53 Commencing smooth kNN distance calibration using 1 thread
02:01:02 Initializing from normalized Laplacian + noise
02:01:02 Commencing optimization for 500 epochs, with 89812 positive edges
02:01:10 Optimization finished
[1] "62 0.18"02:01:10 UMAP embedding parameters a = 1.321 b = 0.9813
02:01:10 Read 1203 rows and found 38 numeric columns
02:01:10 Using Annoy for neighbor search, n_neighbors = 62
02:01:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:01:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c3bd263
02:01:11 Searching Annoy index using 1 thread, search_k = 6200
02:01:11 Annoy recall = 100%
02:01:16 Commencing smooth kNN distance calibration using 1 thread
02:01:24 Initializing from normalized Laplacian + noise
02:01:24 Commencing optimization for 500 epochs, with 89812 positive edges
02:01:32 Optimization finished
[1] "62 0.19"02:01:32 UMAP embedding parameters a = 1.292 b = 0.9921
02:01:32 Read 1203 rows and found 38 numeric columns
02:01:32 Using Annoy for neighbor search, n_neighbors = 62
02:01:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:01:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732b7307f
02:01:33 Searching Annoy index using 1 thread, search_k = 6200
02:01:33 Annoy recall = 100%
02:01:37 Commencing smooth kNN distance calibration using 1 thread
02:01:46 Initializing from normalized Laplacian + noise
02:01:46 Commencing optimization for 500 epochs, with 89812 positive edges
02:01:54 Optimization finished
[1] "62 0.2"02:01:54 UMAP embedding parameters a = 1.262 b = 1.003
02:01:54 Read 1203 rows and found 38 numeric columns
02:01:54 Using Annoy for neighbor search, n_neighbors = 62
02:01:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:01:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87485a735
02:01:55 Searching Annoy index using 1 thread, search_k = 6200
02:01:55 Annoy recall = 100%
02:02:00 Commencing smooth kNN distance calibration using 1 thread
02:02:08 Initializing from normalized Laplacian + noise
02:02:08 Commencing optimization for 500 epochs, with 89812 positive edges
02:02:16 Optimization finished
[1] "63 0"02:02:16 UMAP embedding parameters a = 1.933 b = 0.7905
02:02:16 Read 1203 rows and found 38 numeric columns
02:02:16 Using Annoy for neighbor search, n_neighbors = 63
02:02:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:02:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b8ac33c
02:02:17 Searching Annoy index using 1 thread, search_k = 6300
02:02:17 Annoy recall = 100%
02:02:22 Commencing smooth kNN distance calibration using 1 thread
02:02:30 Initializing from normalized Laplacian + noise
02:02:30 Commencing optimization for 500 epochs, with 91158 positive edges
02:02:38 Optimization finished
[1] "63 0.01"02:02:38 UMAP embedding parameters a = 1.896 b = 0.8006
02:02:38 Read 1203 rows and found 38 numeric columns
02:02:38 Using Annoy for neighbor search, n_neighbors = 63
02:02:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:02:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a6c7f21
02:02:39 Searching Annoy index using 1 thread, search_k = 6300
02:02:39 Annoy recall = 100%
02:02:43 Commencing smooth kNN distance calibration using 1 thread
02:02:52 Initializing from normalized Laplacian + noise
02:02:52 Commencing optimization for 500 epochs, with 91158 positive edges
02:03:00 Optimization finished
[1] "63 0.02"02:03:00 UMAP embedding parameters a = 1.859 b = 0.8109
02:03:00 Read 1203 rows and found 38 numeric columns
02:03:00 Using Annoy for neighbor search, n_neighbors = 63
02:03:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ad725ea
02:03:00 Searching Annoy index using 1 thread, search_k = 6300
02:03:01 Annoy recall = 100%
02:03:05 Commencing smooth kNN distance calibration using 1 thread
02:03:14 Initializing from normalized Laplacian + noise
02:03:14 Commencing optimization for 500 epochs, with 91158 positive edges
02:03:22 Optimization finished
[1] "63 0.03"02:03:22 UMAP embedding parameters a = 1.822 b = 0.8212
02:03:22 Read 1203 rows and found 38 numeric columns
02:03:22 Using Annoy for neighbor search, n_neighbors = 63
02:03:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713514e4b
02:03:22 Searching Annoy index using 1 thread, search_k = 6300
02:03:23 Annoy recall = 100%
02:03:27 Commencing smooth kNN distance calibration using 1 thread
02:03:36 Initializing from normalized Laplacian + noise
02:03:36 Commencing optimization for 500 epochs, with 91158 positive edges
02:03:44 Optimization finished
[1] "63 0.04"02:03:44 UMAP embedding parameters a = 1.786 b = 0.8316
02:03:44 Read 1203 rows and found 38 numeric columns
02:03:44 Using Annoy for neighbor search, n_neighbors = 63
02:03:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720a3a997
02:03:44 Searching Annoy index using 1 thread, search_k = 6300
02:03:45 Annoy recall = 100%
02:03:49 Commencing smooth kNN distance calibration using 1 thread
02:03:58 Initializing from normalized Laplacian + noise
02:03:58 Commencing optimization for 500 epochs, with 91158 positive edges
02:04:05 Optimization finished
[1] "63 0.05"02:04:06 UMAP embedding parameters a = 1.75 b = 0.8421
02:04:06 Read 1203 rows and found 38 numeric columns
02:04:06 Using Annoy for neighbor search, n_neighbors = 63
02:04:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749aa32d5
02:04:06 Searching Annoy index using 1 thread, search_k = 6300
02:04:07 Annoy recall = 100%
02:04:11 Commencing smooth kNN distance calibration using 1 thread
02:04:20 Initializing from normalized Laplacian + noise
02:04:20 Commencing optimization for 500 epochs, with 91158 positive edges
02:04:27 Optimization finished
[1] "63 0.06"02:04:28 UMAP embedding parameters a = 1.715 b = 0.8526
02:04:28 Read 1203 rows and found 38 numeric columns
02:04:28 Using Annoy for neighbor search, n_neighbors = 63
02:04:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c2140b
02:04:28 Searching Annoy index using 1 thread, search_k = 6300
02:04:29 Annoy recall = 100%
02:04:33 Commencing smooth kNN distance calibration using 1 thread
02:04:42 Initializing from normalized Laplacian + noise
02:04:42 Commencing optimization for 500 epochs, with 91158 positive edges
02:04:49 Optimization finished
[1] "63 0.07"02:04:49 UMAP embedding parameters a = 1.68 b = 0.8631
02:04:49 Read 1203 rows and found 38 numeric columns
02:04:49 Using Annoy for neighbor search, n_neighbors = 63
02:04:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743c01cd
02:04:50 Searching Annoy index using 1 thread, search_k = 6300
02:04:50 Annoy recall = 100%
02:04:55 Commencing smooth kNN distance calibration using 1 thread
02:05:03 Initializing from normalized Laplacian + noise
02:05:04 Commencing optimization for 500 epochs, with 91158 positive edges
02:05:11 Optimization finished
[1] "63 0.08"02:05:11 UMAP embedding parameters a = 1.645 b = 0.8737
02:05:11 Read 1203 rows and found 38 numeric columns
02:05:11 Using Annoy for neighbor search, n_neighbors = 63
02:05:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724da2599
02:05:12 Searching Annoy index using 1 thread, search_k = 6300
02:05:12 Annoy recall = 100%
02:05:17 Commencing smooth kNN distance calibration using 1 thread
02:05:26 Initializing from normalized Laplacian + noise
02:05:26 Commencing optimization for 500 epochs, with 91158 positive edges
02:05:33 Optimization finished
[1] "63 0.09"02:05:33 UMAP embedding parameters a = 1.611 b = 0.8844
02:05:33 Read 1203 rows and found 38 numeric columns
02:05:33 Using Annoy for neighbor search, n_neighbors = 63
02:05:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87169bccf7
02:05:34 Searching Annoy index using 1 thread, search_k = 6300
02:05:34 Annoy recall = 100%
02:05:39 Commencing smooth kNN distance calibration using 1 thread
02:05:47 Initializing from normalized Laplacian + noise
02:05:47 Commencing optimization for 500 epochs, with 91158 positive edges
02:05:55 Optimization finished
[1] "63 0.1"02:05:55 UMAP embedding parameters a = 1.577 b = 0.8951
02:05:55 Read 1203 rows and found 38 numeric columns
02:05:55 Using Annoy for neighbor search, n_neighbors = 63
02:05:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a0fa7f3
02:05:56 Searching Annoy index using 1 thread, search_k = 6300
02:05:56 Annoy recall = 100%
02:06:00 Commencing smooth kNN distance calibration using 1 thread
02:06:09 Initializing from normalized Laplacian + noise
02:06:09 Commencing optimization for 500 epochs, with 91158 positive edges
02:06:17 Optimization finished
[1] "63 0.11"02:06:17 UMAP embedding parameters a = 1.544 b = 0.9058
02:06:17 Read 1203 rows and found 38 numeric columns
02:06:17 Using Annoy for neighbor search, n_neighbors = 63
02:06:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873393c56a
02:06:17 Searching Annoy index using 1 thread, search_k = 6300
02:06:18 Annoy recall = 100%
02:06:22 Commencing smooth kNN distance calibration using 1 thread
02:06:31 Initializing from normalized Laplacian + noise
02:06:31 Commencing optimization for 500 epochs, with 91158 positive edges
02:06:39 Optimization finished
[1] "63 0.12"02:06:39 UMAP embedding parameters a = 1.51 b = 0.9165
02:06:39 Read 1203 rows and found 38 numeric columns
02:06:39 Using Annoy for neighbor search, n_neighbors = 63
02:06:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773b03f7a
02:06:39 Searching Annoy index using 1 thread, search_k = 6300
02:06:40 Annoy recall = 100%
02:06:44 Commencing smooth kNN distance calibration using 1 thread
02:06:53 Initializing from normalized Laplacian + noise
02:06:53 Commencing optimization for 500 epochs, with 91158 positive edges
02:07:01 Optimization finished
[1] "63 0.13"02:07:01 UMAP embedding parameters a = 1.478 b = 0.9272
02:07:01 Read 1203 rows and found 38 numeric columns
02:07:01 Using Annoy for neighbor search, n_neighbors = 63
02:07:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:07:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720b2ffb7
02:07:01 Searching Annoy index using 1 thread, search_k = 6300
02:07:02 Annoy recall = 100%
02:07:06 Commencing smooth kNN distance calibration using 1 thread
02:07:15 Initializing from normalized Laplacian + noise
02:07:15 Commencing optimization for 500 epochs, with 91158 positive edges
02:07:23 Optimization finished
[1] "63 0.14"02:07:23 UMAP embedding parameters a = 1.446 b = 0.938
02:07:23 Read 1203 rows and found 38 numeric columns
02:07:23 Using Annoy for neighbor search, n_neighbors = 63
02:07:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:07:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723842cd5
02:07:23 Searching Annoy index using 1 thread, search_k = 6300
02:07:24 Annoy recall = 100%
02:07:28 Commencing smooth kNN distance calibration using 1 thread
02:07:37 Initializing from normalized Laplacian + noise
02:07:37 Commencing optimization for 500 epochs, with 91158 positive edges
02:07:44 Optimization finished
[1] "63 0.15"02:07:45 UMAP embedding parameters a = 1.414 b = 0.9488
02:07:45 Read 1203 rows and found 38 numeric columns
02:07:45 Using Annoy for neighbor search, n_neighbors = 63
02:07:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:07:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776cc9cdf
02:07:45 Searching Annoy index using 1 thread, search_k = 6300
02:07:46 Annoy recall = 100%
02:07:50 Commencing smooth kNN distance calibration using 1 thread
02:07:59 Initializing from normalized Laplacian + noise
02:07:59 Commencing optimization for 500 epochs, with 91158 positive edges
02:08:06 Optimization finished
[1] "63 0.16"02:08:07 UMAP embedding parameters a = 1.383 b = 0.9596
02:08:07 Read 1203 rows and found 38 numeric columns
02:08:07 Using Annoy for neighbor search, n_neighbors = 63
02:08:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:08:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871efa043
02:08:07 Searching Annoy index using 1 thread, search_k = 6300
02:08:07 Annoy recall = 100%
02:08:12 Commencing smooth kNN distance calibration using 1 thread
02:08:21 Initializing from normalized Laplacian + noise
02:08:21 Commencing optimization for 500 epochs, with 91158 positive edges
02:08:28 Optimization finished
[1] "63 0.17"02:08:29 UMAP embedding parameters a = 1.352 b = 0.9704
02:08:29 Read 1203 rows and found 38 numeric columns
02:08:29 Using Annoy for neighbor search, n_neighbors = 63
02:08:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:08:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877aaf3189
02:08:29 Searching Annoy index using 1 thread, search_k = 6300
02:08:30 Annoy recall = 100%
02:08:34 Commencing smooth kNN distance calibration using 1 thread
02:08:43 Initializing from normalized Laplacian + noise
02:08:43 Commencing optimization for 500 epochs, with 91158 positive edges
02:08:50 Optimization finished
[1] "63 0.18"02:08:51 UMAP embedding parameters a = 1.321 b = 0.9813
02:08:51 Read 1203 rows and found 38 numeric columns
02:08:51 Using Annoy for neighbor search, n_neighbors = 63
02:08:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:08:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720ae364e
02:08:51 Searching Annoy index using 1 thread, search_k = 6300
02:08:51 Annoy recall = 100%
02:08:56 Commencing smooth kNN distance calibration using 1 thread
02:09:05 Initializing from normalized Laplacian + noise
02:09:05 Commencing optimization for 500 epochs, with 91158 positive edges
02:09:12 Optimization finished
[1] "63 0.19"02:09:13 UMAP embedding parameters a = 1.292 b = 0.9921
02:09:13 Read 1203 rows and found 38 numeric columns
02:09:13 Using Annoy for neighbor search, n_neighbors = 63
02:09:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:09:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d134f75
02:09:13 Searching Annoy index using 1 thread, search_k = 6300
02:09:13 Annoy recall = 100%
02:09:18 Commencing smooth kNN distance calibration using 1 thread
02:09:27 Initializing from normalized Laplacian + noise
02:09:27 Commencing optimization for 500 epochs, with 91158 positive edges
02:09:34 Optimization finished
[1] "63 0.2"02:09:35 UMAP embedding parameters a = 1.262 b = 1.003
02:09:35 Read 1203 rows and found 38 numeric columns
02:09:35 Using Annoy for neighbor search, n_neighbors = 63
02:09:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:09:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b9a454f
02:09:35 Searching Annoy index using 1 thread, search_k = 6300
02:09:35 Annoy recall = 100%
02:09:40 Commencing smooth kNN distance calibration using 1 thread
02:09:49 Initializing from normalized Laplacian + noise
02:09:49 Commencing optimization for 500 epochs, with 91158 positive edges
02:09:56 Optimization finished
[1] "64 0"02:09:56 UMAP embedding parameters a = 1.933 b = 0.7905
02:09:56 Read 1203 rows and found 38 numeric columns
02:09:56 Using Annoy for neighbor search, n_neighbors = 64
02:09:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:09:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cc1822e
02:09:57 Searching Annoy index using 1 thread, search_k = 6400
02:09:57 Annoy recall = 100%
02:10:02 Commencing smooth kNN distance calibration using 1 thread
02:10:11 Initializing from normalized Laplacian + noise
02:10:11 Commencing optimization for 500 epochs, with 92546 positive edges
02:10:18 Optimization finished
[1] "64 0.01"02:10:18 UMAP embedding parameters a = 1.896 b = 0.8006
02:10:18 Read 1203 rows and found 38 numeric columns
02:10:18 Using Annoy for neighbor search, n_neighbors = 64
02:10:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:10:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877dd82d77
02:10:19 Searching Annoy index using 1 thread, search_k = 6400
02:10:19 Annoy recall = 100%
02:10:24 Commencing smooth kNN distance calibration using 1 thread
02:10:33 Initializing from normalized Laplacian + noise
02:10:33 Commencing optimization for 500 epochs, with 92546 positive edges
02:10:40 Optimization finished
[1] "64 0.02"02:10:41 UMAP embedding parameters a = 1.859 b = 0.8109
02:10:41 Read 1203 rows and found 38 numeric columns
02:10:41 Using Annoy for neighbor search, n_neighbors = 64
02:10:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:10:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742d874d8
02:10:41 Searching Annoy index using 1 thread, search_k = 6400
02:10:42 Annoy recall = 100%
02:10:46 Commencing smooth kNN distance calibration using 1 thread
02:10:55 Initializing from normalized Laplacian + noise
02:10:55 Commencing optimization for 500 epochs, with 92546 positive edges
02:11:02 Optimization finished
[1] "64 0.03"02:11:03 UMAP embedding parameters a = 1.822 b = 0.8212
02:11:03 Read 1203 rows and found 38 numeric columns
02:11:03 Using Annoy for neighbor search, n_neighbors = 64
02:11:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:11:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c463471
02:11:03 Searching Annoy index using 1 thread, search_k = 6400
02:11:03 Annoy recall = 100%
02:11:08 Commencing smooth kNN distance calibration using 1 thread
02:11:17 Initializing from normalized Laplacian + noise
02:11:17 Commencing optimization for 500 epochs, with 92546 positive edges
02:11:24 Optimization finished
[1] "64 0.04"02:11:25 UMAP embedding parameters a = 1.786 b = 0.8316
02:11:25 Read 1203 rows and found 38 numeric columns
02:11:25 Using Annoy for neighbor search, n_neighbors = 64
02:11:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:11:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c79576d
02:11:25 Searching Annoy index using 1 thread, search_k = 6400
02:11:26 Annoy recall = 100%
02:11:30 Commencing smooth kNN distance calibration using 1 thread
02:11:39 Initializing from normalized Laplacian + noise
02:11:39 Commencing optimization for 500 epochs, with 92546 positive edges
02:11:47 Optimization finished
[1] "64 0.05"02:11:47 UMAP embedding parameters a = 1.75 b = 0.8421
02:11:47 Read 1203 rows and found 38 numeric columns
02:11:47 Using Annoy for neighbor search, n_neighbors = 64
02:11:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:11:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713a20d14
02:11:47 Searching Annoy index using 1 thread, search_k = 6400
02:11:48 Annoy recall = 100%
02:11:52 Commencing smooth kNN distance calibration using 1 thread
02:12:01 Initializing from normalized Laplacian + noise
02:12:01 Commencing optimization for 500 epochs, with 92546 positive edges
02:12:09 Optimization finished
[1] "64 0.06"02:12:09 UMAP embedding parameters a = 1.715 b = 0.8526
02:12:09 Read 1203 rows and found 38 numeric columns
02:12:09 Using Annoy for neighbor search, n_neighbors = 64
02:12:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:12:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739f2cc53
02:12:09 Searching Annoy index using 1 thread, search_k = 6400
02:12:10 Annoy recall = 100%
02:12:14 Commencing smooth kNN distance calibration using 1 thread
02:12:23 Initializing from normalized Laplacian + noise
02:12:23 Commencing optimization for 500 epochs, with 92546 positive edges
02:12:31 Optimization finished
[1] "64 0.07"02:12:31 UMAP embedding parameters a = 1.68 b = 0.8631
02:12:31 Read 1203 rows and found 38 numeric columns
02:12:31 Using Annoy for neighbor search, n_neighbors = 64
02:12:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:12:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878b529d1
02:12:31 Searching Annoy index using 1 thread, search_k = 6400
02:12:32 Annoy recall = 100%
02:12:36 Commencing smooth kNN distance calibration using 1 thread
02:12:45 Initializing from normalized Laplacian + noise
02:12:45 Commencing optimization for 500 epochs, with 92546 positive edges
02:12:53 Optimization finished
[1] "64 0.08"02:12:53 UMAP embedding parameters a = 1.645 b = 0.8737
02:12:53 Read 1203 rows and found 38 numeric columns
02:12:53 Using Annoy for neighbor search, n_neighbors = 64
02:12:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:12:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746593d93
02:12:53 Searching Annoy index using 1 thread, search_k = 6400
02:12:54 Annoy recall = 100%
02:12:58 Commencing smooth kNN distance calibration using 1 thread
02:13:07 Initializing from normalized Laplacian + noise
02:13:07 Commencing optimization for 500 epochs, with 92546 positive edges
02:13:15 Optimization finished
[1] "64 0.09"02:13:15 UMAP embedding parameters a = 1.611 b = 0.8844
02:13:15 Read 1203 rows and found 38 numeric columns
02:13:15 Using Annoy for neighbor search, n_neighbors = 64
02:13:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:13:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e787388
02:13:16 Searching Annoy index using 1 thread, search_k = 6400
02:13:16 Annoy recall = 100%
02:13:20 Commencing smooth kNN distance calibration using 1 thread
02:13:29 Initializing from normalized Laplacian + noise
02:13:29 Commencing optimization for 500 epochs, with 92546 positive edges
02:13:37 Optimization finished
[1] "64 0.1"02:13:37 UMAP embedding parameters a = 1.577 b = 0.8951
02:13:37 Read 1203 rows and found 38 numeric columns
02:13:37 Using Annoy for neighbor search, n_neighbors = 64
02:13:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:13:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743fed0d
02:13:38 Searching Annoy index using 1 thread, search_k = 6400
02:13:38 Annoy recall = 100%
02:13:43 Commencing smooth kNN distance calibration using 1 thread
02:13:52 Initializing from normalized Laplacian + noise
02:13:52 Commencing optimization for 500 epochs, with 92546 positive edges
02:13:59 Optimization finished
[1] "64 0.11"02:14:00 UMAP embedding parameters a = 1.544 b = 0.9058
02:14:00 Read 1203 rows and found 38 numeric columns
02:14:00 Using Annoy for neighbor search, n_neighbors = 64
02:14:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:14:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c5bcb4
02:14:00 Searching Annoy index using 1 thread, search_k = 6400
02:14:00 Annoy recall = 100%
02:14:05 Commencing smooth kNN distance calibration using 1 thread
02:14:14 Initializing from normalized Laplacian + noise
02:14:14 Commencing optimization for 500 epochs, with 92546 positive edges
02:14:21 Optimization finished
[1] "64 0.12"02:14:22 UMAP embedding parameters a = 1.51 b = 0.9165
02:14:22 Read 1203 rows and found 38 numeric columns
02:14:22 Using Annoy for neighbor search, n_neighbors = 64
02:14:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:14:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8794f9972
02:14:22 Searching Annoy index using 1 thread, search_k = 6400
02:14:23 Annoy recall = 100%
02:14:27 Commencing smooth kNN distance calibration using 1 thread
02:14:36 Initializing from normalized Laplacian + noise
02:14:36 Commencing optimization for 500 epochs, with 92546 positive edges
02:14:44 Optimization finished
[1] "64 0.13"02:14:44 UMAP embedding parameters a = 1.478 b = 0.9272
02:14:44 Read 1203 rows and found 38 numeric columns
02:14:44 Using Annoy for neighbor search, n_neighbors = 64
02:14:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:14:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717913b58
02:14:44 Searching Annoy index using 1 thread, search_k = 6400
02:14:45 Annoy recall = 100%
02:14:49 Commencing smooth kNN distance calibration using 1 thread
02:14:58 Initializing from normalized Laplacian + noise
02:14:58 Commencing optimization for 500 epochs, with 92546 positive edges
02:15:06 Optimization finished
[1] "64 0.14"02:15:06 UMAP embedding parameters a = 1.446 b = 0.938
02:15:06 Read 1203 rows and found 38 numeric columns
02:15:06 Using Annoy for neighbor search, n_neighbors = 64
02:15:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:15:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872169664b
02:15:06 Searching Annoy index using 1 thread, search_k = 6400
02:15:07 Annoy recall = 100%
02:15:11 Commencing smooth kNN distance calibration using 1 thread
02:15:20 Initializing from normalized Laplacian + noise
02:15:20 Commencing optimization for 500 epochs, with 92546 positive edges
02:15:28 Optimization finished
[1] "64 0.15"02:15:28 UMAP embedding parameters a = 1.414 b = 0.9488
02:15:28 Read 1203 rows and found 38 numeric columns
02:15:28 Using Annoy for neighbor search, n_neighbors = 64
02:15:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:15:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752f9cc47
02:15:29 Searching Annoy index using 1 thread, search_k = 6400
02:15:29 Annoy recall = 100%
02:15:34 Commencing smooth kNN distance calibration using 1 thread
02:15:43 Initializing from normalized Laplacian + noise
02:15:43 Commencing optimization for 500 epochs, with 92546 positive edges
02:15:50 Optimization finished
[1] "64 0.16"02:15:51 UMAP embedding parameters a = 1.383 b = 0.9596
02:15:51 Read 1203 rows and found 38 numeric columns
02:15:51 Using Annoy for neighbor search, n_neighbors = 64
02:15:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:15:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f534f63
02:15:51 Searching Annoy index using 1 thread, search_k = 6400
02:15:52 Annoy recall = 100%
02:15:56 Commencing smooth kNN distance calibration using 1 thread
02:16:05 Initializing from normalized Laplacian + noise
02:16:05 Commencing optimization for 500 epochs, with 92546 positive edges
02:16:13 Optimization finished
[1] "64 0.17"02:16:13 UMAP embedding parameters a = 1.352 b = 0.9704
02:16:13 Read 1203 rows and found 38 numeric columns
02:16:13 Using Annoy for neighbor search, n_neighbors = 64
02:16:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:16:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725a56819
02:16:13 Searching Annoy index using 1 thread, search_k = 6400
02:16:14 Annoy recall = 100%
02:16:18 Commencing smooth kNN distance calibration using 1 thread
02:16:27 Initializing from normalized Laplacian + noise
02:16:27 Commencing optimization for 500 epochs, with 92546 positive edges
02:16:35 Optimization finished
[1] "64 0.18"02:16:35 UMAP embedding parameters a = 1.321 b = 0.9813
02:16:35 Read 1203 rows and found 38 numeric columns
02:16:35 Using Annoy for neighbor search, n_neighbors = 64
02:16:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:16:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777d3f1e1
02:16:35 Searching Annoy index using 1 thread, search_k = 6400
02:16:36 Annoy recall = 100%
02:16:40 Commencing smooth kNN distance calibration using 1 thread
02:16:49 Initializing from normalized Laplacian + noise
02:16:49 Commencing optimization for 500 epochs, with 92546 positive edges
02:16:57 Optimization finished
[1] "64 0.19"02:16:57 UMAP embedding parameters a = 1.292 b = 0.9921
02:16:57 Read 1203 rows and found 38 numeric columns
02:16:57 Using Annoy for neighbor search, n_neighbors = 64
02:16:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:16:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735ef1c5a
02:16:58 Searching Annoy index using 1 thread, search_k = 6400
02:16:58 Annoy recall = 100%
02:17:03 Commencing smooth kNN distance calibration using 1 thread
02:17:12 Initializing from normalized Laplacian + noise
02:17:12 Commencing optimization for 500 epochs, with 92546 positive edges
02:17:19 Optimization finished
[1] "64 0.2"02:17:20 UMAP embedding parameters a = 1.262 b = 1.003
02:17:20 Read 1203 rows and found 38 numeric columns
02:17:20 Using Annoy for neighbor search, n_neighbors = 64
02:17:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:17:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fb5100c
02:17:20 Searching Annoy index using 1 thread, search_k = 6400
02:17:21 Annoy recall = 100%
02:17:25 Commencing smooth kNN distance calibration using 1 thread
02:17:34 Initializing from normalized Laplacian + noise
02:17:34 Commencing optimization for 500 epochs, with 92546 positive edges
02:17:42 Optimization finished
[1] "65 0"02:17:42 UMAP embedding parameters a = 1.933 b = 0.7905
02:17:42 Read 1203 rows and found 38 numeric columns
02:17:42 Using Annoy for neighbor search, n_neighbors = 65
02:17:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:17:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b67b74b
02:17:42 Searching Annoy index using 1 thread, search_k = 6500
02:17:43 Annoy recall = 100%
02:17:47 Commencing smooth kNN distance calibration using 1 thread
02:17:56 Initializing from normalized Laplacian + noise
02:17:56 Commencing optimization for 500 epochs, with 93908 positive edges
02:18:04 Optimization finished
[1] "65 0.01"02:18:04 UMAP embedding parameters a = 1.896 b = 0.8006
02:18:04 Read 1203 rows and found 38 numeric columns
02:18:04 Using Annoy for neighbor search, n_neighbors = 65
02:18:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:18:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87299f5bd4
02:18:05 Searching Annoy index using 1 thread, search_k = 6500
02:18:05 Annoy recall = 100%
02:18:10 Commencing smooth kNN distance calibration using 1 thread
02:18:19 Initializing from normalized Laplacian + noise
02:18:19 Commencing optimization for 500 epochs, with 93908 positive edges
02:18:26 Optimization finished
[1] "65 0.02"02:18:27 UMAP embedding parameters a = 1.859 b = 0.8109
02:18:27 Read 1203 rows and found 38 numeric columns
02:18:27 Using Annoy for neighbor search, n_neighbors = 65
02:18:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:18:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750680fc4
02:18:27 Searching Annoy index using 1 thread, search_k = 6500
02:18:28 Annoy recall = 100%
02:18:32 Commencing smooth kNN distance calibration using 1 thread
02:18:41 Initializing from normalized Laplacian + noise
02:18:41 Commencing optimization for 500 epochs, with 93908 positive edges
02:18:49 Optimization finished
[1] "65 0.03"02:18:49 UMAP embedding parameters a = 1.822 b = 0.8212
02:18:49 Read 1203 rows and found 38 numeric columns
02:18:49 Using Annoy for neighbor search, n_neighbors = 65
02:18:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:18:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874eebe420
02:18:49 Searching Annoy index using 1 thread, search_k = 6500
02:18:50 Annoy recall = 100%
02:18:54 Commencing smooth kNN distance calibration using 1 thread
02:19:03 Initializing from normalized Laplacian + noise
02:19:03 Commencing optimization for 500 epochs, with 93908 positive edges
02:19:11 Optimization finished
[1] "65 0.04"02:19:11 UMAP embedding parameters a = 1.786 b = 0.8316
02:19:11 Read 1203 rows and found 38 numeric columns
02:19:11 Using Annoy for neighbor search, n_neighbors = 65
02:19:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:19:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87206bf8b3
02:19:11 Searching Annoy index using 1 thread, search_k = 6500
02:19:12 Annoy recall = 100%
02:19:16 Commencing smooth kNN distance calibration using 1 thread
02:19:25 Initializing from normalized Laplacian + noise
02:19:25 Commencing optimization for 500 epochs, with 93908 positive edges
02:19:33 Optimization finished
[1] "65 0.05"02:19:33 UMAP embedding parameters a = 1.75 b = 0.8421
02:19:33 Read 1203 rows and found 38 numeric columns
02:19:33 Using Annoy for neighbor search, n_neighbors = 65
02:19:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:19:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875257b007
02:19:34 Searching Annoy index using 1 thread, search_k = 6500
02:19:34 Annoy recall = 100%
02:19:39 Commencing smooth kNN distance calibration using 1 thread
02:19:48 Initializing from normalized Laplacian + noise
02:19:48 Commencing optimization for 500 epochs, with 93908 positive edges
02:19:56 Optimization finished
[1] "65 0.06"02:19:56 UMAP embedding parameters a = 1.715 b = 0.8526
02:19:56 Read 1203 rows and found 38 numeric columns
02:19:56 Using Annoy for neighbor search, n_neighbors = 65
02:19:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:19:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87499b15a9
02:19:56 Searching Annoy index using 1 thread, search_k = 6500
02:19:57 Annoy recall = 100%
02:20:01 Commencing smooth kNN distance calibration using 1 thread
02:20:10 Initializing from normalized Laplacian + noise
02:20:10 Commencing optimization for 500 epochs, with 93908 positive edges
02:20:18 Optimization finished
[1] "65 0.07"02:20:18 UMAP embedding parameters a = 1.68 b = 0.8631
02:20:18 Read 1203 rows and found 38 numeric columns
02:20:18 Using Annoy for neighbor search, n_neighbors = 65
02:20:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:20:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87411a2f01
02:20:18 Searching Annoy index using 1 thread, search_k = 6500
02:20:19 Annoy recall = 100%
02:20:23 Commencing smooth kNN distance calibration using 1 thread
02:20:32 Initializing from normalized Laplacian + noise
02:20:32 Commencing optimization for 500 epochs, with 93908 positive edges
02:20:40 Optimization finished
[1] "65 0.08"02:20:40 UMAP embedding parameters a = 1.645 b = 0.8737
02:20:40 Read 1203 rows and found 38 numeric columns
02:20:40 Using Annoy for neighbor search, n_neighbors = 65
02:20:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:20:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f6aff7d
02:20:41 Searching Annoy index using 1 thread, search_k = 6500
02:20:41 Annoy recall = 100%
02:20:46 Commencing smooth kNN distance calibration using 1 thread
02:20:55 Initializing from normalized Laplacian + noise
02:20:55 Commencing optimization for 500 epochs, with 93908 positive edges
02:21:03 Optimization finished
[1] "65 0.09"02:21:03 UMAP embedding parameters a = 1.611 b = 0.8844
02:21:03 Read 1203 rows and found 38 numeric columns
02:21:03 Using Annoy for neighbor search, n_neighbors = 65
02:21:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:21:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765355af8
02:21:03 Searching Annoy index using 1 thread, search_k = 6500
02:21:04 Annoy recall = 100%
02:21:08 Commencing smooth kNN distance calibration using 1 thread
02:21:17 Initializing from normalized Laplacian + noise
02:21:17 Commencing optimization for 500 epochs, with 93908 positive edges
02:21:25 Optimization finished
[1] "65 0.1"02:21:25 UMAP embedding parameters a = 1.577 b = 0.8951
02:21:25 Read 1203 rows and found 38 numeric columns
02:21:25 Using Annoy for neighbor search, n_neighbors = 65
02:21:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:21:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ddbb130
02:21:25 Searching Annoy index using 1 thread, search_k = 6500
02:21:26 Annoy recall = 100%
02:21:30 Commencing smooth kNN distance calibration using 1 thread
02:21:39 Initializing from normalized Laplacian + noise
02:21:39 Commencing optimization for 500 epochs, with 93908 positive edges
02:21:47 Optimization finished
[1] "65 0.11"02:21:48 UMAP embedding parameters a = 1.544 b = 0.9058
02:21:48 Read 1203 rows and found 38 numeric columns
02:21:48 Using Annoy for neighbor search, n_neighbors = 65
02:21:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:21:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d432cf4
02:21:48 Searching Annoy index using 1 thread, search_k = 6500
02:21:48 Annoy recall = 100%
02:21:53 Commencing smooth kNN distance calibration using 1 thread
02:22:02 Initializing from normalized Laplacian + noise
02:22:02 Commencing optimization for 500 epochs, with 93908 positive edges
02:22:10 Optimization finished
[1] "65 0.12"02:22:10 UMAP embedding parameters a = 1.51 b = 0.9165
02:22:10 Read 1203 rows and found 38 numeric columns
02:22:10 Using Annoy for neighbor search, n_neighbors = 65
02:22:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:22:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87280dcfd0
02:22:10 Searching Annoy index using 1 thread, search_k = 6500
02:22:11 Annoy recall = 100%
02:22:15 Commencing smooth kNN distance calibration using 1 thread
02:22:24 Initializing from normalized Laplacian + noise
02:22:24 Commencing optimization for 500 epochs, with 93908 positive edges
02:22:32 Optimization finished
[1] "65 0.13"02:22:32 UMAP embedding parameters a = 1.478 b = 0.9272
02:22:32 Read 1203 rows and found 38 numeric columns
02:22:32 Using Annoy for neighbor search, n_neighbors = 65
02:22:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:22:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a21e5a1
02:22:33 Searching Annoy index using 1 thread, search_k = 6500
02:22:33 Annoy recall = 100%
02:22:38 Commencing smooth kNN distance calibration using 1 thread
02:22:47 Initializing from normalized Laplacian + noise
02:22:47 Commencing optimization for 500 epochs, with 93908 positive edges
02:22:55 Optimization finished
[1] "65 0.14"02:22:55 UMAP embedding parameters a = 1.446 b = 0.938
02:22:55 Read 1203 rows and found 38 numeric columns
02:22:55 Using Annoy for neighbor search, n_neighbors = 65
02:22:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:22:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729bc8461
02:22:55 Searching Annoy index using 1 thread, search_k = 6500
02:22:56 Annoy recall = 100%
02:23:00 Commencing smooth kNN distance calibration using 1 thread
02:23:09 Initializing from normalized Laplacian + noise
02:23:09 Commencing optimization for 500 epochs, with 93908 positive edges
02:23:17 Optimization finished
[1] "65 0.15"02:23:17 UMAP embedding parameters a = 1.414 b = 0.9488
02:23:17 Read 1203 rows and found 38 numeric columns
02:23:17 Using Annoy for neighbor search, n_neighbors = 65
02:23:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:23:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bafdce4
02:23:18 Searching Annoy index using 1 thread, search_k = 6500
02:23:18 Annoy recall = 100%
02:23:22 Commencing smooth kNN distance calibration using 1 thread
02:23:32 Initializing from normalized Laplacian + noise
02:23:32 Commencing optimization for 500 epochs, with 93908 positive edges
02:23:39 Optimization finished
[1] "65 0.16"02:23:40 UMAP embedding parameters a = 1.383 b = 0.9596
02:23:40 Read 1203 rows and found 38 numeric columns
02:23:40 Using Annoy for neighbor search, n_neighbors = 65
02:23:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:23:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877414b1f4
02:23:40 Searching Annoy index using 1 thread, search_k = 6500
02:23:40 Annoy recall = 100%
02:23:45 Commencing smooth kNN distance calibration using 1 thread
02:23:54 Initializing from normalized Laplacian + noise
02:23:54 Commencing optimization for 500 epochs, with 93908 positive edges
02:24:02 Optimization finished
[1] "65 0.17"02:24:02 UMAP embedding parameters a = 1.352 b = 0.9704
02:24:02 Read 1203 rows and found 38 numeric columns
02:24:02 Using Annoy for neighbor search, n_neighbors = 65
02:24:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:24:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873271ae32
02:24:03 Searching Annoy index using 1 thread, search_k = 6500
02:24:03 Annoy recall = 100%
02:24:08 Commencing smooth kNN distance calibration using 1 thread
02:24:16 Initializing from normalized Laplacian + noise
02:24:16 Commencing optimization for 500 epochs, with 93908 positive edges
02:24:24 Optimization finished
[1] "65 0.18"02:24:24 UMAP embedding parameters a = 1.321 b = 0.9813
02:24:24 Read 1203 rows and found 38 numeric columns
02:24:25 Using Annoy for neighbor search, n_neighbors = 65
02:24:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:24:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872091a78
02:24:25 Searching Annoy index using 1 thread, search_k = 6500
02:24:25 Annoy recall = 100%
02:24:30 Commencing smooth kNN distance calibration using 1 thread
02:24:39 Initializing from normalized Laplacian + noise
02:24:39 Commencing optimization for 500 epochs, with 93908 positive edges
02:24:47 Optimization finished
[1] "65 0.19"02:24:47 UMAP embedding parameters a = 1.292 b = 0.9921
02:24:47 Read 1203 rows and found 38 numeric columns
02:24:47 Using Annoy for neighbor search, n_neighbors = 65
02:24:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:24:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87328d257c
02:24:47 Searching Annoy index using 1 thread, search_k = 6500
02:24:48 Annoy recall = 100%
02:24:52 Commencing smooth kNN distance calibration using 1 thread
02:25:01 Initializing from normalized Laplacian + noise
02:25:02 Commencing optimization for 500 epochs, with 93908 positive edges
02:25:09 Optimization finished
[1] "65 0.2"02:25:10 UMAP embedding parameters a = 1.262 b = 1.003
02:25:10 Read 1203 rows and found 38 numeric columns
02:25:10 Using Annoy for neighbor search, n_neighbors = 65
02:25:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:25:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736b19b3f
02:25:10 Searching Annoy index using 1 thread, search_k = 6500
02:25:10 Annoy recall = 100%
02:25:15 Commencing smooth kNN distance calibration using 1 thread
02:25:24 Initializing from normalized Laplacian + noise
02:25:24 Commencing optimization for 500 epochs, with 93908 positive edges
02:25:32 Optimization finished
[1] "66 0"02:25:32 UMAP embedding parameters a = 1.933 b = 0.7905
02:25:32 Read 1203 rows and found 38 numeric columns
02:25:32 Using Annoy for neighbor search, n_neighbors = 66
02:25:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:25:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ced72c
02:25:32 Searching Annoy index using 1 thread, search_k = 6600
02:25:33 Annoy recall = 100%
02:25:37 Commencing smooth kNN distance calibration using 1 thread
02:25:46 Initializing from normalized Laplacian + noise
02:25:46 Commencing optimization for 500 epochs, with 95290 positive edges
02:25:54 Optimization finished
[1] "66 0.01"02:25:55 UMAP embedding parameters a = 1.896 b = 0.8006
02:25:55 Read 1203 rows and found 38 numeric columns
02:25:55 Using Annoy for neighbor search, n_neighbors = 66
02:25:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:25:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bdcbeee
02:25:55 Searching Annoy index using 1 thread, search_k = 6600
02:25:55 Annoy recall = 100%
02:26:00 Commencing smooth kNN distance calibration using 1 thread
02:26:09 Initializing from normalized Laplacian + noise
02:26:09 Commencing optimization for 500 epochs, with 95290 positive edges
02:26:17 Optimization finished
[1] "66 0.02"02:26:17 UMAP embedding parameters a = 1.859 b = 0.8109
02:26:17 Read 1203 rows and found 38 numeric columns
02:26:17 Using Annoy for neighbor search, n_neighbors = 66
02:26:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:26:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e42d697
02:26:18 Searching Annoy index using 1 thread, search_k = 6600
02:26:18 Annoy recall = 100%
02:26:22 Commencing smooth kNN distance calibration using 1 thread
02:26:31 Initializing from normalized Laplacian + noise
02:26:32 Commencing optimization for 500 epochs, with 95290 positive edges
02:26:39 Optimization finished
[1] "66 0.03"02:26:40 UMAP embedding parameters a = 1.822 b = 0.8212
02:26:40 Read 1203 rows and found 38 numeric columns
02:26:40 Using Annoy for neighbor search, n_neighbors = 66
02:26:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:26:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724383d78
02:26:40 Searching Annoy index using 1 thread, search_k = 6600
02:26:41 Annoy recall = 100%
02:26:45 Commencing smooth kNN distance calibration using 1 thread
02:26:54 Initializing from normalized Laplacian + noise
02:26:54 Commencing optimization for 500 epochs, with 95290 positive edges
02:27:02 Optimization finished
[1] "66 0.04"02:27:02 UMAP embedding parameters a = 1.786 b = 0.8316
02:27:02 Read 1203 rows and found 38 numeric columns
02:27:02 Using Annoy for neighbor search, n_neighbors = 66
02:27:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:27:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ed68b35
02:27:03 Searching Annoy index using 1 thread, search_k = 6600
02:27:03 Annoy recall = 100%
02:27:08 Commencing smooth kNN distance calibration using 1 thread
02:27:17 Initializing from normalized Laplacian + noise
02:27:17 Commencing optimization for 500 epochs, with 95290 positive edges
02:27:25 Optimization finished
[1] "66 0.05"02:27:25 UMAP embedding parameters a = 1.75 b = 0.8421
02:27:25 Read 1203 rows and found 38 numeric columns
02:27:25 Using Annoy for neighbor search, n_neighbors = 66
02:27:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:27:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d9625fb
02:27:25 Searching Annoy index using 1 thread, search_k = 6600
02:27:26 Annoy recall = 100%
02:27:30 Commencing smooth kNN distance calibration using 1 thread
02:27:39 Initializing from normalized Laplacian + noise
02:27:39 Commencing optimization for 500 epochs, with 95290 positive edges
02:27:47 Optimization finished
[1] "66 0.06"02:27:47 UMAP embedding parameters a = 1.715 b = 0.8526
02:27:47 Read 1203 rows and found 38 numeric columns
02:27:47 Using Annoy for neighbor search, n_neighbors = 66
02:27:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:27:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749dda591
02:27:48 Searching Annoy index using 1 thread, search_k = 6600
02:27:48 Annoy recall = 100%
02:27:53 Commencing smooth kNN distance calibration using 1 thread
02:28:02 Initializing from normalized Laplacian + noise
02:28:02 Commencing optimization for 500 epochs, with 95290 positive edges
02:28:10 Optimization finished
[1] "66 0.07"02:28:10 UMAP embedding parameters a = 1.68 b = 0.8631
02:28:10 Read 1203 rows and found 38 numeric columns
02:28:10 Using Annoy for neighbor search, n_neighbors = 66
02:28:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:28:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aa7d16
02:28:11 Searching Annoy index using 1 thread, search_k = 6600
02:28:11 Annoy recall = 100%
02:28:16 Commencing smooth kNN distance calibration using 1 thread
02:28:25 Initializing from normalized Laplacian + noise
02:28:25 Commencing optimization for 500 epochs, with 95290 positive edges
02:28:32 Optimization finished
[1] "66 0.08"02:28:33 UMAP embedding parameters a = 1.645 b = 0.8737
02:28:33 Read 1203 rows and found 38 numeric columns
02:28:33 Using Annoy for neighbor search, n_neighbors = 66
02:28:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:28:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723854255
02:28:33 Searching Annoy index using 1 thread, search_k = 6600
02:28:34 Annoy recall = 100%
02:28:38 Commencing smooth kNN distance calibration using 1 thread
02:28:47 Initializing from normalized Laplacian + noise
02:28:47 Commencing optimization for 500 epochs, with 95290 positive edges
02:28:55 Optimization finished
[1] "66 0.09"02:28:55 UMAP embedding parameters a = 1.611 b = 0.8844
02:28:55 Read 1203 rows and found 38 numeric columns
02:28:55 Using Annoy for neighbor search, n_neighbors = 66
02:28:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:28:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877992b59d
02:28:56 Searching Annoy index using 1 thread, search_k = 6600
02:28:56 Annoy recall = 100%
02:29:01 Commencing smooth kNN distance calibration using 1 thread
02:29:10 Initializing from normalized Laplacian + noise
02:29:10 Commencing optimization for 500 epochs, with 95290 positive edges
02:29:18 Optimization finished
[1] "66 0.1"02:29:18 UMAP embedding parameters a = 1.577 b = 0.8951
02:29:18 Read 1203 rows and found 38 numeric columns
02:29:18 Using Annoy for neighbor search, n_neighbors = 66
02:29:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:29:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732123461
02:29:19 Searching Annoy index using 1 thread, search_k = 6600
02:29:19 Annoy recall = 100%
02:29:24 Commencing smooth kNN distance calibration using 1 thread
02:29:33 Initializing from normalized Laplacian + noise
02:29:33 Commencing optimization for 500 epochs, with 95290 positive edges
02:29:41 Optimization finished
[1] "66 0.11"02:29:41 UMAP embedding parameters a = 1.544 b = 0.9058
02:29:41 Read 1203 rows and found 38 numeric columns
02:29:41 Using Annoy for neighbor search, n_neighbors = 66
02:29:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:29:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d249e2a
02:29:41 Searching Annoy index using 1 thread, search_k = 6600
02:29:42 Annoy recall = 100%
02:29:46 Commencing smooth kNN distance calibration using 1 thread
02:29:55 Initializing from normalized Laplacian + noise
02:29:55 Commencing optimization for 500 epochs, with 95290 positive edges
02:30:03 Optimization finished
[1] "66 0.12"02:30:04 UMAP embedding parameters a = 1.51 b = 0.9165
02:30:04 Read 1203 rows and found 38 numeric columns
02:30:04 Using Annoy for neighbor search, n_neighbors = 66
02:30:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:30:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749fac561
02:30:04 Searching Annoy index using 1 thread, search_k = 6600
02:30:05 Annoy recall = 100%
02:30:09 Commencing smooth kNN distance calibration using 1 thread
02:30:18 Initializing from normalized Laplacian + noise
02:30:18 Commencing optimization for 500 epochs, with 95290 positive edges
02:30:26 Optimization finished
[1] "66 0.13"02:30:26 UMAP embedding parameters a = 1.478 b = 0.9272
02:30:26 Read 1203 rows and found 38 numeric columns
02:30:26 Using Annoy for neighbor search, n_neighbors = 66
02:30:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:30:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fe1881
02:30:27 Searching Annoy index using 1 thread, search_k = 6600
02:30:27 Annoy recall = 100%
02:30:32 Commencing smooth kNN distance calibration using 1 thread
02:30:41 Initializing from normalized Laplacian + noise
02:30:41 Commencing optimization for 500 epochs, with 95290 positive edges
02:30:49 Optimization finished
[1] "66 0.14"02:30:49 UMAP embedding parameters a = 1.446 b = 0.938
02:30:49 Read 1203 rows and found 38 numeric columns
02:30:49 Using Annoy for neighbor search, n_neighbors = 66
02:30:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:30:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d9096dd
02:30:49 Searching Annoy index using 1 thread, search_k = 6600
02:30:50 Annoy recall = 100%
02:30:55 Commencing smooth kNN distance calibration using 1 thread
02:31:04 Initializing from normalized Laplacian + noise
02:31:04 Commencing optimization for 500 epochs, with 95290 positive edges
02:31:12 Optimization finished
[1] "66 0.15"02:31:12 UMAP embedding parameters a = 1.414 b = 0.9488
02:31:12 Read 1203 rows and found 38 numeric columns
02:31:12 Using Annoy for neighbor search, n_neighbors = 66
02:31:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:31:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c527569
02:31:12 Searching Annoy index using 1 thread, search_k = 6600
02:31:13 Annoy recall = 100%
02:31:17 Commencing smooth kNN distance calibration using 1 thread
02:31:27 Initializing from normalized Laplacian + noise
02:31:27 Commencing optimization for 500 epochs, with 95290 positive edges
02:31:34 Optimization finished
[1] "66 0.16"02:31:35 UMAP embedding parameters a = 1.383 b = 0.9596
02:31:35 Read 1203 rows and found 38 numeric columns
02:31:35 Using Annoy for neighbor search, n_neighbors = 66
02:31:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:31:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a992e2a
02:31:35 Searching Annoy index using 1 thread, search_k = 6600
02:31:36 Annoy recall = 100%
02:31:40 Commencing smooth kNN distance calibration using 1 thread
02:31:49 Initializing from normalized Laplacian + noise
02:31:49 Commencing optimization for 500 epochs, with 95290 positive edges
02:31:57 Optimization finished
[1] "66 0.17"02:31:58 UMAP embedding parameters a = 1.352 b = 0.9704
02:31:58 Read 1203 rows and found 38 numeric columns
02:31:58 Using Annoy for neighbor search, n_neighbors = 66
02:31:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:31:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872eaac5df
02:31:58 Searching Annoy index using 1 thread, search_k = 6600
02:31:58 Annoy recall = 100%
02:32:03 Commencing smooth kNN distance calibration using 1 thread
02:32:12 Initializing from normalized Laplacian + noise
02:32:12 Commencing optimization for 500 epochs, with 95290 positive edges
02:32:20 Optimization finished
[1] "66 0.18"02:32:20 UMAP embedding parameters a = 1.321 b = 0.9813
02:32:20 Read 1203 rows and found 38 numeric columns
02:32:20 Using Annoy for neighbor search, n_neighbors = 66
02:32:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:32:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bbd74e6
02:32:21 Searching Annoy index using 1 thread, search_k = 6600
02:32:21 Annoy recall = 100%
02:32:26 Commencing smooth kNN distance calibration using 1 thread
02:32:35 Initializing from normalized Laplacian + noise
02:32:35 Commencing optimization for 500 epochs, with 95290 positive edges
02:32:43 Optimization finished
[1] "66 0.19"02:32:43 UMAP embedding parameters a = 1.292 b = 0.9921
02:32:43 Read 1203 rows and found 38 numeric columns
02:32:43 Using Annoy for neighbor search, n_neighbors = 66
02:32:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:32:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fce8923
02:32:44 Searching Annoy index using 1 thread, search_k = 6600
02:32:44 Annoy recall = 100%
02:32:49 Commencing smooth kNN distance calibration using 1 thread
02:32:58 Initializing from normalized Laplacian + noise
02:32:58 Commencing optimization for 500 epochs, with 95290 positive edges
02:33:06 Optimization finished
[1] "66 0.2"02:33:06 UMAP embedding parameters a = 1.262 b = 1.003
02:33:06 Read 1203 rows and found 38 numeric columns
02:33:06 Using Annoy for neighbor search, n_neighbors = 66
02:33:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:33:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c86770f
02:33:06 Searching Annoy index using 1 thread, search_k = 6600
02:33:07 Annoy recall = 100%
02:33:12 Commencing smooth kNN distance calibration using 1 thread
02:33:21 Initializing from normalized Laplacian + noise
02:33:21 Commencing optimization for 500 epochs, with 95290 positive edges
02:33:29 Optimization finished
[1] "67 0"02:33:29 UMAP embedding parameters a = 1.933 b = 0.7905
02:33:29 Read 1203 rows and found 38 numeric columns
02:33:29 Using Annoy for neighbor search, n_neighbors = 67
02:33:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:33:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873900a1da
02:33:29 Searching Annoy index using 1 thread, search_k = 6700
02:33:30 Annoy recall = 100%
02:33:34 Commencing smooth kNN distance calibration using 1 thread
02:33:43 Initializing from normalized Laplacian + noise
02:33:44 Commencing optimization for 500 epochs, with 96630 positive edges
02:33:52 Optimization finished
[1] "67 0.01"02:33:52 UMAP embedding parameters a = 1.896 b = 0.8006
02:33:52 Read 1203 rows and found 38 numeric columns
02:33:52 Using Annoy for neighbor search, n_neighbors = 67
02:33:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:33:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757dc58f3
02:33:52 Searching Annoy index using 1 thread, search_k = 6700
02:33:53 Annoy recall = 100%
02:33:57 Commencing smooth kNN distance calibration using 1 thread
02:34:06 Initializing from normalized Laplacian + noise
02:34:07 Commencing optimization for 500 epochs, with 96630 positive edges
02:34:14 Optimization finished
[1] "67 0.02"02:34:15 UMAP embedding parameters a = 1.859 b = 0.8109
02:34:15 Read 1203 rows and found 38 numeric columns
02:34:15 Using Annoy for neighbor search, n_neighbors = 67
02:34:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:34:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a85cb0
02:34:15 Searching Annoy index using 1 thread, search_k = 6700
02:34:16 Annoy recall = 100%
02:34:20 Commencing smooth kNN distance calibration using 1 thread
02:34:29 Initializing from normalized Laplacian + noise
02:34:30 Commencing optimization for 500 epochs, with 96630 positive edges
02:34:37 Optimization finished
[1] "67 0.03"02:34:38 UMAP embedding parameters a = 1.822 b = 0.8212
02:34:38 Read 1203 rows and found 38 numeric columns
02:34:38 Using Annoy for neighbor search, n_neighbors = 67
02:34:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:34:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762bd263b
02:34:38 Searching Annoy index using 1 thread, search_k = 6700
02:34:39 Annoy recall = 100%
02:34:43 Commencing smooth kNN distance calibration using 1 thread
02:34:52 Initializing from normalized Laplacian + noise
02:34:52 Commencing optimization for 500 epochs, with 96630 positive edges
02:35:00 Optimization finished
[1] "67 0.04"02:35:00 UMAP embedding parameters a = 1.786 b = 0.8316
02:35:00 Read 1203 rows and found 38 numeric columns
02:35:00 Using Annoy for neighbor search, n_neighbors = 67
02:35:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:35:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87138c35d8
02:35:01 Searching Annoy index using 1 thread, search_k = 6700
02:35:01 Annoy recall = 100%
02:35:06 Commencing smooth kNN distance calibration using 1 thread
02:35:15 Initializing from normalized Laplacian + noise
02:35:15 Commencing optimization for 500 epochs, with 96630 positive edges
02:35:23 Optimization finished
[1] "67 0.05"02:35:24 UMAP embedding parameters a = 1.75 b = 0.8421
02:35:24 Read 1203 rows and found 38 numeric columns
02:35:24 Using Annoy for neighbor search, n_neighbors = 67
02:35:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:35:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877abd0ea4
02:35:24 Searching Annoy index using 1 thread, search_k = 6700
02:35:24 Annoy recall = 100%
02:35:29 Commencing smooth kNN distance calibration using 1 thread
02:35:38 Initializing from normalized Laplacian + noise
02:35:38 Commencing optimization for 500 epochs, with 96630 positive edges
02:35:46 Optimization finished
[1] "67 0.06"02:35:46 UMAP embedding parameters a = 1.715 b = 0.8526
02:35:46 Read 1203 rows and found 38 numeric columns
02:35:46 Using Annoy for neighbor search, n_neighbors = 67
02:35:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:35:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87152ed46e
02:35:47 Searching Annoy index using 1 thread, search_k = 6700
02:35:47 Annoy recall = 100%
02:35:52 Commencing smooth kNN distance calibration using 1 thread
02:36:01 Initializing from normalized Laplacian + noise
02:36:01 Commencing optimization for 500 epochs, with 96630 positive edges
02:36:09 Optimization finished
[1] "67 0.07"02:36:09 UMAP embedding parameters a = 1.68 b = 0.8631
02:36:09 Read 1203 rows and found 38 numeric columns
02:36:09 Using Annoy for neighbor search, n_neighbors = 67
02:36:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:36:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715955050
02:36:10 Searching Annoy index using 1 thread, search_k = 6700
02:36:10 Annoy recall = 100%
02:36:15 Commencing smooth kNN distance calibration using 1 thread
02:36:24 Initializing from normalized Laplacian + noise
02:36:24 Commencing optimization for 500 epochs, with 96630 positive edges
02:36:32 Optimization finished
[1] "67 0.08"02:36:32 UMAP embedding parameters a = 1.645 b = 0.8737
02:36:32 Read 1203 rows and found 38 numeric columns
02:36:32 Using Annoy for neighbor search, n_neighbors = 67
02:36:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:36:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d4a3420
02:36:33 Searching Annoy index using 1 thread, search_k = 6700
02:36:33 Annoy recall = 100%
02:36:38 Commencing smooth kNN distance calibration using 1 thread
02:36:47 Initializing from normalized Laplacian + noise
02:36:47 Commencing optimization for 500 epochs, with 96630 positive edges
02:36:55 Optimization finished
[1] "67 0.09"02:36:55 UMAP embedding parameters a = 1.611 b = 0.8844
02:36:55 Read 1203 rows and found 38 numeric columns
02:36:55 Using Annoy for neighbor search, n_neighbors = 67
02:36:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:36:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874be06fad
02:36:56 Searching Annoy index using 1 thread, search_k = 6700
02:36:56 Annoy recall = 100%
02:37:01 Commencing smooth kNN distance calibration using 1 thread
02:37:10 Initializing from normalized Laplacian + noise
02:37:10 Commencing optimization for 500 epochs, with 96630 positive edges
02:37:18 Optimization finished
[1] "67 0.1"02:37:18 UMAP embedding parameters a = 1.577 b = 0.8951
02:37:18 Read 1203 rows and found 38 numeric columns
02:37:18 Using Annoy for neighbor search, n_neighbors = 67
02:37:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:37:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871864277c
02:37:19 Searching Annoy index using 1 thread, search_k = 6700
02:37:19 Annoy recall = 100%
02:37:24 Commencing smooth kNN distance calibration using 1 thread
02:37:33 Initializing from normalized Laplacian + noise
02:37:33 Commencing optimization for 500 epochs, with 96630 positive edges
02:37:41 Optimization finished
[1] "67 0.11"02:37:41 UMAP embedding parameters a = 1.544 b = 0.9058
02:37:41 Read 1203 rows and found 38 numeric columns
02:37:41 Using Annoy for neighbor search, n_neighbors = 67
02:37:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:37:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876926f30e
02:37:42 Searching Annoy index using 1 thread, search_k = 6700
02:37:42 Annoy recall = 100%
02:37:47 Commencing smooth kNN distance calibration using 1 thread
02:37:56 Initializing from normalized Laplacian + noise
02:37:56 Commencing optimization for 500 epochs, with 96630 positive edges
02:38:04 Optimization finished
[1] "67 0.12"02:38:04 UMAP embedding parameters a = 1.51 b = 0.9165
02:38:04 Read 1203 rows and found 38 numeric columns
02:38:04 Using Annoy for neighbor search, n_neighbors = 67
02:38:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:38:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a234645
02:38:05 Searching Annoy index using 1 thread, search_k = 6700
02:38:05 Annoy recall = 100%
02:38:10 Commencing smooth kNN distance calibration using 1 thread
02:38:19 Initializing from normalized Laplacian + noise
02:38:19 Commencing optimization for 500 epochs, with 96630 positive edges
02:38:27 Optimization finished
[1] "67 0.13"02:38:28 UMAP embedding parameters a = 1.478 b = 0.9272
02:38:28 Read 1203 rows and found 38 numeric columns
02:38:28 Using Annoy for neighbor search, n_neighbors = 67
02:38:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:38:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c9c64f4
02:38:28 Searching Annoy index using 1 thread, search_k = 6700
02:38:29 Annoy recall = 100%
02:38:33 Commencing smooth kNN distance calibration using 1 thread
02:38:42 Initializing from normalized Laplacian + noise
02:38:42 Commencing optimization for 500 epochs, with 96630 positive edges
02:38:50 Optimization finished
[1] "67 0.14"02:38:51 UMAP embedding parameters a = 1.446 b = 0.938
02:38:51 Read 1203 rows and found 38 numeric columns
02:38:51 Using Annoy for neighbor search, n_neighbors = 67
02:38:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:38:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777fd7e43
02:38:51 Searching Annoy index using 1 thread, search_k = 6700
02:38:51 Annoy recall = 100%
02:38:56 Commencing smooth kNN distance calibration using 1 thread
02:39:05 Initializing from normalized Laplacian + noise
02:39:05 Commencing optimization for 500 epochs, with 96630 positive edges
02:39:13 Optimization finished
[1] "67 0.15"02:39:14 UMAP embedding parameters a = 1.414 b = 0.9488
02:39:14 Read 1203 rows and found 38 numeric columns
02:39:14 Using Annoy for neighbor search, n_neighbors = 67
02:39:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:39:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b96c40
02:39:14 Searching Annoy index using 1 thread, search_k = 6700
02:39:15 Annoy recall = 100%
02:39:19 Commencing smooth kNN distance calibration using 1 thread
02:39:29 Initializing from normalized Laplacian + noise
02:39:29 Commencing optimization for 500 epochs, with 96630 positive edges
02:39:37 Optimization finished
[1] "67 0.16"02:39:37 UMAP embedding parameters a = 1.383 b = 0.9596
02:39:37 Read 1203 rows and found 38 numeric columns
02:39:37 Using Annoy for neighbor search, n_neighbors = 67
02:39:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:39:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767a0a85
02:39:37 Searching Annoy index using 1 thread, search_k = 6700
02:39:38 Annoy recall = 100%
02:39:42 Commencing smooth kNN distance calibration using 1 thread
02:39:52 Initializing from normalized Laplacian + noise
02:39:52 Commencing optimization for 500 epochs, with 96630 positive edges
02:40:00 Optimization finished
[1] "67 0.17"02:40:00 UMAP embedding parameters a = 1.352 b = 0.9704
02:40:00 Read 1203 rows and found 38 numeric columns
02:40:00 Using Annoy for neighbor search, n_neighbors = 67
02:40:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:40:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ea7fb5a
02:40:00 Searching Annoy index using 1 thread, search_k = 6700
02:40:01 Annoy recall = 100%
02:40:05 Commencing smooth kNN distance calibration using 1 thread
02:40:15 Initializing from normalized Laplacian + noise
02:40:15 Commencing optimization for 500 epochs, with 96630 positive edges
02:40:23 Optimization finished
[1] "67 0.18"02:40:23 UMAP embedding parameters a = 1.321 b = 0.9813
02:40:23 Read 1203 rows and found 38 numeric columns
02:40:23 Using Annoy for neighbor search, n_neighbors = 67
02:40:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:40:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b3eae95
02:40:23 Searching Annoy index using 1 thread, search_k = 6700
02:40:24 Annoy recall = 100%
02:40:29 Commencing smooth kNN distance calibration using 1 thread
02:40:38 Initializing from normalized Laplacian + noise
02:40:38 Commencing optimization for 500 epochs, with 96630 positive edges
02:40:46 Optimization finished
[1] "67 0.19"02:40:46 UMAP embedding parameters a = 1.292 b = 0.9921
02:40:46 Read 1203 rows and found 38 numeric columns
02:40:46 Using Annoy for neighbor search, n_neighbors = 67
02:40:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:40:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cc023
02:40:46 Searching Annoy index using 1 thread, search_k = 6700
02:40:47 Annoy recall = 100%
02:40:52 Commencing smooth kNN distance calibration using 1 thread
02:41:01 Initializing from normalized Laplacian + noise
02:41:01 Commencing optimization for 500 epochs, with 96630 positive edges
02:41:09 Optimization finished
[1] "67 0.2"02:41:09 UMAP embedding parameters a = 1.262 b = 1.003
02:41:09 Read 1203 rows and found 38 numeric columns
02:41:09 Using Annoy for neighbor search, n_neighbors = 67
02:41:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:41:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730ba2fbb
02:41:10 Searching Annoy index using 1 thread, search_k = 6700
02:41:10 Annoy recall = 100%
02:41:15 Commencing smooth kNN distance calibration using 1 thread
02:41:24 Initializing from normalized Laplacian + noise
02:41:24 Commencing optimization for 500 epochs, with 96630 positive edges
02:41:32 Optimization finished
[1] "68 0"02:41:32 UMAP embedding parameters a = 1.933 b = 0.7905
02:41:32 Read 1203 rows and found 38 numeric columns
02:41:32 Using Annoy for neighbor search, n_neighbors = 68
02:41:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:41:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778634cbf
02:41:33 Searching Annoy index using 1 thread, search_k = 6800
02:41:33 Annoy recall = 100%
02:41:38 Commencing smooth kNN distance calibration using 1 thread
02:41:47 Initializing from normalized Laplacian + noise
02:41:47 Commencing optimization for 500 epochs, with 97966 positive edges
02:41:55 Optimization finished
[1] "68 0.01"02:41:56 UMAP embedding parameters a = 1.896 b = 0.8006
02:41:56 Read 1203 rows and found 38 numeric columns
02:41:56 Using Annoy for neighbor search, n_neighbors = 68
02:41:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:41:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a078584
02:41:56 Searching Annoy index using 1 thread, search_k = 6800
02:41:56 Annoy recall = 100%
02:42:01 Commencing smooth kNN distance calibration using 1 thread
02:42:10 Initializing from normalized Laplacian + noise
02:42:10 Commencing optimization for 500 epochs, with 97966 positive edges
02:42:18 Optimization finished
[1] "68 0.02"02:42:19 UMAP embedding parameters a = 1.859 b = 0.8109
02:42:19 Read 1203 rows and found 38 numeric columns
02:42:19 Using Annoy for neighbor search, n_neighbors = 68
02:42:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:42:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731b8483d
02:42:19 Searching Annoy index using 1 thread, search_k = 6800
02:42:20 Annoy recall = 100%
02:42:24 Commencing smooth kNN distance calibration using 1 thread
02:42:34 Initializing from normalized Laplacian + noise
02:42:34 Commencing optimization for 500 epochs, with 97966 positive edges
02:42:42 Optimization finished
[1] "68 0.03"02:42:42 UMAP embedding parameters a = 1.822 b = 0.8212
02:42:42 Read 1203 rows and found 38 numeric columns
02:42:42 Using Annoy for neighbor search, n_neighbors = 68
02:42:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:42:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765f3e39d
02:42:42 Searching Annoy index using 1 thread, search_k = 6800
02:42:43 Annoy recall = 100%
02:42:48 Commencing smooth kNN distance calibration using 1 thread
02:42:57 Initializing from normalized Laplacian + noise
02:42:57 Commencing optimization for 500 epochs, with 97966 positive edges
02:43:06 Optimization finished
[1] "68 0.04"02:43:06 UMAP embedding parameters a = 1.786 b = 0.8316
02:43:06 Read 1203 rows and found 38 numeric columns
02:43:06 Using Annoy for neighbor search, n_neighbors = 68
02:43:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:43:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876659faed
02:43:06 Searching Annoy index using 1 thread, search_k = 6800
02:43:07 Annoy recall = 100%
02:43:11 Commencing smooth kNN distance calibration using 1 thread
02:43:21 Initializing from normalized Laplacian + noise
02:43:21 Commencing optimization for 500 epochs, with 97966 positive edges
02:43:29 Optimization finished
[1] "68 0.05"02:43:30 UMAP embedding parameters a = 1.75 b = 0.8421
02:43:30 Read 1203 rows and found 38 numeric columns
02:43:30 Using Annoy for neighbor search, n_neighbors = 68
02:43:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:43:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c517667
02:43:30 Searching Annoy index using 1 thread, search_k = 6800
02:43:31 Annoy recall = 100%
02:43:35 Commencing smooth kNN distance calibration using 1 thread
02:43:45 Initializing from normalized Laplacian + noise
02:43:45 Commencing optimization for 500 epochs, with 97966 positive edges
02:43:53 Optimization finished
[1] "68 0.06"02:43:53 UMAP embedding parameters a = 1.715 b = 0.8526
02:43:53 Read 1203 rows and found 38 numeric columns
02:43:53 Using Annoy for neighbor search, n_neighbors = 68
02:43:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:43:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87149ea97c
02:43:54 Searching Annoy index using 1 thread, search_k = 6800
02:43:54 Annoy recall = 100%
02:43:59 Commencing smooth kNN distance calibration using 1 thread
02:44:09 Initializing from normalized Laplacian + noise
02:44:09 Commencing optimization for 500 epochs, with 97966 positive edges
02:44:17 Optimization finished
[1] "68 0.07"02:44:17 UMAP embedding parameters a = 1.68 b = 0.8631
02:44:17 Read 1203 rows and found 38 numeric columns
02:44:17 Using Annoy for neighbor search, n_neighbors = 68
02:44:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:44:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752176fd3
02:44:18 Searching Annoy index using 1 thread, search_k = 6800
02:44:18 Annoy recall = 100%
02:44:23 Commencing smooth kNN distance calibration using 1 thread
02:44:33 Initializing from normalized Laplacian + noise
02:44:33 Commencing optimization for 500 epochs, with 97966 positive edges
02:44:41 Optimization finished
[1] "68 0.08"02:44:41 UMAP embedding parameters a = 1.645 b = 0.8737
02:44:41 Read 1203 rows and found 38 numeric columns
02:44:41 Using Annoy for neighbor search, n_neighbors = 68
02:44:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:44:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c1fff8a
02:44:42 Searching Annoy index using 1 thread, search_k = 6800
02:44:42 Annoy recall = 100%
02:44:47 Commencing smooth kNN distance calibration using 1 thread
02:44:57 Initializing from normalized Laplacian + noise
02:44:57 Commencing optimization for 500 epochs, with 97966 positive edges
02:45:05 Optimization finished
[1] "68 0.09"02:45:05 UMAP embedding parameters a = 1.611 b = 0.8844
02:45:05 Read 1203 rows and found 38 numeric columns
02:45:05 Using Annoy for neighbor search, n_neighbors = 68
02:45:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:45:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876125208b
02:45:05 Searching Annoy index using 1 thread, search_k = 6800
02:45:06 Annoy recall = 100%
02:45:11 Commencing smooth kNN distance calibration using 1 thread
02:45:20 Initializing from normalized Laplacian + noise
02:45:21 Commencing optimization for 500 epochs, with 97966 positive edges
02:45:29 Optimization finished
[1] "68 0.1"02:45:29 UMAP embedding parameters a = 1.577 b = 0.8951
02:45:29 Read 1203 rows and found 38 numeric columns
02:45:29 Using Annoy for neighbor search, n_neighbors = 68
02:45:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:45:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b1811ad
02:45:29 Searching Annoy index using 1 thread, search_k = 6800
02:45:30 Annoy recall = 100%
02:45:35 Commencing smooth kNN distance calibration using 1 thread
02:45:45 Initializing from normalized Laplacian + noise
02:45:45 Commencing optimization for 500 epochs, with 97966 positive edges
02:45:53 Optimization finished
[1] "68 0.11"02:45:53 UMAP embedding parameters a = 1.544 b = 0.9058
02:45:53 Read 1203 rows and found 38 numeric columns
02:45:53 Using Annoy for neighbor search, n_neighbors = 68
02:45:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:45:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fc587e
02:45:53 Searching Annoy index using 1 thread, search_k = 6800
02:45:54 Annoy recall = 100%
02:45:59 Commencing smooth kNN distance calibration using 1 thread
02:46:08 Initializing from normalized Laplacian + noise
02:46:08 Commencing optimization for 500 epochs, with 97966 positive edges
02:46:17 Optimization finished
[1] "68 0.12"02:46:17 UMAP embedding parameters a = 1.51 b = 0.9165
02:46:17 Read 1203 rows and found 38 numeric columns
02:46:17 Using Annoy for neighbor search, n_neighbors = 68
02:46:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:46:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767cd7d3b
02:46:17 Searching Annoy index using 1 thread, search_k = 6800
02:46:18 Annoy recall = 100%
02:46:23 Commencing smooth kNN distance calibration using 1 thread
02:46:32 Initializing from normalized Laplacian + noise
02:46:32 Commencing optimization for 500 epochs, with 97966 positive edges
02:46:41 Optimization finished
[1] "68 0.13"02:46:41 UMAP embedding parameters a = 1.478 b = 0.9272
02:46:41 Read 1203 rows and found 38 numeric columns
02:46:41 Using Annoy for neighbor search, n_neighbors = 68
02:46:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:46:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876dd537e9
02:46:41 Searching Annoy index using 1 thread, search_k = 6800
02:46:42 Annoy recall = 100%
02:46:47 Commencing smooth kNN distance calibration using 1 thread
02:46:56 Initializing from normalized Laplacian + noise
02:46:56 Commencing optimization for 500 epochs, with 97966 positive edges
02:47:04 Optimization finished
[1] "68 0.14"02:47:05 UMAP embedding parameters a = 1.446 b = 0.938
02:47:05 Read 1203 rows and found 38 numeric columns
02:47:05 Using Annoy for neighbor search, n_neighbors = 68
02:47:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:47:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717888e56
02:47:05 Searching Annoy index using 1 thread, search_k = 6800
02:47:06 Annoy recall = 100%
02:47:10 Commencing smooth kNN distance calibration using 1 thread
02:47:20 Initializing from normalized Laplacian + noise
02:47:20 Commencing optimization for 500 epochs, with 97966 positive edges
02:47:28 Optimization finished
[1] "68 0.15"02:47:29 UMAP embedding parameters a = 1.414 b = 0.9488
02:47:29 Read 1203 rows and found 38 numeric columns
02:47:29 Using Annoy for neighbor search, n_neighbors = 68
02:47:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:47:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87628a8bdf
02:47:29 Searching Annoy index using 1 thread, search_k = 6800
02:47:30 Annoy recall = 100%
02:47:34 Commencing smooth kNN distance calibration using 1 thread
02:47:44 Initializing from normalized Laplacian + noise
02:47:44 Commencing optimization for 500 epochs, with 97966 positive edges
02:47:53 Optimization finished
[1] "68 0.16"02:47:53 UMAP embedding parameters a = 1.383 b = 0.9596
02:47:53 Read 1203 rows and found 38 numeric columns
02:47:53 Using Annoy for neighbor search, n_neighbors = 68
02:47:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:47:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873040c57
02:47:53 Searching Annoy index using 1 thread, search_k = 6800
02:47:54 Annoy recall = 100%
02:47:59 Commencing smooth kNN distance calibration using 1 thread
02:48:08 Initializing from normalized Laplacian + noise
02:48:08 Commencing optimization for 500 epochs, with 97966 positive edges
02:48:16 Optimization finished
[1] "68 0.17"02:48:17 UMAP embedding parameters a = 1.352 b = 0.9704
02:48:17 Read 1203 rows and found 38 numeric columns
02:48:17 Using Annoy for neighbor search, n_neighbors = 68
02:48:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:48:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d1ddea6
02:48:17 Searching Annoy index using 1 thread, search_k = 6800
02:48:18 Annoy recall = 100%
02:48:22 Commencing smooth kNN distance calibration using 1 thread
02:48:32 Initializing from normalized Laplacian + noise
02:48:32 Commencing optimization for 500 epochs, with 97966 positive edges
02:48:41 Optimization finished
[1] "68 0.18"02:48:41 UMAP embedding parameters a = 1.321 b = 0.9813
02:48:41 Read 1203 rows and found 38 numeric columns
02:48:41 Using Annoy for neighbor search, n_neighbors = 68
02:48:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:48:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fd4bfff
02:48:41 Searching Annoy index using 1 thread, search_k = 6800
02:48:42 Annoy recall = 100%
02:48:47 Commencing smooth kNN distance calibration using 1 thread
02:48:56 Initializing from normalized Laplacian + noise
02:48:56 Commencing optimization for 500 epochs, with 97966 positive edges
02:49:05 Optimization finished
[1] "68 0.19"02:49:05 UMAP embedding parameters a = 1.292 b = 0.9921
02:49:05 Read 1203 rows and found 38 numeric columns
02:49:05 Using Annoy for neighbor search, n_neighbors = 68
02:49:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:49:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ee47c04
02:49:05 Searching Annoy index using 1 thread, search_k = 6800
02:49:06 Annoy recall = 100%
02:49:11 Commencing smooth kNN distance calibration using 1 thread
02:49:20 Initializing from normalized Laplacian + noise
02:49:20 Commencing optimization for 500 epochs, with 97966 positive edges
02:49:29 Optimization finished
[1] "68 0.2"02:49:29 UMAP embedding parameters a = 1.262 b = 1.003
02:49:29 Read 1203 rows and found 38 numeric columns
02:49:29 Using Annoy for neighbor search, n_neighbors = 68
02:49:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:49:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745820622
02:49:29 Searching Annoy index using 1 thread, search_k = 6800
02:49:30 Annoy recall = 100%
02:49:35 Commencing smooth kNN distance calibration using 1 thread
02:49:44 Initializing from normalized Laplacian + noise
02:49:44 Commencing optimization for 500 epochs, with 97966 positive edges
02:49:53 Optimization finished
[1] "69 0"02:49:53 UMAP embedding parameters a = 1.933 b = 0.7905
02:49:53 Read 1203 rows and found 38 numeric columns
02:49:53 Using Annoy for neighbor search, n_neighbors = 69
02:49:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:49:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778fbb30d
02:49:53 Searching Annoy index using 1 thread, search_k = 6900
02:49:54 Annoy recall = 100%
02:49:59 Commencing smooth kNN distance calibration using 1 thread
02:50:08 Initializing from normalized Laplacian + noise
02:50:08 Commencing optimization for 500 epochs, with 99298 positive edges
02:50:17 Optimization finished
[1] "69 0.01"02:50:17 UMAP embedding parameters a = 1.896 b = 0.8006
02:50:17 Read 1203 rows and found 38 numeric columns
02:50:17 Using Annoy for neighbor search, n_neighbors = 69
02:50:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:50:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876907c249
02:50:17 Searching Annoy index using 1 thread, search_k = 6900
02:50:18 Annoy recall = 100%
02:50:23 Commencing smooth kNN distance calibration using 1 thread
02:50:33 Initializing from normalized Laplacian + noise
02:50:33 Commencing optimization for 500 epochs, with 99298 positive edges
02:50:41 Optimization finished
[1] "69 0.02"02:50:41 UMAP embedding parameters a = 1.859 b = 0.8109
02:50:41 Read 1203 rows and found 38 numeric columns
02:50:41 Using Annoy for neighbor search, n_neighbors = 69
02:50:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:50:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721e6b17
02:50:41 Searching Annoy index using 1 thread, search_k = 6900
02:50:42 Annoy recall = 100%
02:50:47 Commencing smooth kNN distance calibration using 1 thread
02:50:57 Initializing from normalized Laplacian + noise
02:50:57 Commencing optimization for 500 epochs, with 99298 positive edges
02:51:05 Optimization finished
[1] "69 0.03"02:51:05 UMAP embedding parameters a = 1.822 b = 0.8212
02:51:05 Read 1203 rows and found 38 numeric columns
02:51:05 Using Annoy for neighbor search, n_neighbors = 69
02:51:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:51:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770f93150
02:51:06 Searching Annoy index using 1 thread, search_k = 6900
02:51:06 Annoy recall = 100%
02:51:11 Commencing smooth kNN distance calibration using 1 thread
02:51:21 Initializing from normalized Laplacian + noise
02:51:21 Commencing optimization for 500 epochs, with 99298 positive edges
02:51:29 Optimization finished
[1] "69 0.04"02:51:29 UMAP embedding parameters a = 1.786 b = 0.8316
02:51:29 Read 1203 rows and found 38 numeric columns
02:51:29 Using Annoy for neighbor search, n_neighbors = 69
02:51:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:51:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770c12e89
02:51:30 Searching Annoy index using 1 thread, search_k = 6900
02:51:30 Annoy recall = 100%
02:51:35 Commencing smooth kNN distance calibration using 1 thread
02:51:45 Initializing from normalized Laplacian + noise
02:51:45 Commencing optimization for 500 epochs, with 99298 positive edges
02:51:53 Optimization finished
[1] "69 0.05"02:51:53 UMAP embedding parameters a = 1.75 b = 0.8421
02:51:53 Read 1203 rows and found 38 numeric columns
02:51:53 Using Annoy for neighbor search, n_neighbors = 69
02:51:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:51:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87898759c
02:51:54 Searching Annoy index using 1 thread, search_k = 6900
02:51:54 Annoy recall = 100%
02:51:59 Commencing smooth kNN distance calibration using 1 thread
02:52:09 Initializing from normalized Laplacian + noise
02:52:09 Commencing optimization for 500 epochs, with 99298 positive edges
02:52:17 Optimization finished
[1] "69 0.06"02:52:17 UMAP embedding parameters a = 1.715 b = 0.8526
02:52:17 Read 1203 rows and found 38 numeric columns
02:52:17 Using Annoy for neighbor search, n_neighbors = 69
02:52:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:52:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876fa12caa
02:52:18 Searching Annoy index using 1 thread, search_k = 6900
02:52:18 Annoy recall = 100%
02:52:23 Commencing smooth kNN distance calibration using 1 thread
02:52:33 Initializing from normalized Laplacian + noise
02:52:33 Commencing optimization for 500 epochs, with 99298 positive edges
02:52:41 Optimization finished
[1] "69 0.07"02:52:41 UMAP embedding parameters a = 1.68 b = 0.8631
02:52:41 Read 1203 rows and found 38 numeric columns
02:52:41 Using Annoy for neighbor search, n_neighbors = 69
02:52:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:52:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871bffdd1f
02:52:42 Searching Annoy index using 1 thread, search_k = 6900
02:52:42 Annoy recall = 100%
02:52:47 Commencing smooth kNN distance calibration using 1 thread
02:52:57 Initializing from normalized Laplacian + noise
02:52:57 Commencing optimization for 500 epochs, with 99298 positive edges
02:53:05 Optimization finished
[1] "69 0.08"02:53:05 UMAP embedding parameters a = 1.645 b = 0.8737
02:53:05 Read 1203 rows and found 38 numeric columns
02:53:05 Using Annoy for neighbor search, n_neighbors = 69
02:53:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:53:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878a535bf
02:53:06 Searching Annoy index using 1 thread, search_k = 6900
02:53:06 Annoy recall = 100%
02:53:11 Commencing smooth kNN distance calibration using 1 thread
02:53:20 Initializing from normalized Laplacian + noise
02:53:21 Commencing optimization for 500 epochs, with 99298 positive edges
02:53:29 Optimization finished
[1] "69 0.09"02:53:29 UMAP embedding parameters a = 1.611 b = 0.8844
02:53:29 Read 1203 rows and found 38 numeric columns
02:53:29 Using Annoy for neighbor search, n_neighbors = 69
02:53:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:53:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87205b5c66
02:53:29 Searching Annoy index using 1 thread, search_k = 6900
02:53:30 Annoy recall = 100%
02:53:35 Commencing smooth kNN distance calibration using 1 thread
02:53:45 Initializing from normalized Laplacian + noise
02:53:45 Commencing optimization for 500 epochs, with 99298 positive edges
02:53:53 Optimization finished
[1] "69 0.1"02:53:53 UMAP embedding parameters a = 1.577 b = 0.8951
02:53:53 Read 1203 rows and found 38 numeric columns
02:53:53 Using Annoy for neighbor search, n_neighbors = 69
02:53:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:53:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87146329de
02:53:53 Searching Annoy index using 1 thread, search_k = 6900
02:53:54 Annoy recall = 100%
02:53:59 Commencing smooth kNN distance calibration using 1 thread
02:54:09 Initializing from normalized Laplacian + noise
02:54:09 Commencing optimization for 500 epochs, with 99298 positive edges
02:54:17 Optimization finished
[1] "69 0.11"02:54:17 UMAP embedding parameters a = 1.544 b = 0.9058
02:54:17 Read 1203 rows and found 38 numeric columns
02:54:17 Using Annoy for neighbor search, n_neighbors = 69
02:54:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:54:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752acbb44
02:54:17 Searching Annoy index using 1 thread, search_k = 6900
02:54:18 Annoy recall = 100%
02:54:23 Commencing smooth kNN distance calibration using 1 thread
02:54:32 Initializing from normalized Laplacian + noise
02:54:32 Commencing optimization for 500 epochs, with 99298 positive edges
02:54:41 Optimization finished
[1] "69 0.12"02:54:41 UMAP embedding parameters a = 1.51 b = 0.9165
02:54:41 Read 1203 rows and found 38 numeric columns
02:54:41 Using Annoy for neighbor search, n_neighbors = 69
02:54:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:54:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875213a4a3
02:54:41 Searching Annoy index using 1 thread, search_k = 6900
02:54:42 Annoy recall = 100%
02:54:47 Commencing smooth kNN distance calibration using 1 thread
02:54:57 Initializing from normalized Laplacian + noise
02:54:57 Commencing optimization for 500 epochs, with 99298 positive edges
02:55:05 Optimization finished
[1] "69 0.13"02:55:05 UMAP embedding parameters a = 1.478 b = 0.9272
02:55:05 Read 1203 rows and found 38 numeric columns
02:55:05 Using Annoy for neighbor search, n_neighbors = 69
02:55:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:55:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a570d7b
02:55:05 Searching Annoy index using 1 thread, search_k = 6900
02:55:06 Annoy recall = 100%
02:55:11 Commencing smooth kNN distance calibration using 1 thread
02:55:20 Initializing from normalized Laplacian + noise
02:55:21 Commencing optimization for 500 epochs, with 99298 positive edges
02:55:29 Optimization finished
[1] "69 0.14"02:55:29 UMAP embedding parameters a = 1.446 b = 0.938
02:55:29 Read 1203 rows and found 38 numeric columns
02:55:29 Using Annoy for neighbor search, n_neighbors = 69
02:55:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:55:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873906b631
02:55:29 Searching Annoy index using 1 thread, search_k = 6900
02:55:30 Annoy recall = 100%
02:55:35 Commencing smooth kNN distance calibration using 1 thread
02:55:44 Initializing from normalized Laplacian + noise
02:55:45 Commencing optimization for 500 epochs, with 99298 positive edges
02:55:53 Optimization finished
[1] "69 0.15"02:55:53 UMAP embedding parameters a = 1.414 b = 0.9488
02:55:53 Read 1203 rows and found 38 numeric columns
02:55:53 Using Annoy for neighbor search, n_neighbors = 69
02:55:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:55:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e651b0a
02:55:53 Searching Annoy index using 1 thread, search_k = 6900
02:55:54 Annoy recall = 100%
02:55:59 Commencing smooth kNN distance calibration using 1 thread
02:56:09 Initializing from normalized Laplacian + noise
02:56:09 Commencing optimization for 500 epochs, with 99298 positive edges
02:56:17 Optimization finished
[1] "69 0.16"02:56:17 UMAP embedding parameters a = 1.383 b = 0.9596
02:56:17 Read 1203 rows and found 38 numeric columns
02:56:17 Using Annoy for neighbor search, n_neighbors = 69
02:56:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:56:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ef5b6f7
02:56:17 Searching Annoy index using 1 thread, search_k = 6900
02:56:18 Annoy recall = 100%
02:56:23 Commencing smooth kNN distance calibration using 1 thread
02:56:33 Initializing from normalized Laplacian + noise
02:56:33 Commencing optimization for 500 epochs, with 99298 positive edges
02:56:41 Optimization finished
[1] "69 0.17"02:56:41 UMAP embedding parameters a = 1.352 b = 0.9704
02:56:41 Read 1203 rows and found 38 numeric columns
02:56:41 Using Annoy for neighbor search, n_neighbors = 69
02:56:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:56:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b1e2605
02:56:41 Searching Annoy index using 1 thread, search_k = 6900
02:56:42 Annoy recall = 100%
02:56:47 Commencing smooth kNN distance calibration using 1 thread
02:56:57 Initializing from normalized Laplacian + noise
02:56:57 Commencing optimization for 500 epochs, with 99298 positive edges
02:57:05 Optimization finished
[1] "69 0.18"02:57:05 UMAP embedding parameters a = 1.321 b = 0.9813
02:57:05 Read 1203 rows and found 38 numeric columns
02:57:05 Using Annoy for neighbor search, n_neighbors = 69
02:57:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:57:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a851a95
02:57:06 Searching Annoy index using 1 thread, search_k = 6900
02:57:06 Annoy recall = 100%
02:57:11 Commencing smooth kNN distance calibration using 1 thread
02:57:21 Initializing from normalized Laplacian + noise
02:57:21 Commencing optimization for 500 epochs, with 99298 positive edges
02:57:29 Optimization finished
[1] "69 0.19"02:57:29 UMAP embedding parameters a = 1.292 b = 0.9921
02:57:29 Read 1203 rows and found 38 numeric columns
02:57:29 Using Annoy for neighbor search, n_neighbors = 69
02:57:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:57:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87701ad782
02:57:30 Searching Annoy index using 1 thread, search_k = 6900
02:57:30 Annoy recall = 100%
02:57:35 Commencing smooth kNN distance calibration using 1 thread
02:57:45 Initializing from normalized Laplacian + noise
02:57:45 Commencing optimization for 500 epochs, with 99298 positive edges
02:57:53 Optimization finished
[1] "69 0.2"02:57:53 UMAP embedding parameters a = 1.262 b = 1.003
02:57:53 Read 1203 rows and found 38 numeric columns
02:57:53 Using Annoy for neighbor search, n_neighbors = 69
02:57:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:57:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87163637b2
02:57:54 Searching Annoy index using 1 thread, search_k = 6900
02:57:54 Annoy recall = 100%
02:57:59 Commencing smooth kNN distance calibration using 1 thread
02:58:09 Initializing from normalized Laplacian + noise
02:58:09 Commencing optimization for 500 epochs, with 99298 positive edges
02:58:17 Optimization finished
[1] "70 0"02:58:18 UMAP embedding parameters a = 1.933 b = 0.7905
02:58:18 Read 1203 rows and found 38 numeric columns
02:58:18 Using Annoy for neighbor search, n_neighbors = 70
02:58:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:58:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e817313
02:58:18 Searching Annoy index using 1 thread, search_k = 7000
02:58:19 Annoy recall = 100%
02:58:23 Commencing smooth kNN distance calibration using 1 thread
02:58:33 Initializing from normalized Laplacian + noise
02:58:33 Commencing optimization for 500 epochs, with 100670 positive edges
02:58:41 Optimization finished
[1] "70 0.01"02:58:42 UMAP embedding parameters a = 1.896 b = 0.8006
02:58:42 Read 1203 rows and found 38 numeric columns
02:58:42 Using Annoy for neighbor search, n_neighbors = 70
02:58:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:58:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757e854bd
02:58:42 Searching Annoy index using 1 thread, search_k = 7000
02:58:43 Annoy recall = 100%
02:58:47 Commencing smooth kNN distance calibration using 1 thread
02:58:57 Initializing from normalized Laplacian + noise
02:58:57 Commencing optimization for 500 epochs, with 100670 positive edges
02:59:06 Optimization finished
[1] "70 0.02"02:59:06 UMAP embedding parameters a = 1.859 b = 0.8109
02:59:06 Read 1203 rows and found 38 numeric columns
02:59:06 Using Annoy for neighbor search, n_neighbors = 70
02:59:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:59:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740b6f9b
02:59:06 Searching Annoy index using 1 thread, search_k = 7000
02:59:07 Annoy recall = 100%
02:59:12 Commencing smooth kNN distance calibration using 1 thread
02:59:22 Initializing from normalized Laplacian + noise
02:59:22 Commencing optimization for 500 epochs, with 100670 positive edges
02:59:30 Optimization finished
[1] "70 0.03"02:59:30 UMAP embedding parameters a = 1.822 b = 0.8212
02:59:30 Read 1203 rows and found 38 numeric columns
02:59:30 Using Annoy for neighbor search, n_neighbors = 70
02:59:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:59:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87160a0169
02:59:30 Searching Annoy index using 1 thread, search_k = 7000
02:59:31 Annoy recall = 100%
02:59:36 Commencing smooth kNN distance calibration using 1 thread
02:59:46 Initializing from normalized Laplacian + noise
02:59:46 Commencing optimization for 500 epochs, with 100670 positive edges
02:59:54 Optimization finished
[1] "70 0.04"02:59:54 UMAP embedding parameters a = 1.786 b = 0.8316
02:59:54 Read 1203 rows and found 38 numeric columns
02:59:54 Using Annoy for neighbor search, n_neighbors = 70
02:59:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:59:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a72e09c
02:59:55 Searching Annoy index using 1 thread, search_k = 7000
02:59:55 Annoy recall = 100%
03:00:00 Commencing smooth kNN distance calibration using 1 thread
03:00:10 Initializing from normalized Laplacian + noise
03:00:10 Commencing optimization for 500 epochs, with 100670 positive edges
03:00:18 Optimization finished
[1] "70 0.05"03:00:19 UMAP embedding parameters a = 1.75 b = 0.8421
03:00:19 Read 1203 rows and found 38 numeric columns
03:00:19 Using Annoy for neighbor search, n_neighbors = 70
03:00:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:00:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770f7bf2
03:00:19 Searching Annoy index using 1 thread, search_k = 7000
03:00:19 Annoy recall = 100%
03:00:24 Commencing smooth kNN distance calibration using 1 thread
03:00:34 Initializing from normalized Laplacian + noise
03:00:34 Commencing optimization for 500 epochs, with 100670 positive edges
03:00:42 Optimization finished
[1] "70 0.06"03:00:43 UMAP embedding parameters a = 1.715 b = 0.8526
03:00:43 Read 1203 rows and found 38 numeric columns
03:00:43 Using Annoy for neighbor search, n_neighbors = 70
03:00:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:00:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874327e00f
03:00:43 Searching Annoy index using 1 thread, search_k = 7000
03:00:44 Annoy recall = 100%
03:00:48 Commencing smooth kNN distance calibration using 1 thread
03:00:58 Initializing from normalized Laplacian + noise
03:00:58 Commencing optimization for 500 epochs, with 100670 positive edges
03:01:07 Optimization finished
[1] "70 0.07"03:01:07 UMAP embedding parameters a = 1.68 b = 0.8631
03:01:07 Read 1203 rows and found 38 numeric columns
03:01:07 Using Annoy for neighbor search, n_neighbors = 70
03:01:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:01:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a47a09b
03:01:07 Searching Annoy index using 1 thread, search_k = 7000
03:01:08 Annoy recall = 100%
03:01:13 Commencing smooth kNN distance calibration using 1 thread
03:01:23 Initializing from normalized Laplacian + noise
03:01:23 Commencing optimization for 500 epochs, with 100670 positive edges
03:01:31 Optimization finished
[1] "70 0.08"03:01:31 UMAP embedding parameters a = 1.645 b = 0.8737
03:01:31 Read 1203 rows and found 38 numeric columns
03:01:31 Using Annoy for neighbor search, n_neighbors = 70
03:01:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:01:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755f3f7f7
03:01:32 Searching Annoy index using 1 thread, search_k = 7000
03:01:32 Annoy recall = 100%
03:01:37 Commencing smooth kNN distance calibration using 1 thread
03:01:47 Initializing from normalized Laplacian + noise
03:01:47 Commencing optimization for 500 epochs, with 100670 positive edges
03:01:55 Optimization finished
[1] "70 0.09"03:01:55 UMAP embedding parameters a = 1.611 b = 0.8844
03:01:55 Read 1203 rows and found 38 numeric columns
03:01:55 Using Annoy for neighbor search, n_neighbors = 70
03:01:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:01:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878a9e631
03:01:56 Searching Annoy index using 1 thread, search_k = 7000
03:01:56 Annoy recall = 100%
03:02:01 Commencing smooth kNN distance calibration using 1 thread
03:02:11 Initializing from normalized Laplacian + noise
03:02:11 Commencing optimization for 500 epochs, with 100670 positive edges
03:02:20 Optimization finished
[1] "70 0.1"03:02:20 UMAP embedding parameters a = 1.577 b = 0.8951
03:02:20 Read 1203 rows and found 38 numeric columns
03:02:20 Using Annoy for neighbor search, n_neighbors = 70
03:02:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:02:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87434353a8
03:02:20 Searching Annoy index using 1 thread, search_k = 7000
03:02:21 Annoy recall = 100%
03:02:26 Commencing smooth kNN distance calibration using 1 thread
03:02:35 Initializing from normalized Laplacian + noise
03:02:35 Commencing optimization for 500 epochs, with 100670 positive edges
03:02:44 Optimization finished
[1] "70 0.11"03:02:44 UMAP embedding parameters a = 1.544 b = 0.9058
03:02:44 Read 1203 rows and found 38 numeric columns
03:02:44 Using Annoy for neighbor search, n_neighbors = 70
03:02:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:02:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873efbba40
03:02:44 Searching Annoy index using 1 thread, search_k = 7000
03:02:45 Annoy recall = 100%
03:02:50 Commencing smooth kNN distance calibration using 1 thread
03:03:00 Initializing from normalized Laplacian + noise
03:03:00 Commencing optimization for 500 epochs, with 100670 positive edges
03:03:08 Optimization finished
[1] "70 0.12"03:03:08 UMAP embedding parameters a = 1.51 b = 0.9165
03:03:08 Read 1203 rows and found 38 numeric columns
03:03:08 Using Annoy for neighbor search, n_neighbors = 70
03:03:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:03:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ac85148
03:03:09 Searching Annoy index using 1 thread, search_k = 7000
03:03:09 Annoy recall = 100%
03:03:14 Commencing smooth kNN distance calibration using 1 thread
03:03:24 Initializing from normalized Laplacian + noise
03:03:24 Commencing optimization for 500 epochs, with 100670 positive edges
03:03:32 Optimization finished
[1] "70 0.13"03:03:33 UMAP embedding parameters a = 1.478 b = 0.9272
03:03:33 Read 1203 rows and found 38 numeric columns
03:03:33 Using Annoy for neighbor search, n_neighbors = 70
03:03:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:03:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87343c84f9
03:03:33 Searching Annoy index using 1 thread, search_k = 7000
03:03:34 Annoy recall = 100%
03:03:38 Commencing smooth kNN distance calibration using 1 thread
03:03:48 Initializing from normalized Laplacian + noise
03:03:48 Commencing optimization for 500 epochs, with 100670 positive edges
03:03:57 Optimization finished
[1] "70 0.14"03:03:57 UMAP embedding parameters a = 1.446 b = 0.938
03:03:57 Read 1203 rows and found 38 numeric columns
03:03:57 Using Annoy for neighbor search, n_neighbors = 70
03:03:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:03:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fbce8ca
03:03:57 Searching Annoy index using 1 thread, search_k = 7000
03:03:58 Annoy recall = 100%
03:04:03 Commencing smooth kNN distance calibration using 1 thread
03:04:13 Initializing from normalized Laplacian + noise
03:04:13 Commencing optimization for 500 epochs, with 100670 positive edges
03:04:21 Optimization finished
[1] "70 0.15"03:04:21 UMAP embedding parameters a = 1.414 b = 0.9488
03:04:21 Read 1203 rows and found 38 numeric columns
03:04:21 Using Annoy for neighbor search, n_neighbors = 70
03:04:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:04:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871360c6e5
03:04:22 Searching Annoy index using 1 thread, search_k = 7000
03:04:22 Annoy recall = 100%
03:04:27 Commencing smooth kNN distance calibration using 1 thread
03:04:37 Initializing from normalized Laplacian + noise
03:04:37 Commencing optimization for 500 epochs, with 100670 positive edges
03:04:45 Optimization finished
[1] "70 0.16"03:04:46 UMAP embedding parameters a = 1.383 b = 0.9596
03:04:46 Read 1203 rows and found 38 numeric columns
03:04:46 Using Annoy for neighbor search, n_neighbors = 70
03:04:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:04:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723ddb1a3
03:04:46 Searching Annoy index using 1 thread, search_k = 7000
03:04:47 Annoy recall = 100%
03:04:51 Commencing smooth kNN distance calibration using 1 thread
03:05:01 Initializing from normalized Laplacian + noise
03:05:01 Commencing optimization for 500 epochs, with 100670 positive edges
03:05:10 Optimization finished
[1] "70 0.17"03:05:10 UMAP embedding parameters a = 1.352 b = 0.9704
03:05:10 Read 1203 rows and found 38 numeric columns
03:05:10 Using Annoy for neighbor search, n_neighbors = 70
03:05:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:05:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bbcc5e9
03:05:10 Searching Annoy index using 1 thread, search_k = 7000
03:05:11 Annoy recall = 100%
03:05:16 Commencing smooth kNN distance calibration using 1 thread
03:05:26 Initializing from normalized Laplacian + noise
03:05:26 Commencing optimization for 500 epochs, with 100670 positive edges
03:05:34 Optimization finished
[1] "70 0.18"03:05:34 UMAP embedding parameters a = 1.321 b = 0.9813
03:05:34 Read 1203 rows and found 38 numeric columns
03:05:34 Using Annoy for neighbor search, n_neighbors = 70
03:05:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:05:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c05fca4
03:05:35 Searching Annoy index using 1 thread, search_k = 7000
03:05:35 Annoy recall = 100%
03:05:40 Commencing smooth kNN distance calibration using 1 thread
03:05:50 Initializing from normalized Laplacian + noise
03:05:50 Commencing optimization for 500 epochs, with 100670 positive edges
03:05:59 Optimization finished
[1] "70 0.19"03:05:59 UMAP embedding parameters a = 1.292 b = 0.9921
03:05:59 Read 1203 rows and found 38 numeric columns
03:05:59 Using Annoy for neighbor search, n_neighbors = 70
03:05:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:05:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744390e09
03:05:59 Searching Annoy index using 1 thread, search_k = 7000
03:06:00 Annoy recall = 100%
03:06:05 Commencing smooth kNN distance calibration using 1 thread
03:06:15 Initializing from normalized Laplacian + noise
03:06:15 Commencing optimization for 500 epochs, with 100670 positive edges
03:06:23 Optimization finished
[1] "70 0.2"03:06:23 UMAP embedding parameters a = 1.262 b = 1.003
03:06:23 Read 1203 rows and found 38 numeric columns
03:06:23 Using Annoy for neighbor search, n_neighbors = 70
03:06:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:06:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87601fefc7
03:06:24 Searching Annoy index using 1 thread, search_k = 7000
03:06:24 Annoy recall = 100%
03:06:29 Commencing smooth kNN distance calibration using 1 thread
03:06:39 Initializing from normalized Laplacian + noise
03:06:39 Commencing optimization for 500 epochs, with 100670 positive edges
03:06:47 Optimization finished
[1] "71 0"03:06:48 UMAP embedding parameters a = 1.933 b = 0.7905
03:06:48 Read 1203 rows and found 38 numeric columns
03:06:48 Using Annoy for neighbor search, n_neighbors = 71
03:06:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:06:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876eb2b7e8
03:06:48 Searching Annoy index using 1 thread, search_k = 7100
03:06:49 Annoy recall = 100%
03:06:53 Commencing smooth kNN distance calibration using 1 thread
03:07:03 Initializing from normalized Laplacian + noise
03:07:03 Commencing optimization for 500 epochs, with 101976 positive edges
03:07:12 Optimization finished
[1] "71 0.01"03:07:12 UMAP embedding parameters a = 1.896 b = 0.8006
03:07:12 Read 1203 rows and found 38 numeric columns
03:07:12 Using Annoy for neighbor search, n_neighbors = 71
03:07:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:07:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87164cb2ac
03:07:12 Searching Annoy index using 1 thread, search_k = 7100
03:07:13 Annoy recall = 100%
03:07:18 Commencing smooth kNN distance calibration using 1 thread
03:07:28 Initializing from normalized Laplacian + noise
03:07:28 Commencing optimization for 500 epochs, with 101976 positive edges
03:07:36 Optimization finished
[1] "71 0.02"03:07:37 UMAP embedding parameters a = 1.859 b = 0.8109
03:07:37 Read 1203 rows and found 38 numeric columns
03:07:37 Using Annoy for neighbor search, n_neighbors = 71
03:07:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:07:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a76fd43
03:07:37 Searching Annoy index using 1 thread, search_k = 7100
03:07:38 Annoy recall = 100%
03:07:42 Commencing smooth kNN distance calibration using 1 thread
03:07:52 Initializing from normalized Laplacian + noise
03:07:52 Commencing optimization for 500 epochs, with 101976 positive edges
03:08:01 Optimization finished
[1] "71 0.03"03:08:01 UMAP embedding parameters a = 1.822 b = 0.8212
03:08:01 Read 1203 rows and found 38 numeric columns
03:08:01 Using Annoy for neighbor search, n_neighbors = 71
03:08:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:08:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727b96e1a
03:08:01 Searching Annoy index using 1 thread, search_k = 7100
03:08:02 Annoy recall = 100%
03:08:07 Commencing smooth kNN distance calibration using 1 thread
03:08:17 Initializing from normalized Laplacian + noise
03:08:17 Commencing optimization for 500 epochs, with 101976 positive edges
03:08:25 Optimization finished
[1] "71 0.04"03:08:26 UMAP embedding parameters a = 1.786 b = 0.8316
03:08:26 Read 1203 rows and found 38 numeric columns
03:08:26 Using Annoy for neighbor search, n_neighbors = 71
03:08:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:08:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764b1cdb7
03:08:26 Searching Annoy index using 1 thread, search_k = 7100
03:08:27 Annoy recall = 100%
03:08:32 Commencing smooth kNN distance calibration using 1 thread
03:08:41 Initializing from normalized Laplacian + noise
03:08:41 Commencing optimization for 500 epochs, with 101976 positive edges
03:08:50 Optimization finished
[1] "71 0.05"03:08:50 UMAP embedding parameters a = 1.75 b = 0.8421
03:08:50 Read 1203 rows and found 38 numeric columns
03:08:50 Using Annoy for neighbor search, n_neighbors = 71
03:08:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:08:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87696cb43a
03:08:50 Searching Annoy index using 1 thread, search_k = 7100
03:08:51 Annoy recall = 100%
03:08:56 Commencing smooth kNN distance calibration using 1 thread
03:09:06 Initializing from normalized Laplacian + noise
03:09:06 Commencing optimization for 500 epochs, with 101976 positive edges
03:09:14 Optimization finished
[1] "71 0.06"03:09:15 UMAP embedding parameters a = 1.715 b = 0.8526
03:09:15 Read 1203 rows and found 38 numeric columns
03:09:15 Using Annoy for neighbor search, n_neighbors = 71
03:09:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:09:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732d7941f
03:09:15 Searching Annoy index using 1 thread, search_k = 7100
03:09:15 Annoy recall = 100%
03:09:20 Commencing smooth kNN distance calibration using 1 thread
03:09:31 Initializing from normalized Laplacian + noise
03:09:31 Commencing optimization for 500 epochs, with 101976 positive edges
03:09:39 Optimization finished
[1] "71 0.07"03:09:39 UMAP embedding parameters a = 1.68 b = 0.8631
03:09:39 Read 1203 rows and found 38 numeric columns
03:09:39 Using Annoy for neighbor search, n_neighbors = 71
03:09:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:09:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f36e84c
03:09:40 Searching Annoy index using 1 thread, search_k = 7100
03:09:40 Annoy recall = 100%
03:09:45 Commencing smooth kNN distance calibration using 1 thread
03:09:55 Initializing from normalized Laplacian + noise
03:09:55 Commencing optimization for 500 epochs, with 101976 positive edges
03:10:03 Optimization finished
[1] "71 0.08"03:10:04 UMAP embedding parameters a = 1.645 b = 0.8737
03:10:04 Read 1203 rows and found 38 numeric columns
03:10:04 Using Annoy for neighbor search, n_neighbors = 71
03:10:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:10:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759878bbd
03:10:04 Searching Annoy index using 1 thread, search_k = 7100
03:10:05 Annoy recall = 100%
03:10:10 Commencing smooth kNN distance calibration using 1 thread
03:10:20 Initializing from normalized Laplacian + noise
03:10:20 Commencing optimization for 500 epochs, with 101976 positive edges
03:10:28 Optimization finished
[1] "71 0.09"03:10:28 UMAP embedding parameters a = 1.611 b = 0.8844
03:10:28 Read 1203 rows and found 38 numeric columns
03:10:28 Using Annoy for neighbor search, n_neighbors = 71
03:10:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:10:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87490dcbd1
03:10:29 Searching Annoy index using 1 thread, search_k = 7100
03:10:29 Annoy recall = 100%
03:10:34 Commencing smooth kNN distance calibration using 1 thread
03:10:44 Initializing from normalized Laplacian + noise
03:10:44 Commencing optimization for 500 epochs, with 101976 positive edges
03:10:52 Optimization finished
[1] "71 0.1"03:10:53 UMAP embedding parameters a = 1.577 b = 0.8951
03:10:53 Read 1203 rows and found 38 numeric columns
03:10:53 Using Annoy for neighbor search, n_neighbors = 71
03:10:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:10:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875db85b5f
03:10:53 Searching Annoy index using 1 thread, search_k = 7100
03:10:54 Annoy recall = 100%
03:10:59 Commencing smooth kNN distance calibration using 1 thread
03:11:09 Initializing from normalized Laplacian + noise
03:11:09 Commencing optimization for 500 epochs, with 101976 positive edges
03:11:17 Optimization finished
[1] "71 0.11"03:11:17 UMAP embedding parameters a = 1.544 b = 0.9058
03:11:17 Read 1203 rows and found 38 numeric columns
03:11:17 Using Annoy for neighbor search, n_neighbors = 71
03:11:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:11:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87316fe07a
03:11:18 Searching Annoy index using 1 thread, search_k = 7100
03:11:18 Annoy recall = 100%
03:11:23 Commencing smooth kNN distance calibration using 1 thread
03:11:33 Initializing from normalized Laplacian + noise
03:11:33 Commencing optimization for 500 epochs, with 101976 positive edges
03:11:42 Optimization finished
[1] "71 0.12"03:11:42 UMAP embedding parameters a = 1.51 b = 0.9165
03:11:42 Read 1203 rows and found 38 numeric columns
03:11:42 Using Annoy for neighbor search, n_neighbors = 71
03:11:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:11:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d193b6d
03:11:42 Searching Annoy index using 1 thread, search_k = 7100
03:11:43 Annoy recall = 100%
03:11:48 Commencing smooth kNN distance calibration using 1 thread
03:11:58 Initializing from normalized Laplacian + noise
03:11:58 Commencing optimization for 500 epochs, with 101976 positive edges
03:12:06 Optimization finished
[1] "71 0.13"03:12:07 UMAP embedding parameters a = 1.478 b = 0.9272
03:12:07 Read 1203 rows and found 38 numeric columns
03:12:07 Using Annoy for neighbor search, n_neighbors = 71
03:12:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:12:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773c25cc8
03:12:07 Searching Annoy index using 1 thread, search_k = 7100
03:12:07 Annoy recall = 100%
03:12:12 Commencing smooth kNN distance calibration using 1 thread
03:12:23 Initializing from normalized Laplacian + noise
03:12:23 Commencing optimization for 500 epochs, with 101976 positive edges
03:12:31 Optimization finished
[1] "71 0.14"03:12:31 UMAP embedding parameters a = 1.446 b = 0.938
03:12:31 Read 1203 rows and found 38 numeric columns
03:12:31 Using Annoy for neighbor search, n_neighbors = 71
03:12:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:12:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876be2c117
03:12:32 Searching Annoy index using 1 thread, search_k = 7100
03:12:32 Annoy recall = 100%
03:12:37 Commencing smooth kNN distance calibration using 1 thread
03:12:47 Initializing from normalized Laplacian + noise
03:12:47 Commencing optimization for 500 epochs, with 101976 positive edges
03:12:56 Optimization finished
[1] "71 0.15"03:12:56 UMAP embedding parameters a = 1.414 b = 0.9488
03:12:56 Read 1203 rows and found 38 numeric columns
03:12:56 Using Annoy for neighbor search, n_neighbors = 71
03:12:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:12:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875428b75f
03:12:56 Searching Annoy index using 1 thread, search_k = 7100
03:12:57 Annoy recall = 100%
03:13:02 Commencing smooth kNN distance calibration using 1 thread
03:13:12 Initializing from normalized Laplacian + noise
03:13:12 Commencing optimization for 500 epochs, with 101976 positive edges
03:13:20 Optimization finished
[1] "71 0.16"03:13:21 UMAP embedding parameters a = 1.383 b = 0.9596
03:13:21 Read 1203 rows and found 38 numeric columns
03:13:21 Using Annoy for neighbor search, n_neighbors = 71
03:13:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:13:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736ea3cd7
03:13:21 Searching Annoy index using 1 thread, search_k = 7100
03:13:21 Annoy recall = 100%
03:13:27 Commencing smooth kNN distance calibration using 1 thread
03:13:37 Initializing from normalized Laplacian + noise
03:13:37 Commencing optimization for 500 epochs, with 101976 positive edges
03:13:45 Optimization finished
[1] "71 0.17"03:13:45 UMAP embedding parameters a = 1.352 b = 0.9704
03:13:45 Read 1203 rows and found 38 numeric columns
03:13:45 Using Annoy for neighbor search, n_neighbors = 71
03:13:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:13:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87362a61b2
03:13:46 Searching Annoy index using 1 thread, search_k = 7100
03:13:46 Annoy recall = 100%
03:13:51 Commencing smooth kNN distance calibration using 1 thread
03:14:01 Initializing from normalized Laplacian + noise
03:14:01 Commencing optimization for 500 epochs, with 101976 positive edges
03:14:10 Optimization finished
[1] "71 0.18"03:14:10 UMAP embedding parameters a = 1.321 b = 0.9813
03:14:10 Read 1203 rows and found 38 numeric columns
03:14:10 Using Annoy for neighbor search, n_neighbors = 71
03:14:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:14:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a1caf56
03:14:10 Searching Annoy index using 1 thread, search_k = 7100
03:14:11 Annoy recall = 100%
03:14:16 Commencing smooth kNN distance calibration using 1 thread
03:14:26 Initializing from normalized Laplacian + noise
03:14:26 Commencing optimization for 500 epochs, with 101976 positive edges
03:14:34 Optimization finished
[1] "71 0.19"03:14:35 UMAP embedding parameters a = 1.292 b = 0.9921
03:14:35 Read 1203 rows and found 38 numeric columns
03:14:35 Using Annoy for neighbor search, n_neighbors = 71
03:14:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:14:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f942308
03:14:35 Searching Annoy index using 1 thread, search_k = 7100
03:14:36 Annoy recall = 100%
03:14:41 Commencing smooth kNN distance calibration using 1 thread
03:14:51 Initializing from normalized Laplacian + noise
03:14:51 Commencing optimization for 500 epochs, with 101976 positive edges
03:14:59 Optimization finished
[1] "71 0.2"03:14:59 UMAP embedding parameters a = 1.262 b = 1.003
03:14:59 Read 1203 rows and found 38 numeric columns
03:14:59 Using Annoy for neighbor search, n_neighbors = 71
03:14:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:15:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87796db55b
03:15:00 Searching Annoy index using 1 thread, search_k = 7100
03:15:00 Annoy recall = 100%
03:15:05 Commencing smooth kNN distance calibration using 1 thread
03:15:15 Initializing from normalized Laplacian + noise
03:15:15 Commencing optimization for 500 epochs, with 101976 positive edges
03:15:24 Optimization finished
[1] "72 0"03:15:24 UMAP embedding parameters a = 1.933 b = 0.7905
03:15:24 Read 1203 rows and found 38 numeric columns
03:15:24 Using Annoy for neighbor search, n_neighbors = 72
03:15:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:15:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769186997
03:15:24 Searching Annoy index using 1 thread, search_k = 7200
03:15:25 Annoy recall = 100%
03:15:30 Commencing smooth kNN distance calibration using 1 thread
03:15:40 Initializing from normalized Laplacian + noise
03:15:40 Commencing optimization for 500 epochs, with 103328 positive edges
03:15:49 Optimization finished
[1] "72 0.01"03:15:49 UMAP embedding parameters a = 1.896 b = 0.8006
03:15:49 Read 1203 rows and found 38 numeric columns
03:15:49 Using Annoy for neighbor search, n_neighbors = 72
03:15:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:15:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a5c7451
03:15:49 Searching Annoy index using 1 thread, search_k = 7200
03:15:50 Annoy recall = 100%
03:15:55 Commencing smooth kNN distance calibration using 1 thread
03:16:05 Initializing from normalized Laplacian + noise
03:16:05 Commencing optimization for 500 epochs, with 103328 positive edges
03:16:13 Optimization finished
[1] "72 0.02"03:16:13 UMAP embedding parameters a = 1.859 b = 0.8109
03:16:13 Read 1203 rows and found 38 numeric columns
03:16:13 Using Annoy for neighbor search, n_neighbors = 72
03:16:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:16:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872daa3a54
03:16:14 Searching Annoy index using 1 thread, search_k = 7200
03:16:14 Annoy recall = 100%
03:16:19 Commencing smooth kNN distance calibration using 1 thread
03:16:29 Initializing from normalized Laplacian + noise
03:16:29 Commencing optimization for 500 epochs, with 103328 positive edges
03:16:38 Optimization finished
[1] "72 0.03"03:16:38 UMAP embedding parameters a = 1.822 b = 0.8212
03:16:38 Read 1203 rows and found 38 numeric columns
03:16:38 Using Annoy for neighbor search, n_neighbors = 72
03:16:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:16:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718d55261
03:16:39 Searching Annoy index using 1 thread, search_k = 7200
03:16:39 Annoy recall = 100%
03:16:44 Commencing smooth kNN distance calibration using 1 thread
03:16:54 Initializing from normalized Laplacian + noise
03:16:54 Commencing optimization for 500 epochs, with 103328 positive edges
03:17:02 Optimization finished
[1] "72 0.04"03:17:03 UMAP embedding parameters a = 1.786 b = 0.8316
03:17:03 Read 1203 rows and found 38 numeric columns
03:17:03 Using Annoy for neighbor search, n_neighbors = 72
03:17:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:17:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875dbd3b36
03:17:03 Searching Annoy index using 1 thread, search_k = 7200
03:17:03 Annoy recall = 100%
03:17:08 Commencing smooth kNN distance calibration using 1 thread
03:17:18 Initializing from normalized Laplacian + noise
03:17:18 Commencing optimization for 500 epochs, with 103328 positive edges
03:17:27 Optimization finished
[1] "72 0.05"03:17:27 UMAP embedding parameters a = 1.75 b = 0.8421
03:17:27 Read 1203 rows and found 38 numeric columns
03:17:27 Using Annoy for neighbor search, n_neighbors = 72
03:17:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:17:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875187ebf7
03:17:27 Searching Annoy index using 1 thread, search_k = 7200
03:17:28 Annoy recall = 100%
03:17:33 Commencing smooth kNN distance calibration using 1 thread
03:17:43 Initializing from normalized Laplacian + noise
03:17:43 Commencing optimization for 500 epochs, with 103328 positive edges
03:17:51 Optimization finished
[1] "72 0.06"03:17:52 UMAP embedding parameters a = 1.715 b = 0.8526
03:17:52 Read 1203 rows and found 38 numeric columns
03:17:52 Using Annoy for neighbor search, n_neighbors = 72
03:17:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:17:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876492184a
03:17:52 Searching Annoy index using 1 thread, search_k = 7200
03:17:53 Annoy recall = 100%
03:17:58 Commencing smooth kNN distance calibration using 1 thread
03:18:08 Initializing from normalized Laplacian + noise
03:18:08 Commencing optimization for 500 epochs, with 103328 positive edges
03:18:16 Optimization finished
[1] "72 0.07"03:18:16 UMAP embedding parameters a = 1.68 b = 0.8631
03:18:16 Read 1203 rows and found 38 numeric columns
03:18:16 Using Annoy for neighbor search, n_neighbors = 72
03:18:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:18:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779c337da
03:18:17 Searching Annoy index using 1 thread, search_k = 7200
03:18:17 Annoy recall = 100%
03:18:22 Commencing smooth kNN distance calibration using 1 thread
03:18:32 Initializing from normalized Laplacian + noise
03:18:32 Commencing optimization for 500 epochs, with 103328 positive edges
03:18:41 Optimization finished
[1] "72 0.08"03:18:41 UMAP embedding parameters a = 1.645 b = 0.8737
03:18:41 Read 1203 rows and found 38 numeric columns
03:18:41 Using Annoy for neighbor search, n_neighbors = 72
03:18:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:18:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715c0fa01
03:18:41 Searching Annoy index using 1 thread, search_k = 7200
03:18:42 Annoy recall = 100%
03:18:47 Commencing smooth kNN distance calibration using 1 thread
03:18:57 Initializing from normalized Laplacian + noise
03:18:57 Commencing optimization for 500 epochs, with 103328 positive edges
03:19:05 Optimization finished
[1] "72 0.09"03:19:05 UMAP embedding parameters a = 1.611 b = 0.8844
03:19:05 Read 1203 rows and found 38 numeric columns
03:19:05 Using Annoy for neighbor search, n_neighbors = 72
03:19:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:19:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744b20811
03:19:06 Searching Annoy index using 1 thread, search_k = 7200
03:19:06 Annoy recall = 100%
03:19:11 Commencing smooth kNN distance calibration using 1 thread
03:19:21 Initializing from normalized Laplacian + noise
03:19:21 Commencing optimization for 500 epochs, with 103328 positive edges
03:19:29 Optimization finished
[1] "72 0.1"03:19:30 UMAP embedding parameters a = 1.577 b = 0.8951
03:19:30 Read 1203 rows and found 38 numeric columns
03:19:30 Using Annoy for neighbor search, n_neighbors = 72
03:19:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:19:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876875efc3
03:19:30 Searching Annoy index using 1 thread, search_k = 7200
03:19:31 Annoy recall = 100%
03:19:35 Commencing smooth kNN distance calibration using 1 thread
03:19:45 Initializing from normalized Laplacian + noise
03:19:45 Commencing optimization for 500 epochs, with 103328 positive edges
03:19:54 Optimization finished
[1] "72 0.11"03:19:54 UMAP embedding parameters a = 1.544 b = 0.9058
03:19:54 Read 1203 rows and found 38 numeric columns
03:19:54 Using Annoy for neighbor search, n_neighbors = 72
03:19:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:19:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c0dacad
03:19:54 Searching Annoy index using 1 thread, search_k = 7200
03:19:55 Annoy recall = 100%
03:20:00 Commencing smooth kNN distance calibration using 1 thread
03:20:10 Initializing from normalized Laplacian + noise
03:20:10 Commencing optimization for 500 epochs, with 103328 positive edges
03:20:18 Optimization finished
[1] "72 0.12"03:20:18 UMAP embedding parameters a = 1.51 b = 0.9165
03:20:18 Read 1203 rows and found 38 numeric columns
03:20:18 Using Annoy for neighbor search, n_neighbors = 72
03:20:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:20:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f290554
03:20:19 Searching Annoy index using 1 thread, search_k = 7200
03:20:19 Annoy recall = 100%
03:20:24 Commencing smooth kNN distance calibration using 1 thread
03:20:34 Initializing from normalized Laplacian + noise
03:20:34 Commencing optimization for 500 epochs, with 103328 positive edges
03:20:43 Optimization finished
[1] "72 0.13"03:20:43 UMAP embedding parameters a = 1.478 b = 0.9272
03:20:43 Read 1203 rows and found 38 numeric columns
03:20:43 Using Annoy for neighbor search, n_neighbors = 72
03:20:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:20:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87102f5ddd
03:20:43 Searching Annoy index using 1 thread, search_k = 7200
03:20:44 Annoy recall = 100%
03:20:49 Commencing smooth kNN distance calibration using 1 thread
03:20:59 Initializing from normalized Laplacian + noise
03:20:59 Commencing optimization for 500 epochs, with 103328 positive edges
03:21:07 Optimization finished
[1] "72 0.14"03:21:07 UMAP embedding parameters a = 1.446 b = 0.938
03:21:07 Read 1203 rows and found 38 numeric columns
03:21:07 Using Annoy for neighbor search, n_neighbors = 72
03:21:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:21:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710bf7a64
03:21:08 Searching Annoy index using 1 thread, search_k = 7200
03:21:08 Annoy recall = 100%
03:21:13 Commencing smooth kNN distance calibration using 1 thread
03:21:23 Initializing from normalized Laplacian + noise
03:21:23 Commencing optimization for 500 epochs, with 103328 positive edges
03:21:31 Optimization finished
[1] "72 0.15"03:21:32 UMAP embedding parameters a = 1.414 b = 0.9488
03:21:32 Read 1203 rows and found 38 numeric columns
03:21:32 Using Annoy for neighbor search, n_neighbors = 72
03:21:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:21:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87895b98f
03:21:32 Searching Annoy index using 1 thread, search_k = 7200
03:21:33 Annoy recall = 100%
03:21:38 Commencing smooth kNN distance calibration using 1 thread
03:21:47 Initializing from normalized Laplacian + noise
03:21:48 Commencing optimization for 500 epochs, with 103328 positive edges
03:21:56 Optimization finished
[1] "72 0.16"03:21:56 UMAP embedding parameters a = 1.383 b = 0.9596
03:21:56 Read 1203 rows and found 38 numeric columns
03:21:56 Using Annoy for neighbor search, n_neighbors = 72
03:21:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:21:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874306f1fc
03:21:57 Searching Annoy index using 1 thread, search_k = 7200
03:21:57 Annoy recall = 100%
03:22:02 Commencing smooth kNN distance calibration using 1 thread
03:22:12 Initializing from normalized Laplacian + noise
03:22:12 Commencing optimization for 500 epochs, with 103328 positive edges
03:22:20 Optimization finished
[1] "72 0.17"03:22:20 UMAP embedding parameters a = 1.352 b = 0.9704
03:22:20 Read 1203 rows and found 38 numeric columns
03:22:20 Using Annoy for neighbor search, n_neighbors = 72
03:22:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:22:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ff662b0
03:22:21 Searching Annoy index using 1 thread, search_k = 7200
03:22:21 Annoy recall = 100%
03:22:26 Commencing smooth kNN distance calibration using 1 thread
03:22:36 Initializing from normalized Laplacian + noise
03:22:36 Commencing optimization for 500 epochs, with 103328 positive edges
03:22:45 Optimization finished
[1] "72 0.18"03:22:45 UMAP embedding parameters a = 1.321 b = 0.9813
03:22:45 Read 1203 rows and found 38 numeric columns
03:22:45 Using Annoy for neighbor search, n_neighbors = 72
03:22:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:22:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87621d454c
03:22:45 Searching Annoy index using 1 thread, search_k = 7200
03:22:46 Annoy recall = 100%
03:22:51 Commencing smooth kNN distance calibration using 1 thread
03:23:01 Initializing from normalized Laplacian + noise
03:23:01 Commencing optimization for 500 epochs, with 103328 positive edges
03:23:09 Optimization finished
[1] "72 0.19"03:23:09 UMAP embedding parameters a = 1.292 b = 0.9921
03:23:09 Read 1203 rows and found 38 numeric columns
03:23:09 Using Annoy for neighbor search, n_neighbors = 72
03:23:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:23:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c14bdcd
03:23:10 Searching Annoy index using 1 thread, search_k = 7200
03:23:10 Annoy recall = 100%
03:23:15 Commencing smooth kNN distance calibration using 1 thread
03:23:25 Initializing from normalized Laplacian + noise
03:23:25 Commencing optimization for 500 epochs, with 103328 positive edges
03:23:34 Optimization finished
[1] "72 0.2"03:23:34 UMAP embedding parameters a = 1.262 b = 1.003
03:23:34 Read 1203 rows and found 38 numeric columns
03:23:34 Using Annoy for neighbor search, n_neighbors = 72
03:23:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:23:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874daebe0f
03:23:34 Searching Annoy index using 1 thread, search_k = 7200
03:23:35 Annoy recall = 100%
03:23:40 Commencing smooth kNN distance calibration using 1 thread
03:23:50 Initializing from normalized Laplacian + noise
03:23:50 Commencing optimization for 500 epochs, with 103328 positive edges
03:23:58 Optimization finished
[1] "73 0"03:23:59 UMAP embedding parameters a = 1.933 b = 0.7905
03:23:59 Read 1203 rows and found 38 numeric columns
03:23:59 Using Annoy for neighbor search, n_neighbors = 73
03:23:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:23:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87138d25c6
03:23:59 Searching Annoy index using 1 thread, search_k = 7300
03:24:00 Annoy recall = 100%
03:24:04 Commencing smooth kNN distance calibration using 1 thread
03:24:14 Initializing from normalized Laplacian + noise
03:24:14 Commencing optimization for 500 epochs, with 104668 positive edges
03:24:23 Optimization finished
[1] "73 0.01"03:24:23 UMAP embedding parameters a = 1.896 b = 0.8006
03:24:23 Read 1203 rows and found 38 numeric columns
03:24:23 Using Annoy for neighbor search, n_neighbors = 73
03:24:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:24:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87592df93a
03:24:23 Searching Annoy index using 1 thread, search_k = 7300
03:24:24 Annoy recall = 100%
03:24:29 Commencing smooth kNN distance calibration using 1 thread
03:24:39 Initializing from normalized Laplacian + noise
03:24:39 Commencing optimization for 500 epochs, with 104668 positive edges
03:24:47 Optimization finished
[1] "73 0.02"03:24:48 UMAP embedding parameters a = 1.859 b = 0.8109
03:24:48 Read 1203 rows and found 38 numeric columns
03:24:48 Using Annoy for neighbor search, n_neighbors = 73
03:24:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:24:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741711ad7
03:24:48 Searching Annoy index using 1 thread, search_k = 7300
03:24:49 Annoy recall = 100%
03:24:54 Commencing smooth kNN distance calibration using 1 thread
03:25:04 Initializing from normalized Laplacian + noise
03:25:04 Commencing optimization for 500 epochs, with 104668 positive edges
03:25:12 Optimization finished
[1] "73 0.03"03:25:12 UMAP embedding parameters a = 1.822 b = 0.8212
03:25:12 Read 1203 rows and found 38 numeric columns
03:25:12 Using Annoy for neighbor search, n_neighbors = 73
03:25:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:25:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f6fe6dd
03:25:13 Searching Annoy index using 1 thread, search_k = 7300
03:25:13 Annoy recall = 100%
03:25:18 Commencing smooth kNN distance calibration using 1 thread
03:25:28 Initializing from normalized Laplacian + noise
03:25:28 Commencing optimization for 500 epochs, with 104668 positive edges
03:25:36 Optimization finished
[1] "73 0.04"03:25:37 UMAP embedding parameters a = 1.786 b = 0.8316
03:25:37 Read 1203 rows and found 38 numeric columns
03:25:37 Using Annoy for neighbor search, n_neighbors = 73
03:25:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:25:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d56b09a
03:25:37 Searching Annoy index using 1 thread, search_k = 7300
03:25:38 Annoy recall = 100%
03:25:43 Commencing smooth kNN distance calibration using 1 thread
03:25:53 Initializing from normalized Laplacian + noise
03:25:53 Commencing optimization for 500 epochs, with 104668 positive edges
03:26:01 Optimization finished
[1] "73 0.05"03:26:01 UMAP embedding parameters a = 1.75 b = 0.8421
03:26:01 Read 1203 rows and found 38 numeric columns
03:26:01 Using Annoy for neighbor search, n_neighbors = 73
03:26:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:26:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87785b57ae
03:26:02 Searching Annoy index using 1 thread, search_k = 7300
03:26:02 Annoy recall = 100%
03:26:07 Commencing smooth kNN distance calibration using 1 thread
03:26:17 Initializing from normalized Laplacian + noise
03:26:17 Commencing optimization for 500 epochs, with 104668 positive edges
03:26:26 Optimization finished
[1] "73 0.06"03:26:26 UMAP embedding parameters a = 1.715 b = 0.8526
03:26:26 Read 1203 rows and found 38 numeric columns
03:26:26 Using Annoy for neighbor search, n_neighbors = 73
03:26:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:26:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87359a4890
03:26:26 Searching Annoy index using 1 thread, search_k = 7300
03:26:27 Annoy recall = 100%
03:26:32 Commencing smooth kNN distance calibration using 1 thread
03:26:42 Initializing from normalized Laplacian + noise
03:26:42 Commencing optimization for 500 epochs, with 104668 positive edges
03:26:50 Optimization finished
[1] "73 0.07"03:26:51 UMAP embedding parameters a = 1.68 b = 0.8631
03:26:51 Read 1203 rows and found 38 numeric columns
03:26:51 Using Annoy for neighbor search, n_neighbors = 73
03:26:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:26:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757735ff0
03:26:51 Searching Annoy index using 1 thread, search_k = 7300
03:26:52 Annoy recall = 100%
03:26:57 Commencing smooth kNN distance calibration using 1 thread
03:27:07 Initializing from normalized Laplacian + noise
03:27:07 Commencing optimization for 500 epochs, with 104668 positive edges
03:27:15 Optimization finished
[1] "73 0.08"03:27:15 UMAP embedding parameters a = 1.645 b = 0.8737
03:27:15 Read 1203 rows and found 38 numeric columns
03:27:15 Using Annoy for neighbor search, n_neighbors = 73
03:27:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:27:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737ef7ab7
03:27:16 Searching Annoy index using 1 thread, search_k = 7300
03:27:16 Annoy recall = 100%
03:27:21 Commencing smooth kNN distance calibration using 1 thread
03:27:31 Initializing from normalized Laplacian + noise
03:27:31 Commencing optimization for 500 epochs, with 104668 positive edges
03:27:40 Optimization finished
[1] "73 0.09"03:27:40 UMAP embedding parameters a = 1.611 b = 0.8844
03:27:40 Read 1203 rows and found 38 numeric columns
03:27:40 Using Annoy for neighbor search, n_neighbors = 73
03:27:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:27:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f07fdeb
03:27:40 Searching Annoy index using 1 thread, search_k = 7300
03:27:41 Annoy recall = 100%
03:27:46 Commencing smooth kNN distance calibration using 1 thread
03:27:56 Initializing from normalized Laplacian + noise
03:27:56 Commencing optimization for 500 epochs, with 104668 positive edges
03:28:04 Optimization finished
[1] "73 0.1"03:28:05 UMAP embedding parameters a = 1.577 b = 0.8951
03:28:05 Read 1203 rows and found 38 numeric columns
03:28:05 Using Annoy for neighbor search, n_neighbors = 73
03:28:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:28:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87408bc987
03:28:05 Searching Annoy index using 1 thread, search_k = 7300
03:28:06 Annoy recall = 100%
03:28:10 Commencing smooth kNN distance calibration using 1 thread
03:28:20 Initializing from normalized Laplacian + noise
03:28:20 Commencing optimization for 500 epochs, with 104668 positive edges
03:28:29 Optimization finished
[1] "73 0.11"03:28:29 UMAP embedding parameters a = 1.544 b = 0.9058
03:28:29 Read 1203 rows and found 38 numeric columns
03:28:29 Using Annoy for neighbor search, n_neighbors = 73
03:28:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:28:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724bef08
03:28:29 Searching Annoy index using 1 thread, search_k = 7300
03:28:30 Annoy recall = 100%
03:28:35 Commencing smooth kNN distance calibration using 1 thread
03:28:45 Initializing from normalized Laplacian + noise
03:28:45 Commencing optimization for 500 epochs, with 104668 positive edges
03:28:54 Optimization finished
[1] "73 0.12"03:28:54 UMAP embedding parameters a = 1.51 b = 0.9165
03:28:54 Read 1203 rows and found 38 numeric columns
03:28:54 Using Annoy for neighbor search, n_neighbors = 73
03:28:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:28:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cb2383f
03:28:54 Searching Annoy index using 1 thread, search_k = 7300
03:28:55 Annoy recall = 100%
03:29:00 Commencing smooth kNN distance calibration using 1 thread
03:29:10 Initializing from normalized Laplacian + noise
03:29:10 Commencing optimization for 500 epochs, with 104668 positive edges
03:29:18 Optimization finished
[1] "73 0.13"03:29:19 UMAP embedding parameters a = 1.478 b = 0.9272
03:29:19 Read 1203 rows and found 38 numeric columns
03:29:19 Using Annoy for neighbor search, n_neighbors = 73
03:29:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:29:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759611be8
03:29:19 Searching Annoy index using 1 thread, search_k = 7300
03:29:20 Annoy recall = 100%
03:29:24 Commencing smooth kNN distance calibration using 1 thread
03:29:34 Initializing from normalized Laplacian + noise
03:29:35 Commencing optimization for 500 epochs, with 104668 positive edges
03:29:43 Optimization finished
[1] "73 0.14"03:29:43 UMAP embedding parameters a = 1.446 b = 0.938
03:29:43 Read 1203 rows and found 38 numeric columns
03:29:43 Using Annoy for neighbor search, n_neighbors = 73
03:29:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:29:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760092a3e
03:29:44 Searching Annoy index using 1 thread, search_k = 7300
03:29:44 Annoy recall = 100%
03:29:49 Commencing smooth kNN distance calibration using 1 thread
03:29:59 Initializing from normalized Laplacian + noise
03:29:59 Commencing optimization for 500 epochs, with 104668 positive edges
03:30:08 Optimization finished
[1] "73 0.15"03:30:08 UMAP embedding parameters a = 1.414 b = 0.9488
03:30:08 Read 1203 rows and found 38 numeric columns
03:30:08 Using Annoy for neighbor search, n_neighbors = 73
03:30:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:30:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e3a2436
03:30:08 Searching Annoy index using 1 thread, search_k = 7300
03:30:09 Annoy recall = 100%
03:30:14 Commencing smooth kNN distance calibration using 1 thread
03:30:24 Initializing from normalized Laplacian + noise
03:30:24 Commencing optimization for 500 epochs, with 104668 positive edges
03:30:32 Optimization finished
[1] "73 0.16"03:30:33 UMAP embedding parameters a = 1.383 b = 0.9596
03:30:33 Read 1203 rows and found 38 numeric columns
03:30:33 Using Annoy for neighbor search, n_neighbors = 73
03:30:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:30:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873df33432
03:30:33 Searching Annoy index using 1 thread, search_k = 7300
03:30:34 Annoy recall = 100%
03:30:39 Commencing smooth kNN distance calibration using 1 thread
03:30:49 Initializing from normalized Laplacian + noise
03:30:49 Commencing optimization for 500 epochs, with 104668 positive edges
03:30:57 Optimization finished
[1] "73 0.17"03:30:57 UMAP embedding parameters a = 1.352 b = 0.9704
03:30:57 Read 1203 rows and found 38 numeric columns
03:30:57 Using Annoy for neighbor search, n_neighbors = 73
03:30:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:30:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759cc6218
03:30:58 Searching Annoy index using 1 thread, search_k = 7300
03:30:58 Annoy recall = 100%
03:31:03 Commencing smooth kNN distance calibration using 1 thread
03:31:14 Initializing from normalized Laplacian + noise
03:31:14 Commencing optimization for 500 epochs, with 104668 positive edges
03:31:22 Optimization finished
[1] "73 0.18"03:31:22 UMAP embedding parameters a = 1.321 b = 0.9813
03:31:22 Read 1203 rows and found 38 numeric columns
03:31:22 Using Annoy for neighbor search, n_neighbors = 73
03:31:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:31:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743fb1e37
03:31:23 Searching Annoy index using 1 thread, search_k = 7300
03:31:23 Annoy recall = 100%
03:31:28 Commencing smooth kNN distance calibration using 1 thread
03:31:38 Initializing from normalized Laplacian + noise
03:31:38 Commencing optimization for 500 epochs, with 104668 positive edges
03:31:47 Optimization finished
[1] "73 0.19"03:31:47 UMAP embedding parameters a = 1.292 b = 0.9921
03:31:47 Read 1203 rows and found 38 numeric columns
03:31:47 Using Annoy for neighbor search, n_neighbors = 73
03:31:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:31:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a53c44
03:31:47 Searching Annoy index using 1 thread, search_k = 7300
03:31:48 Annoy recall = 100%
03:31:53 Commencing smooth kNN distance calibration using 1 thread
03:32:03 Initializing from normalized Laplacian + noise
03:32:03 Commencing optimization for 500 epochs, with 104668 positive edges
03:32:12 Optimization finished
[1] "73 0.2"03:32:12 UMAP embedding parameters a = 1.262 b = 1.003
03:32:12 Read 1203 rows and found 38 numeric columns
03:32:12 Using Annoy for neighbor search, n_neighbors = 73
03:32:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:32:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87424251db
03:32:12 Searching Annoy index using 1 thread, search_k = 7300
03:32:13 Annoy recall = 100%
03:32:18 Commencing smooth kNN distance calibration using 1 thread
03:32:28 Initializing from normalized Laplacian + noise
03:32:28 Commencing optimization for 500 epochs, with 104668 positive edges
03:32:36 Optimization finished
[1] "74 0"03:32:37 UMAP embedding parameters a = 1.933 b = 0.7905
03:32:37 Read 1203 rows and found 38 numeric columns
03:32:37 Using Annoy for neighbor search, n_neighbors = 74
03:32:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:32:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877008cae5
03:32:37 Searching Annoy index using 1 thread, search_k = 7400
03:32:38 Annoy recall = 100%
03:32:43 Commencing smooth kNN distance calibration using 1 thread
03:32:53 Initializing from normalized Laplacian + noise
03:32:53 Commencing optimization for 500 epochs, with 105982 positive edges
03:33:01 Optimization finished
[1] "74 0.01"03:33:01 UMAP embedding parameters a = 1.896 b = 0.8006
03:33:01 Read 1203 rows and found 38 numeric columns
03:33:01 Using Annoy for neighbor search, n_neighbors = 74
03:33:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:33:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721ce4198
03:33:02 Searching Annoy index using 1 thread, search_k = 7400
03:33:02 Annoy recall = 100%
03:33:07 Commencing smooth kNN distance calibration using 1 thread
03:33:18 Initializing from normalized Laplacian + noise
03:33:18 Commencing optimization for 500 epochs, with 105982 positive edges
03:33:26 Optimization finished
[1] "74 0.02"03:33:26 UMAP embedding parameters a = 1.859 b = 0.8109
03:33:26 Read 1203 rows and found 38 numeric columns
03:33:26 Using Annoy for neighbor search, n_neighbors = 74
03:33:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:33:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875271afb8
03:33:27 Searching Annoy index using 1 thread, search_k = 7400
03:33:27 Annoy recall = 100%
03:33:32 Commencing smooth kNN distance calibration using 1 thread
03:33:42 Initializing from normalized Laplacian + noise
03:33:42 Commencing optimization for 500 epochs, with 105982 positive edges
03:33:51 Optimization finished
[1] "74 0.03"03:33:51 UMAP embedding parameters a = 1.822 b = 0.8212
03:33:51 Read 1203 rows and found 38 numeric columns
03:33:51 Using Annoy for neighbor search, n_neighbors = 74
03:33:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:33:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c84549
03:33:52 Searching Annoy index using 1 thread, search_k = 7400
03:33:52 Annoy recall = 100%
03:33:57 Commencing smooth kNN distance calibration using 1 thread
03:34:07 Initializing from normalized Laplacian + noise
03:34:07 Commencing optimization for 500 epochs, with 105982 positive edges
03:34:16 Optimization finished
[1] "74 0.04"03:34:16 UMAP embedding parameters a = 1.786 b = 0.8316
03:34:16 Read 1203 rows and found 38 numeric columns
03:34:16 Using Annoy for neighbor search, n_neighbors = 74
03:34:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:34:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a63fb27
03:34:17 Searching Annoy index using 1 thread, search_k = 7400
03:34:17 Annoy recall = 100%
03:34:22 Commencing smooth kNN distance calibration using 1 thread
03:34:32 Initializing from normalized Laplacian + noise
03:34:32 Commencing optimization for 500 epochs, with 105982 positive edges
03:34:41 Optimization finished
[1] "74 0.05"03:34:41 UMAP embedding parameters a = 1.75 b = 0.8421
03:34:41 Read 1203 rows and found 38 numeric columns
03:34:41 Using Annoy for neighbor search, n_neighbors = 74
03:34:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:34:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871578a1b4
03:34:41 Searching Annoy index using 1 thread, search_k = 7400
03:34:42 Annoy recall = 100%
03:34:47 Commencing smooth kNN distance calibration using 1 thread
03:34:57 Initializing from normalized Laplacian + noise
03:34:57 Commencing optimization for 500 epochs, with 105982 positive edges
03:35:06 Optimization finished
[1] "74 0.06"03:35:06 UMAP embedding parameters a = 1.715 b = 0.8526
03:35:06 Read 1203 rows and found 38 numeric columns
03:35:06 Using Annoy for neighbor search, n_neighbors = 74
03:35:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:35:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770bea7fa
03:35:06 Searching Annoy index using 1 thread, search_k = 7400
03:35:07 Annoy recall = 100%
03:35:12 Commencing smooth kNN distance calibration using 1 thread
03:35:22 Initializing from normalized Laplacian + noise
03:35:22 Commencing optimization for 500 epochs, with 105982 positive edges
03:35:31 Optimization finished
[1] "74 0.07"03:35:31 UMAP embedding parameters a = 1.68 b = 0.8631
03:35:31 Read 1203 rows and found 38 numeric columns
03:35:31 Using Annoy for neighbor search, n_neighbors = 74
03:35:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:35:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c814073
03:35:31 Searching Annoy index using 1 thread, search_k = 7400
03:35:32 Annoy recall = 100%
03:35:37 Commencing smooth kNN distance calibration using 1 thread
03:35:47 Initializing from normalized Laplacian + noise
03:35:47 Commencing optimization for 500 epochs, with 105982 positive edges
03:35:55 Optimization finished
[1] "74 0.08"03:35:56 UMAP embedding parameters a = 1.645 b = 0.8737
03:35:56 Read 1203 rows and found 38 numeric columns
03:35:56 Using Annoy for neighbor search, n_neighbors = 74
03:35:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:35:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87218d5f82
03:35:56 Searching Annoy index using 1 thread, search_k = 7400
03:35:57 Annoy recall = 100%
03:36:02 Commencing smooth kNN distance calibration using 1 thread
03:36:12 Initializing from normalized Laplacian + noise
03:36:12 Commencing optimization for 500 epochs, with 105982 positive edges
03:36:21 Optimization finished
[1] "74 0.09"03:36:21 UMAP embedding parameters a = 1.611 b = 0.8844
03:36:21 Read 1203 rows and found 38 numeric columns
03:36:21 Using Annoy for neighbor search, n_neighbors = 74
03:36:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:36:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e6d6609
03:36:21 Searching Annoy index using 1 thread, search_k = 7400
03:36:22 Annoy recall = 100%
03:36:27 Commencing smooth kNN distance calibration using 1 thread
03:36:37 Initializing from normalized Laplacian + noise
03:36:37 Commencing optimization for 500 epochs, with 105982 positive edges
03:36:45 Optimization finished
[1] "74 0.1"03:36:46 UMAP embedding parameters a = 1.577 b = 0.8951
03:36:46 Read 1203 rows and found 38 numeric columns
03:36:46 Using Annoy for neighbor search, n_neighbors = 74
03:36:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:36:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87200e663a
03:36:46 Searching Annoy index using 1 thread, search_k = 7400
03:36:47 Annoy recall = 100%
03:36:52 Commencing smooth kNN distance calibration using 1 thread
03:37:02 Initializing from normalized Laplacian + noise
03:37:02 Commencing optimization for 500 epochs, with 105982 positive edges
03:37:10 Optimization finished
[1] "74 0.11"03:37:11 UMAP embedding parameters a = 1.544 b = 0.9058
03:37:11 Read 1203 rows and found 38 numeric columns
03:37:11 Using Annoy for neighbor search, n_neighbors = 74
03:37:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:37:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877abb58bc
03:37:11 Searching Annoy index using 1 thread, search_k = 7400
03:37:12 Annoy recall = 100%
03:37:17 Commencing smooth kNN distance calibration using 1 thread
03:37:27 Initializing from normalized Laplacian + noise
03:37:27 Commencing optimization for 500 epochs, with 105982 positive edges
03:37:35 Optimization finished
[1] "74 0.12"03:37:36 UMAP embedding parameters a = 1.51 b = 0.9165
03:37:36 Read 1203 rows and found 38 numeric columns
03:37:36 Using Annoy for neighbor search, n_neighbors = 74
03:37:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:37:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fde80e1
03:37:36 Searching Annoy index using 1 thread, search_k = 7400
03:37:37 Annoy recall = 100%
03:37:42 Commencing smooth kNN distance calibration using 1 thread
03:37:52 Initializing from normalized Laplacian + noise
03:37:52 Commencing optimization for 500 epochs, with 105982 positive edges
03:38:01 Optimization finished
[1] "74 0.13"03:38:01 UMAP embedding parameters a = 1.478 b = 0.9272
03:38:01 Read 1203 rows and found 38 numeric columns
03:38:01 Using Annoy for neighbor search, n_neighbors = 74
03:38:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:38:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f7e4d17
03:38:01 Searching Annoy index using 1 thread, search_k = 7400
03:38:02 Annoy recall = 100%
03:38:07 Commencing smooth kNN distance calibration using 1 thread
03:38:17 Initializing from normalized Laplacian + noise
03:38:17 Commencing optimization for 500 epochs, with 105982 positive edges
03:38:26 Optimization finished
[1] "74 0.14"03:38:26 UMAP embedding parameters a = 1.446 b = 0.938
03:38:26 Read 1203 rows and found 38 numeric columns
03:38:26 Using Annoy for neighbor search, n_neighbors = 74
03:38:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:38:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728120956
03:38:26 Searching Annoy index using 1 thread, search_k = 7400
03:38:27 Annoy recall = 100%
03:38:32 Commencing smooth kNN distance calibration using 1 thread
03:38:42 Initializing from normalized Laplacian + noise
03:38:42 Commencing optimization for 500 epochs, with 105982 positive edges
03:38:51 Optimization finished
[1] "74 0.15"03:38:51 UMAP embedding parameters a = 1.414 b = 0.9488
03:38:51 Read 1203 rows and found 38 numeric columns
03:38:51 Using Annoy for neighbor search, n_neighbors = 74
03:38:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:38:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877839d88f
03:38:51 Searching Annoy index using 1 thread, search_k = 7400
03:38:52 Annoy recall = 100%
03:38:57 Commencing smooth kNN distance calibration using 1 thread
03:39:07 Initializing from normalized Laplacian + noise
03:39:07 Commencing optimization for 500 epochs, with 105982 positive edges
03:39:16 Optimization finished
[1] "74 0.16"03:39:16 UMAP embedding parameters a = 1.383 b = 0.9596
03:39:16 Read 1203 rows and found 38 numeric columns
03:39:16 Using Annoy for neighbor search, n_neighbors = 74
03:39:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:39:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87551895a7
03:39:17 Searching Annoy index using 1 thread, search_k = 7400
03:39:17 Annoy recall = 100%
03:39:22 Commencing smooth kNN distance calibration using 1 thread
03:39:33 Initializing from normalized Laplacian + noise
03:39:33 Commencing optimization for 500 epochs, with 105982 positive edges
03:39:41 Optimization finished
[1] "74 0.17"03:39:41 UMAP embedding parameters a = 1.352 b = 0.9704
03:39:42 Read 1203 rows and found 38 numeric columns
03:39:42 Using Annoy for neighbor search, n_neighbors = 74
03:39:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:39:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f856947
03:39:42 Searching Annoy index using 1 thread, search_k = 7400
03:39:42 Annoy recall = 100%
03:39:48 Commencing smooth kNN distance calibration using 1 thread
03:39:58 Initializing from normalized Laplacian + noise
03:39:58 Commencing optimization for 500 epochs, with 105982 positive edges
03:40:06 Optimization finished
[1] "74 0.18"03:40:07 UMAP embedding parameters a = 1.321 b = 0.9813
03:40:07 Read 1203 rows and found 38 numeric columns
03:40:07 Using Annoy for neighbor search, n_neighbors = 74
03:40:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:40:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730295346
03:40:07 Searching Annoy index using 1 thread, search_k = 7400
03:40:08 Annoy recall = 100%
03:40:13 Commencing smooth kNN distance calibration using 1 thread
03:40:23 Initializing from normalized Laplacian + noise
03:40:23 Commencing optimization for 500 epochs, with 105982 positive edges
03:40:32 Optimization finished
[1] "74 0.19"03:40:32 UMAP embedding parameters a = 1.292 b = 0.9921
03:40:32 Read 1203 rows and found 38 numeric columns
03:40:32 Using Annoy for neighbor search, n_neighbors = 74
03:40:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:40:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874209392
03:40:32 Searching Annoy index using 1 thread, search_k = 7400
03:40:33 Annoy recall = 100%
03:40:38 Commencing smooth kNN distance calibration using 1 thread
03:40:48 Initializing from normalized Laplacian + noise
03:40:48 Commencing optimization for 500 epochs, with 105982 positive edges
03:40:57 Optimization finished
[1] "74 0.2"03:40:57 UMAP embedding parameters a = 1.262 b = 1.003
03:40:57 Read 1203 rows and found 38 numeric columns
03:40:57 Using Annoy for neighbor search, n_neighbors = 74
03:40:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:40:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87401132ce
03:40:58 Searching Annoy index using 1 thread, search_k = 7400
03:40:58 Annoy recall = 100%
03:41:03 Commencing smooth kNN distance calibration using 1 thread
03:41:14 Initializing from normalized Laplacian + noise
03:41:14 Commencing optimization for 500 epochs, with 105982 positive edges
03:41:22 Optimization finished
[1] "75 0"03:41:22 UMAP embedding parameters a = 1.933 b = 0.7905
03:41:22 Read 1203 rows and found 38 numeric columns
03:41:22 Using Annoy for neighbor search, n_neighbors = 75
03:41:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:41:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873275424e
03:41:23 Searching Annoy index using 1 thread, search_k = 7500
03:41:23 Annoy recall = 100%
03:41:29 Commencing smooth kNN distance calibration using 1 thread
03:41:39 Initializing from normalized Laplacian + noise
03:41:39 Commencing optimization for 500 epochs, with 107306 positive edges
03:41:47 Optimization finished
[1] "75 0.01"03:41:48 UMAP embedding parameters a = 1.896 b = 0.8006
03:41:48 Read 1203 rows and found 38 numeric columns
03:41:48 Using Annoy for neighbor search, n_neighbors = 75
03:41:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:41:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760d2cbd1
03:41:48 Searching Annoy index using 1 thread, search_k = 7500
03:41:49 Annoy recall = 100%
03:41:54 Commencing smooth kNN distance calibration using 1 thread
03:42:04 Initializing from normalized Laplacian + noise
03:42:04 Commencing optimization for 500 epochs, with 107306 positive edges
03:42:13 Optimization finished
[1] "75 0.02"03:42:13 UMAP embedding parameters a = 1.859 b = 0.8109
03:42:13 Read 1203 rows and found 38 numeric columns
03:42:13 Using Annoy for neighbor search, n_neighbors = 75
03:42:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:42:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719724eb7
03:42:13 Searching Annoy index using 1 thread, search_k = 7500
03:42:14 Annoy recall = 100%
03:42:19 Commencing smooth kNN distance calibration using 1 thread
03:42:29 Initializing from normalized Laplacian + noise
03:42:29 Commencing optimization for 500 epochs, with 107306 positive edges
03:42:38 Optimization finished
[1] "75 0.03"03:42:38 UMAP embedding parameters a = 1.822 b = 0.8212
03:42:38 Read 1203 rows and found 38 numeric columns
03:42:38 Using Annoy for neighbor search, n_neighbors = 75
03:42:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:42:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87127e6c8c
03:42:39 Searching Annoy index using 1 thread, search_k = 7500
03:42:39 Annoy recall = 100%
03:42:44 Commencing smooth kNN distance calibration using 1 thread
03:42:55 Initializing from normalized Laplacian + noise
03:42:55 Commencing optimization for 500 epochs, with 107306 positive edges
03:43:03 Optimization finished
[1] "75 0.04"03:43:04 UMAP embedding parameters a = 1.786 b = 0.8316
03:43:04 Read 1203 rows and found 38 numeric columns
03:43:04 Using Annoy for neighbor search, n_neighbors = 75
03:43:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:43:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f0cf008
03:43:04 Searching Annoy index using 1 thread, search_k = 7500
03:43:05 Annoy recall = 100%
03:43:10 Commencing smooth kNN distance calibration using 1 thread
03:43:20 Initializing from normalized Laplacian + noise
03:43:20 Commencing optimization for 500 epochs, with 107306 positive edges
03:43:29 Optimization finished
[1] "75 0.05"03:43:29 UMAP embedding parameters a = 1.75 b = 0.8421
03:43:29 Read 1203 rows and found 38 numeric columns
03:43:29 Using Annoy for neighbor search, n_neighbors = 75
03:43:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:43:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87576582e9
03:43:29 Searching Annoy index using 1 thread, search_k = 7500
03:43:30 Annoy recall = 100%
03:43:35 Commencing smooth kNN distance calibration using 1 thread
03:43:45 Initializing from normalized Laplacian + noise
03:43:46 Commencing optimization for 500 epochs, with 107306 positive edges
03:43:54 Optimization finished
[1] "75 0.06"03:43:54 UMAP embedding parameters a = 1.715 b = 0.8526
03:43:54 Read 1203 rows and found 38 numeric columns
03:43:54 Using Annoy for neighbor search, n_neighbors = 75
03:43:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:43:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c4acea5
03:43:55 Searching Annoy index using 1 thread, search_k = 7500
03:43:55 Annoy recall = 100%
03:44:00 Commencing smooth kNN distance calibration using 1 thread
03:44:11 Initializing from normalized Laplacian + noise
03:44:11 Commencing optimization for 500 epochs, with 107306 positive edges
03:44:19 Optimization finished
[1] "75 0.07"03:44:20 UMAP embedding parameters a = 1.68 b = 0.8631
03:44:20 Read 1203 rows and found 38 numeric columns
03:44:20 Using Annoy for neighbor search, n_neighbors = 75
03:44:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:44:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753080e3f
03:44:20 Searching Annoy index using 1 thread, search_k = 7500
03:44:21 Annoy recall = 100%
03:44:26 Commencing smooth kNN distance calibration using 1 thread
03:44:36 Initializing from normalized Laplacian + noise
03:44:36 Commencing optimization for 500 epochs, with 107306 positive edges
03:44:45 Optimization finished
[1] "75 0.08"03:44:45 UMAP embedding parameters a = 1.645 b = 0.8737
03:44:45 Read 1203 rows and found 38 numeric columns
03:44:45 Using Annoy for neighbor search, n_neighbors = 75
03:44:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:44:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a0abf2d
03:44:46 Searching Annoy index using 1 thread, search_k = 7500
03:44:46 Annoy recall = 100%
03:44:51 Commencing smooth kNN distance calibration using 1 thread
03:45:01 Initializing from normalized Laplacian + noise
03:45:01 Commencing optimization for 500 epochs, with 107306 positive edges
03:45:10 Optimization finished
[1] "75 0.09"03:45:10 UMAP embedding parameters a = 1.611 b = 0.8844
03:45:10 Read 1203 rows and found 38 numeric columns
03:45:10 Using Annoy for neighbor search, n_neighbors = 75
03:45:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:45:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e8d2080
03:45:11 Searching Annoy index using 1 thread, search_k = 7500
03:45:11 Annoy recall = 100%
03:45:16 Commencing smooth kNN distance calibration using 1 thread
03:45:27 Initializing from normalized Laplacian + noise
03:45:27 Commencing optimization for 500 epochs, with 107306 positive edges
03:45:36 Optimization finished
[1] "75 0.1"03:45:36 UMAP embedding parameters a = 1.577 b = 0.8951
03:45:36 Read 1203 rows and found 38 numeric columns
03:45:36 Using Annoy for neighbor search, n_neighbors = 75
03:45:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:45:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874310d924
03:45:36 Searching Annoy index using 1 thread, search_k = 7500
03:45:37 Annoy recall = 100%
03:45:42 Commencing smooth kNN distance calibration using 1 thread
03:45:52 Initializing from normalized Laplacian + noise
03:45:52 Commencing optimization for 500 epochs, with 107306 positive edges
03:46:01 Optimization finished
[1] "75 0.11"03:46:01 UMAP embedding parameters a = 1.544 b = 0.9058
03:46:01 Read 1203 rows and found 38 numeric columns
03:46:01 Using Annoy for neighbor search, n_neighbors = 75
03:46:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:46:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bd900c6
03:46:02 Searching Annoy index using 1 thread, search_k = 7500
03:46:02 Annoy recall = 100%
03:46:07 Commencing smooth kNN distance calibration using 1 thread
03:46:18 Initializing from normalized Laplacian + noise
03:46:18 Commencing optimization for 500 epochs, with 107306 positive edges
03:46:26 Optimization finished
[1] "75 0.12"03:46:26 UMAP embedding parameters a = 1.51 b = 0.9165
03:46:27 Read 1203 rows and found 38 numeric columns
03:46:27 Using Annoy for neighbor search, n_neighbors = 75
03:46:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:46:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fed039
03:46:27 Searching Annoy index using 1 thread, search_k = 7500
03:46:27 Annoy recall = 100%
03:46:33 Commencing smooth kNN distance calibration using 1 thread
03:46:43 Initializing from normalized Laplacian + noise
03:46:43 Commencing optimization for 500 epochs, with 107306 positive edges
03:46:52 Optimization finished
[1] "75 0.13"03:46:52 UMAP embedding parameters a = 1.478 b = 0.9272
03:46:52 Read 1203 rows and found 38 numeric columns
03:46:52 Using Annoy for neighbor search, n_neighbors = 75
03:46:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:46:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743d91e6e
03:46:52 Searching Annoy index using 1 thread, search_k = 7500
03:46:53 Annoy recall = 100%
03:46:58 Commencing smooth kNN distance calibration using 1 thread
03:47:08 Initializing from normalized Laplacian + noise
03:47:08 Commencing optimization for 500 epochs, with 107306 positive edges
03:47:17 Optimization finished
[1] "75 0.14"03:47:17 UMAP embedding parameters a = 1.446 b = 0.938
03:47:17 Read 1203 rows and found 38 numeric columns
03:47:17 Using Annoy for neighbor search, n_neighbors = 75
03:47:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:47:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87263cfbed
03:47:18 Searching Annoy index using 1 thread, search_k = 7500
03:47:18 Annoy recall = 100%
03:47:23 Commencing smooth kNN distance calibration using 1 thread
03:47:34 Initializing from normalized Laplacian + noise
03:47:34 Commencing optimization for 500 epochs, with 107306 positive edges
03:47:42 Optimization finished
[1] "75 0.15"03:47:43 UMAP embedding parameters a = 1.414 b = 0.9488
03:47:43 Read 1203 rows and found 38 numeric columns
03:47:43 Using Annoy for neighbor search, n_neighbors = 75
03:47:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:47:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87167771ed
03:47:43 Searching Annoy index using 1 thread, search_k = 7500
03:47:44 Annoy recall = 100%
03:47:49 Commencing smooth kNN distance calibration using 1 thread
03:47:59 Initializing from normalized Laplacian + noise
03:47:59 Commencing optimization for 500 epochs, with 107306 positive edges
03:48:08 Optimization finished
[1] "75 0.16"03:48:08 UMAP embedding parameters a = 1.383 b = 0.9596
03:48:08 Read 1203 rows and found 38 numeric columns
03:48:08 Using Annoy for neighbor search, n_neighbors = 75
03:48:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:48:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873497c668
03:48:08 Searching Annoy index using 1 thread, search_k = 7500
03:48:09 Annoy recall = 100%
03:48:14 Commencing smooth kNN distance calibration using 1 thread
03:48:24 Initializing from normalized Laplacian + noise
03:48:24 Commencing optimization for 500 epochs, with 107306 positive edges
03:48:33 Optimization finished
[1] "75 0.17"03:48:33 UMAP embedding parameters a = 1.352 b = 0.9704
03:48:33 Read 1203 rows and found 38 numeric columns
03:48:33 Using Annoy for neighbor search, n_neighbors = 75
03:48:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:48:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732be3c61
03:48:34 Searching Annoy index using 1 thread, search_k = 7500
03:48:34 Annoy recall = 100%
03:48:40 Commencing smooth kNN distance calibration using 1 thread
03:48:50 Initializing from normalized Laplacian + noise
03:48:50 Commencing optimization for 500 epochs, with 107306 positive edges
03:48:59 Optimization finished
[1] "75 0.18"03:48:59 UMAP embedding parameters a = 1.321 b = 0.9813
03:48:59 Read 1203 rows and found 38 numeric columns
03:48:59 Using Annoy for neighbor search, n_neighbors = 75
03:48:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:48:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873804d16f
03:48:59 Searching Annoy index using 1 thread, search_k = 7500
03:49:00 Annoy recall = 100%
03:49:05 Commencing smooth kNN distance calibration using 1 thread
03:49:15 Initializing from normalized Laplacian + noise
03:49:15 Commencing optimization for 500 epochs, with 107306 positive edges
03:49:24 Optimization finished
[1] "75 0.19"03:49:24 UMAP embedding parameters a = 1.292 b = 0.9921
03:49:24 Read 1203 rows and found 38 numeric columns
03:49:24 Using Annoy for neighbor search, n_neighbors = 75
03:49:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:49:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773052c71
03:49:25 Searching Annoy index using 1 thread, search_k = 7500
03:49:25 Annoy recall = 100%
03:49:30 Commencing smooth kNN distance calibration using 1 thread
03:49:41 Initializing from normalized Laplacian + noise
03:49:41 Commencing optimization for 500 epochs, with 107306 positive edges
03:49:49 Optimization finished
[1] "75 0.2"03:49:50 UMAP embedding parameters a = 1.262 b = 1.003
03:49:50 Read 1203 rows and found 38 numeric columns
03:49:50 Using Annoy for neighbor search, n_neighbors = 75
03:49:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:49:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752cca29b
03:49:50 Searching Annoy index using 1 thread, search_k = 7500
03:49:51 Annoy recall = 100%
03:49:56 Commencing smooth kNN distance calibration using 1 thread
03:50:06 Initializing from normalized Laplacian + noise
03:50:06 Commencing optimization for 500 epochs, with 107306 positive edges
03:50:15 Optimization finished
[1] "76 0"03:50:15 UMAP embedding parameters a = 1.933 b = 0.7905
03:50:15 Read 1203 rows and found 38 numeric columns
03:50:15 Using Annoy for neighbor search, n_neighbors = 76
03:50:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:50:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732c02a2c
03:50:15 Searching Annoy index using 1 thread, search_k = 7600
03:50:16 Annoy recall = 100%
03:50:21 Commencing smooth kNN distance calibration using 1 thread
03:50:32 Initializing from normalized Laplacian + noise
03:50:32 Commencing optimization for 500 epochs, with 108620 positive edges
03:50:40 Optimization finished
[1] "76 0.01"03:50:41 UMAP embedding parameters a = 1.896 b = 0.8006
03:50:41 Read 1203 rows and found 38 numeric columns
03:50:41 Using Annoy for neighbor search, n_neighbors = 76
03:50:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:50:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772e3ad52
03:50:41 Searching Annoy index using 1 thread, search_k = 7600
03:50:42 Annoy recall = 100%
03:50:47 Commencing smooth kNN distance calibration using 1 thread
03:50:57 Initializing from normalized Laplacian + noise
03:50:57 Commencing optimization for 500 epochs, with 108620 positive edges
03:51:06 Optimization finished
[1] "76 0.02"03:51:06 UMAP embedding parameters a = 1.859 b = 0.8109
03:51:06 Read 1203 rows and found 38 numeric columns
03:51:06 Using Annoy for neighbor search, n_neighbors = 76
03:51:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:51:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87724aefb2
03:51:06 Searching Annoy index using 1 thread, search_k = 7600
03:51:07 Annoy recall = 100%
03:51:12 Commencing smooth kNN distance calibration using 1 thread
03:51:23 Initializing from normalized Laplacian + noise
03:51:23 Commencing optimization for 500 epochs, with 108620 positive edges
03:51:31 Optimization finished
[1] "76 0.03"03:51:31 UMAP embedding parameters a = 1.822 b = 0.8212
03:51:31 Read 1203 rows and found 38 numeric columns
03:51:31 Using Annoy for neighbor search, n_neighbors = 76
03:51:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:51:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ad23382
03:51:32 Searching Annoy index using 1 thread, search_k = 7600
03:51:32 Annoy recall = 100%
03:51:38 Commencing smooth kNN distance calibration using 1 thread
03:51:48 Initializing from normalized Laplacian + noise
03:51:48 Commencing optimization for 500 epochs, with 108620 positive edges
03:51:57 Optimization finished
[1] "76 0.04"03:51:57 UMAP embedding parameters a = 1.786 b = 0.8316
03:51:57 Read 1203 rows and found 38 numeric columns
03:51:57 Using Annoy for neighbor search, n_neighbors = 76
03:51:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:51:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b1d85e2
03:51:58 Searching Annoy index using 1 thread, search_k = 7600
03:51:58 Annoy recall = 100%
03:52:03 Commencing smooth kNN distance calibration using 1 thread
03:52:14 Initializing from normalized Laplacian + noise
03:52:14 Commencing optimization for 500 epochs, with 108620 positive edges
03:52:22 Optimization finished
[1] "76 0.05"03:52:23 UMAP embedding parameters a = 1.75 b = 0.8421
03:52:23 Read 1203 rows and found 38 numeric columns
03:52:23 Using Annoy for neighbor search, n_neighbors = 76
03:52:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:52:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874763855a
03:52:23 Searching Annoy index using 1 thread, search_k = 7600
03:52:24 Annoy recall = 100%
03:52:29 Commencing smooth kNN distance calibration using 1 thread
03:52:39 Initializing from normalized Laplacian + noise
03:52:39 Commencing optimization for 500 epochs, with 108620 positive edges
03:52:48 Optimization finished
[1] "76 0.06"03:52:48 UMAP embedding parameters a = 1.715 b = 0.8526
03:52:48 Read 1203 rows and found 38 numeric columns
03:52:48 Using Annoy for neighbor search, n_neighbors = 76
03:52:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:52:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a579cc9
03:52:49 Searching Annoy index using 1 thread, search_k = 7600
03:52:49 Annoy recall = 100%
03:52:54 Commencing smooth kNN distance calibration using 1 thread
03:53:05 Initializing from normalized Laplacian + noise
03:53:05 Commencing optimization for 500 epochs, with 108620 positive edges
03:53:13 Optimization finished
[1] "76 0.07"03:53:14 UMAP embedding parameters a = 1.68 b = 0.8631
03:53:14 Read 1203 rows and found 38 numeric columns
03:53:14 Using Annoy for neighbor search, n_neighbors = 76
03:53:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:53:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b46d928
03:53:14 Searching Annoy index using 1 thread, search_k = 7600
03:53:15 Annoy recall = 100%
03:53:20 Commencing smooth kNN distance calibration using 1 thread
03:53:30 Initializing from normalized Laplacian + noise
03:53:30 Commencing optimization for 500 epochs, with 108620 positive edges
03:53:39 Optimization finished
[1] "76 0.08"03:53:39 UMAP embedding parameters a = 1.645 b = 0.8737
03:53:39 Read 1203 rows and found 38 numeric columns
03:53:39 Using Annoy for neighbor search, n_neighbors = 76
03:53:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:53:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b8418ec
03:53:40 Searching Annoy index using 1 thread, search_k = 7600
03:53:40 Annoy recall = 100%
03:53:45 Commencing smooth kNN distance calibration using 1 thread
03:53:56 Initializing from normalized Laplacian + noise
03:53:56 Commencing optimization for 500 epochs, with 108620 positive edges
03:54:05 Optimization finished
[1] "76 0.09"03:54:05 UMAP embedding parameters a = 1.611 b = 0.8844
03:54:05 Read 1203 rows and found 38 numeric columns
03:54:05 Using Annoy for neighbor search, n_neighbors = 76
03:54:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:54:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a68cf98
03:54:05 Searching Annoy index using 1 thread, search_k = 7600
03:54:06 Annoy recall = 100%
03:54:11 Commencing smooth kNN distance calibration using 1 thread
03:54:21 Initializing from normalized Laplacian + noise
03:54:21 Commencing optimization for 500 epochs, with 108620 positive edges
03:54:30 Optimization finished
[1] "76 0.1"03:54:30 UMAP embedding parameters a = 1.577 b = 0.8951
03:54:30 Read 1203 rows and found 38 numeric columns
03:54:30 Using Annoy for neighbor search, n_neighbors = 76
03:54:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:54:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874dbc1b77
03:54:31 Searching Annoy index using 1 thread, search_k = 7600
03:54:31 Annoy recall = 100%
03:54:37 Commencing smooth kNN distance calibration using 1 thread
03:54:47 Initializing from normalized Laplacian + noise
03:54:47 Commencing optimization for 500 epochs, with 108620 positive edges
03:54:56 Optimization finished
[1] "76 0.11"03:54:56 UMAP embedding parameters a = 1.544 b = 0.9058
03:54:56 Read 1203 rows and found 38 numeric columns
03:54:56 Using Annoy for neighbor search, n_neighbors = 76
03:54:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:54:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c56e4be
03:54:56 Searching Annoy index using 1 thread, search_k = 7600
03:54:57 Annoy recall = 100%
03:55:02 Commencing smooth kNN distance calibration using 1 thread
03:55:13 Initializing from normalized Laplacian + noise
03:55:13 Commencing optimization for 500 epochs, with 108620 positive edges
03:55:21 Optimization finished
[1] "76 0.12"03:55:22 UMAP embedding parameters a = 1.51 b = 0.9165
03:55:22 Read 1203 rows and found 38 numeric columns
03:55:22 Using Annoy for neighbor search, n_neighbors = 76
03:55:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:55:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733db1e4f
03:55:22 Searching Annoy index using 1 thread, search_k = 7600
03:55:23 Annoy recall = 100%
03:55:28 Commencing smooth kNN distance calibration using 1 thread
03:55:38 Initializing from normalized Laplacian + noise
03:55:38 Commencing optimization for 500 epochs, with 108620 positive edges
03:55:47 Optimization finished
[1] "76 0.13"03:55:47 UMAP embedding parameters a = 1.478 b = 0.9272
03:55:47 Read 1203 rows and found 38 numeric columns
03:55:47 Using Annoy for neighbor search, n_neighbors = 76
03:55:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:55:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87603a8803
03:55:48 Searching Annoy index using 1 thread, search_k = 7600
03:55:48 Annoy recall = 100%
03:55:53 Commencing smooth kNN distance calibration using 1 thread
03:56:04 Initializing from normalized Laplacian + noise
03:56:04 Commencing optimization for 500 epochs, with 108620 positive edges
03:56:13 Optimization finished
[1] "76 0.14"03:56:13 UMAP embedding parameters a = 1.446 b = 0.938
03:56:13 Read 1203 rows and found 38 numeric columns
03:56:13 Using Annoy for neighbor search, n_neighbors = 76
03:56:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:56:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b63d4c6
03:56:13 Searching Annoy index using 1 thread, search_k = 7600
03:56:14 Annoy recall = 100%
03:56:19 Commencing smooth kNN distance calibration using 1 thread
03:56:29 Initializing from normalized Laplacian + noise
03:56:30 Commencing optimization for 500 epochs, with 108620 positive edges
03:56:38 Optimization finished
[1] "76 0.15"03:56:38 UMAP embedding parameters a = 1.414 b = 0.9488
03:56:38 Read 1203 rows and found 38 numeric columns
03:56:38 Using Annoy for neighbor search, n_neighbors = 76
03:56:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:56:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b40a138
03:56:39 Searching Annoy index using 1 thread, search_k = 7600
03:56:39 Annoy recall = 100%
03:56:45 Commencing smooth kNN distance calibration using 1 thread
03:56:55 Initializing from normalized Laplacian + noise
03:56:55 Commencing optimization for 500 epochs, with 108620 positive edges
03:57:04 Optimization finished
[1] "76 0.16"03:57:04 UMAP embedding parameters a = 1.383 b = 0.9596
03:57:04 Read 1203 rows and found 38 numeric columns
03:57:04 Using Annoy for neighbor search, n_neighbors = 76
03:57:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:57:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c8556a8
03:57:05 Searching Annoy index using 1 thread, search_k = 7600
03:57:05 Annoy recall = 100%
03:57:11 Commencing smooth kNN distance calibration using 1 thread
03:57:21 Initializing from normalized Laplacian + noise
03:57:21 Commencing optimization for 500 epochs, with 108620 positive edges
03:57:30 Optimization finished
[1] "76 0.17"03:57:30 UMAP embedding parameters a = 1.352 b = 0.9704
03:57:30 Read 1203 rows and found 38 numeric columns
03:57:30 Using Annoy for neighbor search, n_neighbors = 76
03:57:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:57:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e6be305
03:57:30 Searching Annoy index using 1 thread, search_k = 7600
03:57:31 Annoy recall = 100%
03:57:36 Commencing smooth kNN distance calibration using 1 thread
03:57:47 Initializing from normalized Laplacian + noise
03:57:47 Commencing optimization for 500 epochs, with 108620 positive edges
03:57:55 Optimization finished
[1] "76 0.18"03:57:56 UMAP embedding parameters a = 1.321 b = 0.9813
03:57:56 Read 1203 rows and found 38 numeric columns
03:57:56 Using Annoy for neighbor search, n_neighbors = 76
03:57:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:57:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87654b6066
03:57:56 Searching Annoy index using 1 thread, search_k = 7600
03:57:57 Annoy recall = 100%
03:58:02 Commencing smooth kNN distance calibration using 1 thread
03:58:12 Initializing from normalized Laplacian + noise
03:58:12 Commencing optimization for 500 epochs, with 108620 positive edges
03:58:21 Optimization finished
[1] "76 0.19"03:58:21 UMAP embedding parameters a = 1.292 b = 0.9921
03:58:21 Read 1203 rows and found 38 numeric columns
03:58:21 Using Annoy for neighbor search, n_neighbors = 76
03:58:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:58:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b127729
03:58:22 Searching Annoy index using 1 thread, search_k = 7600
03:58:22 Annoy recall = 100%
03:58:27 Commencing smooth kNN distance calibration using 1 thread
03:58:38 Initializing from normalized Laplacian + noise
03:58:38 Commencing optimization for 500 epochs, with 108620 positive edges
03:58:47 Optimization finished
[1] "76 0.2"03:58:47 UMAP embedding parameters a = 1.262 b = 1.003
03:58:47 Read 1203 rows and found 38 numeric columns
03:58:47 Using Annoy for neighbor search, n_neighbors = 76
03:58:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:58:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87517cbc2a
03:58:47 Searching Annoy index using 1 thread, search_k = 7600
03:58:48 Annoy recall = 100%
03:58:53 Commencing smooth kNN distance calibration using 1 thread
03:59:04 Initializing from normalized Laplacian + noise
03:59:04 Commencing optimization for 500 epochs, with 108620 positive edges
03:59:13 Optimization finished
[1] "77 0"03:59:13 UMAP embedding parameters a = 1.933 b = 0.7905
03:59:13 Read 1203 rows and found 38 numeric columns
03:59:13 Using Annoy for neighbor search, n_neighbors = 77
03:59:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:59:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876124612c
03:59:13 Searching Annoy index using 1 thread, search_k = 7700
03:59:14 Annoy recall = 100%
03:59:19 Commencing smooth kNN distance calibration using 1 thread
03:59:30 Initializing from normalized Laplacian + noise
03:59:30 Commencing optimization for 500 epochs, with 109922 positive edges
03:59:38 Optimization finished
[1] "77 0.01"03:59:38 UMAP embedding parameters a = 1.896 b = 0.8006
03:59:39 Read 1203 rows and found 38 numeric columns
03:59:39 Using Annoy for neighbor search, n_neighbors = 77
03:59:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:59:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c114762
03:59:39 Searching Annoy index using 1 thread, search_k = 7700
03:59:40 Annoy recall = 100%
03:59:45 Commencing smooth kNN distance calibration using 1 thread
03:59:55 Initializing from normalized Laplacian + noise
03:59:55 Commencing optimization for 500 epochs, with 109922 positive edges
04:00:04 Optimization finished
[1] "77 0.02"04:00:04 UMAP embedding parameters a = 1.859 b = 0.8109
04:00:04 Read 1203 rows and found 38 numeric columns
04:00:04 Using Annoy for neighbor search, n_neighbors = 77
04:00:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:00:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871555da98
04:00:05 Searching Annoy index using 1 thread, search_k = 7700
04:00:05 Annoy recall = 100%
04:00:11 Commencing smooth kNN distance calibration using 1 thread
04:00:21 Initializing from normalized Laplacian + noise
04:00:21 Commencing optimization for 500 epochs, with 109922 positive edges
04:00:30 Optimization finished
[1] "77 0.03"04:00:30 UMAP embedding parameters a = 1.822 b = 0.8212
04:00:30 Read 1203 rows and found 38 numeric columns
04:00:30 Using Annoy for neighbor search, n_neighbors = 77
04:00:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:00:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877615d19
04:00:31 Searching Annoy index using 1 thread, search_k = 7700
04:00:31 Annoy recall = 100%
04:00:36 Commencing smooth kNN distance calibration using 1 thread
04:00:47 Initializing from normalized Laplacian + noise
04:00:47 Commencing optimization for 500 epochs, with 109922 positive edges
04:00:56 Optimization finished
[1] "77 0.04"04:00:56 UMAP embedding parameters a = 1.786 b = 0.8316
04:00:56 Read 1203 rows and found 38 numeric columns
04:00:56 Using Annoy for neighbor search, n_neighbors = 77
04:00:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:00:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871288b94f
04:00:56 Searching Annoy index using 1 thread, search_k = 7700
04:00:57 Annoy recall = 100%
04:01:02 Commencing smooth kNN distance calibration using 1 thread
04:01:13 Initializing from normalized Laplacian + noise
04:01:13 Commencing optimization for 500 epochs, with 109922 positive edges
04:01:22 Optimization finished
[1] "77 0.05"04:01:22 UMAP embedding parameters a = 1.75 b = 0.8421
04:01:22 Read 1203 rows and found 38 numeric columns
04:01:22 Using Annoy for neighbor search, n_neighbors = 77
04:01:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:01:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749eda100
04:01:22 Searching Annoy index using 1 thread, search_k = 7700
04:01:23 Annoy recall = 100%
04:01:28 Commencing smooth kNN distance calibration using 1 thread
04:01:39 Initializing from normalized Laplacian + noise
04:01:39 Commencing optimization for 500 epochs, with 109922 positive edges
04:01:47 Optimization finished
[1] "77 0.06"04:01:48 UMAP embedding parameters a = 1.715 b = 0.8526
04:01:48 Read 1203 rows and found 38 numeric columns
04:01:48 Using Annoy for neighbor search, n_neighbors = 77
04:01:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:01:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a1f997a
04:01:48 Searching Annoy index using 1 thread, search_k = 7700
04:01:49 Annoy recall = 100%
04:01:54 Commencing smooth kNN distance calibration using 1 thread
04:02:04 Initializing from normalized Laplacian + noise
04:02:04 Commencing optimization for 500 epochs, with 109922 positive edges
04:02:13 Optimization finished
[1] "77 0.07"04:02:14 UMAP embedding parameters a = 1.68 b = 0.8631
04:02:14 Read 1203 rows and found 38 numeric columns
04:02:14 Using Annoy for neighbor search, n_neighbors = 77
04:02:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:02:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a8d8abf
04:02:14 Searching Annoy index using 1 thread, search_k = 7700
04:02:14 Annoy recall = 100%
04:02:20 Commencing smooth kNN distance calibration using 1 thread
04:02:30 Initializing from normalized Laplacian + noise
04:02:30 Commencing optimization for 500 epochs, with 109922 positive edges
04:02:39 Optimization finished
[1] "77 0.08"04:02:39 UMAP embedding parameters a = 1.645 b = 0.8737
04:02:39 Read 1203 rows and found 38 numeric columns
04:02:39 Using Annoy for neighbor search, n_neighbors = 77
04:02:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:02:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cf2cd71
04:02:40 Searching Annoy index using 1 thread, search_k = 7700
04:02:40 Annoy recall = 100%
04:02:46 Commencing smooth kNN distance calibration using 1 thread
04:02:56 Initializing from normalized Laplacian + noise
04:02:56 Commencing optimization for 500 epochs, with 109922 positive edges
04:03:05 Optimization finished
[1] "77 0.09"04:03:05 UMAP embedding parameters a = 1.611 b = 0.8844
04:03:05 Read 1203 rows and found 38 numeric columns
04:03:05 Using Annoy for neighbor search, n_neighbors = 77
04:03:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:03:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cec3c15
04:03:06 Searching Annoy index using 1 thread, search_k = 7700
04:03:06 Annoy recall = 100%
04:03:11 Commencing smooth kNN distance calibration using 1 thread
04:03:22 Initializing from normalized Laplacian + noise
04:03:22 Commencing optimization for 500 epochs, with 109922 positive edges
04:03:31 Optimization finished
[1] "77 0.1"04:03:31 UMAP embedding parameters a = 1.577 b = 0.8951
04:03:31 Read 1203 rows and found 38 numeric columns
04:03:31 Using Annoy for neighbor search, n_neighbors = 77
04:03:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:03:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d4db4eb
04:03:32 Searching Annoy index using 1 thread, search_k = 7700
04:03:32 Annoy recall = 100%
04:03:37 Commencing smooth kNN distance calibration using 1 thread
04:03:48 Initializing from normalized Laplacian + noise
04:03:48 Commencing optimization for 500 epochs, with 109922 positive edges
04:03:57 Optimization finished
[1] "77 0.11"04:03:57 UMAP embedding parameters a = 1.544 b = 0.9058
04:03:57 Read 1203 rows and found 38 numeric columns
04:03:57 Using Annoy for neighbor search, n_neighbors = 77
04:03:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:03:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fd67ac4
04:03:57 Searching Annoy index using 1 thread, search_k = 7700
04:03:58 Annoy recall = 100%
04:04:03 Commencing smooth kNN distance calibration using 1 thread
04:04:14 Initializing from normalized Laplacian + noise
04:04:14 Commencing optimization for 500 epochs, with 109922 positive edges
04:04:23 Optimization finished
[1] "77 0.12"04:04:23 UMAP embedding parameters a = 1.51 b = 0.9165
04:04:23 Read 1203 rows and found 38 numeric columns
04:04:23 Using Annoy for neighbor search, n_neighbors = 77
04:04:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:04:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f372bc8
04:04:23 Searching Annoy index using 1 thread, search_k = 7700
04:04:24 Annoy recall = 100%
04:04:29 Commencing smooth kNN distance calibration using 1 thread
04:04:40 Initializing from normalized Laplacian + noise
04:04:40 Commencing optimization for 500 epochs, with 109922 positive edges
04:04:48 Optimization finished
[1] "77 0.13"04:04:49 UMAP embedding parameters a = 1.478 b = 0.9272
04:04:49 Read 1203 rows and found 38 numeric columns
04:04:49 Using Annoy for neighbor search, n_neighbors = 77
04:04:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:04:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87581fe86d
04:04:49 Searching Annoy index using 1 thread, search_k = 7700
04:04:50 Annoy recall = 100%
04:04:55 Commencing smooth kNN distance calibration using 1 thread
04:05:06 Initializing from normalized Laplacian + noise
04:05:06 Commencing optimization for 500 epochs, with 109922 positive edges
04:05:14 Optimization finished
[1] "77 0.14"04:05:15 UMAP embedding parameters a = 1.446 b = 0.938
04:05:15 Read 1203 rows and found 38 numeric columns
04:05:15 Using Annoy for neighbor search, n_neighbors = 77
04:05:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:05:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871af400a6
04:05:15 Searching Annoy index using 1 thread, search_k = 7700
04:05:16 Annoy recall = 100%
04:05:21 Commencing smooth kNN distance calibration using 1 thread
04:05:32 Initializing from normalized Laplacian + noise
04:05:32 Commencing optimization for 500 epochs, with 109922 positive edges
04:05:40 Optimization finished
[1] "77 0.15"04:05:41 UMAP embedding parameters a = 1.414 b = 0.9488
04:05:41 Read 1203 rows and found 38 numeric columns
04:05:41 Using Annoy for neighbor search, n_neighbors = 77
04:05:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:05:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87469ab122
04:05:41 Searching Annoy index using 1 thread, search_k = 7700
04:05:42 Annoy recall = 100%
04:05:47 Commencing smooth kNN distance calibration using 1 thread
04:05:57 Initializing from normalized Laplacian + noise
04:05:57 Commencing optimization for 500 epochs, with 109922 positive edges
04:06:06 Optimization finished
[1] "77 0.16"04:06:06 UMAP embedding parameters a = 1.383 b = 0.9596
04:06:06 Read 1203 rows and found 38 numeric columns
04:06:06 Using Annoy for neighbor search, n_neighbors = 77
04:06:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:06:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732778537
04:06:07 Searching Annoy index using 1 thread, search_k = 7700
04:06:07 Annoy recall = 100%
04:06:13 Commencing smooth kNN distance calibration using 1 thread
04:06:23 Initializing from normalized Laplacian + noise
04:06:23 Commencing optimization for 500 epochs, with 109922 positive edges
04:06:32 Optimization finished
[1] "77 0.17"04:06:33 UMAP embedding parameters a = 1.352 b = 0.9704
04:06:33 Read 1203 rows and found 38 numeric columns
04:06:33 Using Annoy for neighbor search, n_neighbors = 77
04:06:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:06:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87363ad9ce
04:06:33 Searching Annoy index using 1 thread, search_k = 7700
04:06:34 Annoy recall = 100%
04:06:39 Commencing smooth kNN distance calibration using 1 thread
04:06:49 Initializing from normalized Laplacian + noise
04:06:49 Commencing optimization for 500 epochs, with 109922 positive edges
04:06:58 Optimization finished
[1] "77 0.18"04:06:58 UMAP embedding parameters a = 1.321 b = 0.9813
04:06:58 Read 1203 rows and found 38 numeric columns
04:06:58 Using Annoy for neighbor search, n_neighbors = 77
04:06:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:06:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87121eca0e
04:06:59 Searching Annoy index using 1 thread, search_k = 7700
04:06:59 Annoy recall = 100%
04:07:05 Commencing smooth kNN distance calibration using 1 thread
04:07:15 Initializing from normalized Laplacian + noise
04:07:15 Commencing optimization for 500 epochs, with 109922 positive edges
04:07:24 Optimization finished
[1] "77 0.19"04:07:24 UMAP embedding parameters a = 1.292 b = 0.9921
04:07:24 Read 1203 rows and found 38 numeric columns
04:07:24 Using Annoy for neighbor search, n_neighbors = 77
04:07:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:07:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ce054cf
04:07:25 Searching Annoy index using 1 thread, search_k = 7700
04:07:25 Annoy recall = 100%
04:07:31 Commencing smooth kNN distance calibration using 1 thread
04:07:41 Initializing from normalized Laplacian + noise
04:07:41 Commencing optimization for 500 epochs, with 109922 positive edges
04:07:50 Optimization finished
[1] "77 0.2"04:07:50 UMAP embedding parameters a = 1.262 b = 1.003
04:07:50 Read 1203 rows and found 38 numeric columns
04:07:50 Using Annoy for neighbor search, n_neighbors = 77
04:07:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:07:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f6f545
04:07:51 Searching Annoy index using 1 thread, search_k = 7700
04:07:51 Annoy recall = 100%
04:07:57 Commencing smooth kNN distance calibration using 1 thread
04:08:07 Initializing from normalized Laplacian + noise
04:08:07 Commencing optimization for 500 epochs, with 109922 positive edges
04:08:16 Optimization finished
[1] "78 0"04:08:16 UMAP embedding parameters a = 1.933 b = 0.7905
04:08:16 Read 1203 rows and found 38 numeric columns
04:08:16 Using Annoy for neighbor search, n_neighbors = 78
04:08:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:08:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e75aecc
04:08:17 Searching Annoy index using 1 thread, search_k = 7800
04:08:17 Annoy recall = 100%
04:08:23 Commencing smooth kNN distance calibration using 1 thread
04:08:33 Initializing from normalized Laplacian + noise
04:08:33 Commencing optimization for 500 epochs, with 111252 positive edges
04:08:42 Optimization finished
[1] "78 0.01"04:08:43 UMAP embedding parameters a = 1.896 b = 0.8006
04:08:43 Read 1203 rows and found 38 numeric columns
04:08:43 Using Annoy for neighbor search, n_neighbors = 78
04:08:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:08:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bb731e
04:08:43 Searching Annoy index using 1 thread, search_k = 7800
04:08:44 Annoy recall = 100%
04:08:49 Commencing smooth kNN distance calibration using 1 thread
04:08:59 Initializing from normalized Laplacian + noise
04:08:59 Commencing optimization for 500 epochs, with 111252 positive edges
04:09:08 Optimization finished
[1] "78 0.02"04:09:08 UMAP embedding parameters a = 1.859 b = 0.8109
04:09:08 Read 1203 rows and found 38 numeric columns
04:09:08 Using Annoy for neighbor search, n_neighbors = 78
04:09:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:09:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764317d49
04:09:09 Searching Annoy index using 1 thread, search_k = 7800
04:09:09 Annoy recall = 100%
04:09:15 Commencing smooth kNN distance calibration using 1 thread
04:09:25 Initializing from normalized Laplacian + noise
04:09:25 Commencing optimization for 500 epochs, with 111252 positive edges
04:09:34 Optimization finished
[1] "78 0.03"04:09:35 UMAP embedding parameters a = 1.822 b = 0.8212
04:09:35 Read 1203 rows and found 38 numeric columns
04:09:35 Using Annoy for neighbor search, n_neighbors = 78
04:09:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:09:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779d98392
04:09:35 Searching Annoy index using 1 thread, search_k = 7800
04:09:36 Annoy recall = 100%
04:09:41 Commencing smooth kNN distance calibration using 1 thread
04:09:52 Initializing from normalized Laplacian + noise
04:09:52 Commencing optimization for 500 epochs, with 111252 positive edges
04:10:00 Optimization finished
[1] "78 0.04"04:10:01 UMAP embedding parameters a = 1.786 b = 0.8316
04:10:01 Read 1203 rows and found 38 numeric columns
04:10:01 Using Annoy for neighbor search, n_neighbors = 78
04:10:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:10:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bfc1456
04:10:01 Searching Annoy index using 1 thread, search_k = 7800
04:10:02 Annoy recall = 100%
04:10:07 Commencing smooth kNN distance calibration using 1 thread
04:10:18 Initializing from normalized Laplacian + noise
04:10:18 Commencing optimization for 500 epochs, with 111252 positive edges
04:10:26 Optimization finished
[1] "78 0.05"04:10:27 UMAP embedding parameters a = 1.75 b = 0.8421
04:10:27 Read 1203 rows and found 38 numeric columns
04:10:27 Using Annoy for neighbor search, n_neighbors = 78
04:10:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:10:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730b6d3f1
04:10:27 Searching Annoy index using 1 thread, search_k = 7800
04:10:28 Annoy recall = 100%
04:10:33 Commencing smooth kNN distance calibration using 1 thread
04:10:44 Initializing from normalized Laplacian + noise
04:10:44 Commencing optimization for 500 epochs, with 111252 positive edges
04:10:53 Optimization finished
[1] "78 0.06"04:10:53 UMAP embedding parameters a = 1.715 b = 0.8526
04:10:53 Read 1203 rows and found 38 numeric columns
04:10:53 Using Annoy for neighbor search, n_neighbors = 78
04:10:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:10:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878456698
04:10:53 Searching Annoy index using 1 thread, search_k = 7800
04:10:54 Annoy recall = 100%
04:10:59 Commencing smooth kNN distance calibration using 1 thread
04:11:10 Initializing from normalized Laplacian + noise
04:11:10 Commencing optimization for 500 epochs, with 111252 positive edges
04:11:19 Optimization finished
[1] "78 0.07"04:11:19 UMAP embedding parameters a = 1.68 b = 0.8631
04:11:19 Read 1203 rows and found 38 numeric columns
04:11:19 Using Annoy for neighbor search, n_neighbors = 78
04:11:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:11:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87714774bc
04:11:19 Searching Annoy index using 1 thread, search_k = 7800
04:11:20 Annoy recall = 100%
04:11:25 Commencing smooth kNN distance calibration using 1 thread
04:11:36 Initializing from normalized Laplacian + noise
04:11:36 Commencing optimization for 500 epochs, with 111252 positive edges
04:11:45 Optimization finished
[1] "78 0.08"04:11:45 UMAP embedding parameters a = 1.645 b = 0.8737
04:11:45 Read 1203 rows and found 38 numeric columns
04:11:45 Using Annoy for neighbor search, n_neighbors = 78
04:11:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:11:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872bc94b1a
04:11:46 Searching Annoy index using 1 thread, search_k = 7800
04:11:46 Annoy recall = 100%
04:11:52 Commencing smooth kNN distance calibration using 1 thread
04:12:02 Initializing from normalized Laplacian + noise
04:12:03 Commencing optimization for 500 epochs, with 111252 positive edges
04:12:11 Optimization finished
[1] "78 0.09"04:12:12 UMAP embedding parameters a = 1.611 b = 0.8844
04:12:12 Read 1203 rows and found 38 numeric columns
04:12:12 Using Annoy for neighbor search, n_neighbors = 78
04:12:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:12:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759c222c2
04:12:12 Searching Annoy index using 1 thread, search_k = 7800
04:12:13 Annoy recall = 100%
04:12:18 Commencing smooth kNN distance calibration using 1 thread
04:12:29 Initializing from normalized Laplacian + noise
04:12:29 Commencing optimization for 500 epochs, with 111252 positive edges
04:12:38 Optimization finished
[1] "78 0.1"04:12:38 UMAP embedding parameters a = 1.577 b = 0.8951
04:12:38 Read 1203 rows and found 38 numeric columns
04:12:38 Using Annoy for neighbor search, n_neighbors = 78
04:12:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:12:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87526bd5e8
04:12:38 Searching Annoy index using 1 thread, search_k = 7800
04:12:39 Annoy recall = 100%
04:12:44 Commencing smooth kNN distance calibration using 1 thread
04:12:55 Initializing from normalized Laplacian + noise
04:12:55 Commencing optimization for 500 epochs, with 111252 positive edges
04:13:04 Optimization finished
[1] "78 0.11"04:13:04 UMAP embedding parameters a = 1.544 b = 0.9058
04:13:04 Read 1203 rows and found 38 numeric columns
04:13:04 Using Annoy for neighbor search, n_neighbors = 78
04:13:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:13:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727da927c
04:13:05 Searching Annoy index using 1 thread, search_k = 7800
04:13:05 Annoy recall = 100%
04:13:10 Commencing smooth kNN distance calibration using 1 thread
04:13:21 Initializing from normalized Laplacian + noise
04:13:21 Commencing optimization for 500 epochs, with 111252 positive edges
04:13:30 Optimization finished
[1] "78 0.12"04:13:30 UMAP embedding parameters a = 1.51 b = 0.9165
04:13:30 Read 1203 rows and found 38 numeric columns
04:13:30 Using Annoy for neighbor search, n_neighbors = 78
04:13:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:13:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f17fd5a
04:13:31 Searching Annoy index using 1 thread, search_k = 7800
04:13:31 Annoy recall = 100%
04:13:37 Commencing smooth kNN distance calibration using 1 thread
04:13:48 Initializing from normalized Laplacian + noise
04:13:48 Commencing optimization for 500 epochs, with 111252 positive edges
04:13:57 Optimization finished
[1] "78 0.13"04:13:57 UMAP embedding parameters a = 1.478 b = 0.9272
04:13:57 Read 1203 rows and found 38 numeric columns
04:13:57 Using Annoy for neighbor search, n_neighbors = 78
04:13:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:13:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759cd3302
04:13:57 Searching Annoy index using 1 thread, search_k = 7800
04:13:58 Annoy recall = 100%
04:14:03 Commencing smooth kNN distance calibration using 1 thread
04:14:14 Initializing from normalized Laplacian + noise
04:14:14 Commencing optimization for 500 epochs, with 111252 positive edges
04:14:23 Optimization finished
[1] "78 0.14"04:14:23 UMAP embedding parameters a = 1.446 b = 0.938
04:14:23 Read 1203 rows and found 38 numeric columns
04:14:23 Using Annoy for neighbor search, n_neighbors = 78
04:14:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:14:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a634bcc
04:14:24 Searching Annoy index using 1 thread, search_k = 7800
04:14:24 Annoy recall = 100%
04:14:29 Commencing smooth kNN distance calibration using 1 thread
04:14:40 Initializing from normalized Laplacian + noise
04:14:40 Commencing optimization for 500 epochs, with 111252 positive edges
04:14:49 Optimization finished
[1] "78 0.15"04:14:50 UMAP embedding parameters a = 1.414 b = 0.9488
04:14:50 Read 1203 rows and found 38 numeric columns
04:14:50 Using Annoy for neighbor search, n_neighbors = 78
04:14:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:14:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739059e5a
04:14:50 Searching Annoy index using 1 thread, search_k = 7800
04:14:51 Annoy recall = 100%
04:14:56 Commencing smooth kNN distance calibration using 1 thread
04:15:07 Initializing from normalized Laplacian + noise
04:15:07 Commencing optimization for 500 epochs, with 111252 positive edges
04:15:16 Optimization finished
[1] "78 0.16"04:15:16 UMAP embedding parameters a = 1.383 b = 0.9596
04:15:16 Read 1203 rows and found 38 numeric columns
04:15:16 Using Annoy for neighbor search, n_neighbors = 78
04:15:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:15:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713eccc7c
04:15:16 Searching Annoy index using 1 thread, search_k = 7800
04:15:17 Annoy recall = 100%
04:15:22 Commencing smooth kNN distance calibration using 1 thread
04:15:33 Initializing from normalized Laplacian + noise
04:15:33 Commencing optimization for 500 epochs, with 111252 positive edges
04:15:42 Optimization finished
[1] "78 0.17"04:15:42 UMAP embedding parameters a = 1.352 b = 0.9704
04:15:42 Read 1203 rows and found 38 numeric columns
04:15:42 Using Annoy for neighbor search, n_neighbors = 78
04:15:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:15:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f0d68b
04:15:43 Searching Annoy index using 1 thread, search_k = 7800
04:15:43 Annoy recall = 100%
04:15:49 Commencing smooth kNN distance calibration using 1 thread
04:16:00 Initializing from normalized Laplacian + noise
04:16:00 Commencing optimization for 500 epochs, with 111252 positive edges
04:16:09 Optimization finished
[1] "78 0.18"04:16:09 UMAP embedding parameters a = 1.321 b = 0.9813
04:16:09 Read 1203 rows and found 38 numeric columns
04:16:09 Using Annoy for neighbor search, n_neighbors = 78
04:16:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:16:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775f86bcb
04:16:09 Searching Annoy index using 1 thread, search_k = 7800
04:16:10 Annoy recall = 100%
04:16:15 Commencing smooth kNN distance calibration using 1 thread
04:16:26 Initializing from normalized Laplacian + noise
04:16:26 Commencing optimization for 500 epochs, with 111252 positive edges
04:16:35 Optimization finished
[1] "78 0.19"04:16:35 UMAP embedding parameters a = 1.292 b = 0.9921
04:16:35 Read 1203 rows and found 38 numeric columns
04:16:35 Using Annoy for neighbor search, n_neighbors = 78
04:16:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:16:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720d90892
04:16:35 Searching Annoy index using 1 thread, search_k = 7800
04:16:36 Annoy recall = 100%
04:16:41 Commencing smooth kNN distance calibration using 1 thread
04:16:52 Initializing from normalized Laplacian + noise
04:16:52 Commencing optimization for 500 epochs, with 111252 positive edges
04:17:01 Optimization finished
[1] "78 0.2"04:17:02 UMAP embedding parameters a = 1.262 b = 1.003
04:17:02 Read 1203 rows and found 38 numeric columns
04:17:02 Using Annoy for neighbor search, n_neighbors = 78
04:17:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:17:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723e8b76
04:17:02 Searching Annoy index using 1 thread, search_k = 7800
04:17:03 Annoy recall = 100%
04:17:08 Commencing smooth kNN distance calibration using 1 thread
04:17:19 Initializing from normalized Laplacian + noise
04:17:19 Commencing optimization for 500 epochs, with 111252 positive edges
04:17:28 Optimization finished
[1] "79 0"04:17:28 UMAP embedding parameters a = 1.933 b = 0.7905
04:17:28 Read 1203 rows and found 38 numeric columns
04:17:28 Using Annoy for neighbor search, n_neighbors = 79
04:17:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:17:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725cee68f
04:17:28 Searching Annoy index using 1 thread, search_k = 7900
04:17:29 Annoy recall = 100%
04:17:34 Commencing smooth kNN distance calibration using 1 thread
04:17:45 Initializing from normalized Laplacian + noise
04:17:45 Commencing optimization for 500 epochs, with 112536 positive edges
04:17:54 Optimization finished
[1] "79 0.01"04:17:54 UMAP embedding parameters a = 1.896 b = 0.8006
04:17:54 Read 1203 rows and found 38 numeric columns
04:17:54 Using Annoy for neighbor search, n_neighbors = 79
04:17:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:17:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872010345a
04:17:55 Searching Annoy index using 1 thread, search_k = 7900
04:17:55 Annoy recall = 100%
04:18:01 Commencing smooth kNN distance calibration using 1 thread
04:18:12 Initializing from normalized Laplacian + noise
04:18:12 Commencing optimization for 500 epochs, with 112536 positive edges
04:18:21 Optimization finished
[1] "79 0.02"04:18:21 UMAP embedding parameters a = 1.859 b = 0.8109
04:18:21 Read 1203 rows and found 38 numeric columns
04:18:21 Using Annoy for neighbor search, n_neighbors = 79
04:18:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:18:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a5e73e3
04:18:21 Searching Annoy index using 1 thread, search_k = 7900
04:18:22 Annoy recall = 100%
04:18:27 Commencing smooth kNN distance calibration using 1 thread
04:18:38 Initializing from normalized Laplacian + noise
04:18:38 Commencing optimization for 500 epochs, with 112536 positive edges
04:18:47 Optimization finished
[1] "79 0.03"04:18:47 UMAP embedding parameters a = 1.822 b = 0.8212
04:18:47 Read 1203 rows and found 38 numeric columns
04:18:47 Using Annoy for neighbor search, n_neighbors = 79
04:18:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:18:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740c2e735
04:18:48 Searching Annoy index using 1 thread, search_k = 7900
04:18:48 Annoy recall = 100%
04:18:54 Commencing smooth kNN distance calibration using 1 thread
04:19:05 Initializing from normalized Laplacian + noise
04:19:05 Commencing optimization for 500 epochs, with 112536 positive edges
04:19:14 Optimization finished
[1] "79 0.04"04:19:14 UMAP embedding parameters a = 1.786 b = 0.8316
04:19:14 Read 1203 rows and found 38 numeric columns
04:19:14 Using Annoy for neighbor search, n_neighbors = 79
04:19:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:19:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766aae57c
04:19:14 Searching Annoy index using 1 thread, search_k = 7900
04:19:15 Annoy recall = 100%
04:19:20 Commencing smooth kNN distance calibration using 1 thread
04:19:31 Initializing from normalized Laplacian + noise
04:19:31 Commencing optimization for 500 epochs, with 112536 positive edges
04:19:40 Optimization finished
[1] "79 0.05"04:19:40 UMAP embedding parameters a = 1.75 b = 0.8421
04:19:40 Read 1203 rows and found 38 numeric columns
04:19:40 Using Annoy for neighbor search, n_neighbors = 79
04:19:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:19:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cd5f91a
04:19:41 Searching Annoy index using 1 thread, search_k = 7900
04:19:41 Annoy recall = 100%
04:19:47 Commencing smooth kNN distance calibration using 1 thread
04:19:58 Initializing from normalized Laplacian + noise
04:19:58 Commencing optimization for 500 epochs, with 112536 positive edges
04:20:07 Optimization finished
[1] "79 0.06"04:20:07 UMAP embedding parameters a = 1.715 b = 0.8526
04:20:07 Read 1203 rows and found 38 numeric columns
04:20:07 Using Annoy for neighbor search, n_neighbors = 79
04:20:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:20:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776fdc104
04:20:07 Searching Annoy index using 1 thread, search_k = 7900
04:20:08 Annoy recall = 100%
04:20:13 Commencing smooth kNN distance calibration using 1 thread
04:20:24 Initializing from normalized Laplacian + noise
04:20:24 Commencing optimization for 500 epochs, with 112536 positive edges
04:20:33 Optimization finished
[1] "79 0.07"04:20:33 UMAP embedding parameters a = 1.68 b = 0.8631
04:20:33 Read 1203 rows and found 38 numeric columns
04:20:33 Using Annoy for neighbor search, n_neighbors = 79
04:20:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:20:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778c9af8a
04:20:34 Searching Annoy index using 1 thread, search_k = 7900
04:20:34 Annoy recall = 100%
04:20:40 Commencing smooth kNN distance calibration using 1 thread
04:20:51 Initializing from normalized Laplacian + noise
04:20:51 Commencing optimization for 500 epochs, with 112536 positive edges
04:21:00 Optimization finished
[1] "79 0.08"04:21:00 UMAP embedding parameters a = 1.645 b = 0.8737
04:21:00 Read 1203 rows and found 38 numeric columns
04:21:00 Using Annoy for neighbor search, n_neighbors = 79
04:21:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:21:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759b64de9
04:21:00 Searching Annoy index using 1 thread, search_k = 7900
04:21:01 Annoy recall = 100%
04:21:06 Commencing smooth kNN distance calibration using 1 thread
04:21:17 Initializing from normalized Laplacian + noise
04:21:17 Commencing optimization for 500 epochs, with 112536 positive edges
04:21:26 Optimization finished
[1] "79 0.09"04:21:27 UMAP embedding parameters a = 1.611 b = 0.8844
04:21:27 Read 1203 rows and found 38 numeric columns
04:21:27 Using Annoy for neighbor search, n_neighbors = 79
04:21:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:21:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877af4b649
04:21:27 Searching Annoy index using 1 thread, search_k = 7900
04:21:28 Annoy recall = 100%
04:21:33 Commencing smooth kNN distance calibration using 1 thread
04:21:44 Initializing from normalized Laplacian + noise
04:21:44 Commencing optimization for 500 epochs, with 112536 positive edges
04:21:53 Optimization finished
[1] "79 0.1"04:21:53 UMAP embedding parameters a = 1.577 b = 0.8951
04:21:53 Read 1203 rows and found 38 numeric columns
04:21:53 Using Annoy for neighbor search, n_neighbors = 79
04:21:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:21:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87373f5e57
04:21:53 Searching Annoy index using 1 thread, search_k = 7900
04:21:54 Annoy recall = 100%
04:22:00 Commencing smooth kNN distance calibration using 1 thread
04:22:11 Initializing from normalized Laplacian + noise
04:22:11 Commencing optimization for 500 epochs, with 112536 positive edges
04:22:20 Optimization finished
[1] "79 0.11"04:22:20 UMAP embedding parameters a = 1.544 b = 0.9058
04:22:20 Read 1203 rows and found 38 numeric columns
04:22:20 Using Annoy for neighbor search, n_neighbors = 79
04:22:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:22:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a71c107
04:22:20 Searching Annoy index using 1 thread, search_k = 7900
04:22:21 Annoy recall = 100%
04:22:26 Commencing smooth kNN distance calibration using 1 thread
04:22:37 Initializing from normalized Laplacian + noise
04:22:37 Commencing optimization for 500 epochs, with 112536 positive edges
04:22:46 Optimization finished
[1] "79 0.12"04:22:46 UMAP embedding parameters a = 1.51 b = 0.9165
04:22:46 Read 1203 rows and found 38 numeric columns
04:22:46 Using Annoy for neighbor search, n_neighbors = 79
04:22:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:22:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f263392
04:22:47 Searching Annoy index using 1 thread, search_k = 7900
04:22:47 Annoy recall = 100%
04:22:53 Commencing smooth kNN distance calibration using 1 thread
04:23:04 Initializing from normalized Laplacian + noise
04:23:04 Commencing optimization for 500 epochs, with 112536 positive edges
04:23:13 Optimization finished
[1] "79 0.13"04:23:13 UMAP embedding parameters a = 1.478 b = 0.9272
04:23:13 Read 1203 rows and found 38 numeric columns
04:23:13 Using Annoy for neighbor search, n_neighbors = 79
04:23:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:23:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873118e1e9
04:23:13 Searching Annoy index using 1 thread, search_k = 7900
04:23:14 Annoy recall = 100%
04:23:20 Commencing smooth kNN distance calibration using 1 thread
04:23:30 Initializing from normalized Laplacian + noise
04:23:31 Commencing optimization for 500 epochs, with 112536 positive edges
04:23:39 Optimization finished
[1] "79 0.14"04:23:40 UMAP embedding parameters a = 1.446 b = 0.938
04:23:40 Read 1203 rows and found 38 numeric columns
04:23:40 Using Annoy for neighbor search, n_neighbors = 79
04:23:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:23:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87666dd55e
04:23:40 Searching Annoy index using 1 thread, search_k = 7900
04:23:41 Annoy recall = 100%
04:23:46 Commencing smooth kNN distance calibration using 1 thread
04:23:57 Initializing from normalized Laplacian + noise
04:23:57 Commencing optimization for 500 epochs, with 112536 positive edges
04:24:06 Optimization finished
[1] "79 0.15"04:24:06 UMAP embedding parameters a = 1.414 b = 0.9488
04:24:06 Read 1203 rows and found 38 numeric columns
04:24:06 Using Annoy for neighbor search, n_neighbors = 79
04:24:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:24:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fdd0784
04:24:07 Searching Annoy index using 1 thread, search_k = 7900
04:24:07 Annoy recall = 100%
04:24:13 Commencing smooth kNN distance calibration using 1 thread
04:24:24 Initializing from normalized Laplacian + noise
04:24:24 Commencing optimization for 500 epochs, with 112536 positive edges
04:24:33 Optimization finished
[1] "79 0.16"04:24:33 UMAP embedding parameters a = 1.383 b = 0.9596
04:24:33 Read 1203 rows and found 38 numeric columns
04:24:33 Using Annoy for neighbor search, n_neighbors = 79
04:24:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:24:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87395e4881
04:24:34 Searching Annoy index using 1 thread, search_k = 7900
04:24:34 Annoy recall = 100%
04:24:40 Commencing smooth kNN distance calibration using 1 thread
04:24:51 Initializing from normalized Laplacian + noise
04:24:51 Commencing optimization for 500 epochs, with 112536 positive edges
04:25:00 Optimization finished
[1] "79 0.17"04:25:00 UMAP embedding parameters a = 1.352 b = 0.9704
04:25:00 Read 1203 rows and found 38 numeric columns
04:25:00 Using Annoy for neighbor search, n_neighbors = 79
04:25:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:25:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757b54a1a
04:25:00 Searching Annoy index using 1 thread, search_k = 7900
04:25:01 Annoy recall = 100%
04:25:06 Commencing smooth kNN distance calibration using 1 thread
04:25:17 Initializing from normalized Laplacian + noise
04:25:17 Commencing optimization for 500 epochs, with 112536 positive edges
04:25:26 Optimization finished
[1] "79 0.18"04:25:27 UMAP embedding parameters a = 1.321 b = 0.9813
04:25:27 Read 1203 rows and found 38 numeric columns
04:25:27 Using Annoy for neighbor search, n_neighbors = 79
04:25:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:25:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ba6529e
04:25:27 Searching Annoy index using 1 thread, search_k = 7900
04:25:28 Annoy recall = 100%
04:25:33 Commencing smooth kNN distance calibration using 1 thread
04:25:44 Initializing from normalized Laplacian + noise
04:25:44 Commencing optimization for 500 epochs, with 112536 positive edges
04:25:53 Optimization finished
[1] "79 0.19"04:25:53 UMAP embedding parameters a = 1.292 b = 0.9921
04:25:53 Read 1203 rows and found 38 numeric columns
04:25:53 Using Annoy for neighbor search, n_neighbors = 79
04:25:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:25:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713206b43
04:25:54 Searching Annoy index using 1 thread, search_k = 7900
04:25:54 Annoy recall = 100%
04:26:00 Commencing smooth kNN distance calibration using 1 thread
04:26:11 Initializing from normalized Laplacian + noise
04:26:11 Commencing optimization for 500 epochs, with 112536 positive edges
04:26:20 Optimization finished
[1] "79 0.2"04:26:20 UMAP embedding parameters a = 1.262 b = 1.003
04:26:20 Read 1203 rows and found 38 numeric columns
04:26:20 Using Annoy for neighbor search, n_neighbors = 79
04:26:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:26:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a212003
04:26:21 Searching Annoy index using 1 thread, search_k = 7900
04:26:21 Annoy recall = 100%
04:26:27 Commencing smooth kNN distance calibration using 1 thread
04:26:38 Initializing from normalized Laplacian + noise
04:26:38 Commencing optimization for 500 epochs, with 112536 positive edges
04:26:47 Optimization finished
[1] "80 0"04:26:47 UMAP embedding parameters a = 1.933 b = 0.7905
04:26:47 Read 1203 rows and found 38 numeric columns
04:26:47 Using Annoy for neighbor search, n_neighbors = 80
04:26:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:26:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876380e51b
04:26:47 Searching Annoy index using 1 thread, search_k = 8000
04:26:48 Annoy recall = 100%
04:26:53 Commencing smooth kNN distance calibration using 1 thread
04:27:04 Initializing from normalized Laplacian + noise
04:27:04 Commencing optimization for 500 epochs, with 113884 positive edges
04:27:13 Optimization finished
[1] "80 0.01"04:27:14 UMAP embedding parameters a = 1.896 b = 0.8006
04:27:14 Read 1203 rows and found 38 numeric columns
04:27:14 Using Annoy for neighbor search, n_neighbors = 80
04:27:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:27:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87238689d
04:27:14 Searching Annoy index using 1 thread, search_k = 8000
04:27:15 Annoy recall = 100%
04:27:20 Commencing smooth kNN distance calibration using 1 thread
04:27:31 Initializing from normalized Laplacian + noise
04:27:31 Commencing optimization for 500 epochs, with 113884 positive edges
04:27:40 Optimization finished
[1] "80 0.02"04:27:41 UMAP embedding parameters a = 1.859 b = 0.8109
04:27:41 Read 1203 rows and found 38 numeric columns
04:27:41 Using Annoy for neighbor search, n_neighbors = 80
04:27:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:27:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ee5305
04:27:41 Searching Annoy index using 1 thread, search_k = 8000
04:27:42 Annoy recall = 100%
04:27:47 Commencing smooth kNN distance calibration using 1 thread
04:27:58 Initializing from normalized Laplacian + noise
04:27:58 Commencing optimization for 500 epochs, with 113884 positive edges
04:28:07 Optimization finished
[1] "80 0.03"04:28:07 UMAP embedding parameters a = 1.822 b = 0.8212
04:28:07 Read 1203 rows and found 38 numeric columns
04:28:07 Using Annoy for neighbor search, n_neighbors = 80
04:28:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:28:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871de430e7
04:28:08 Searching Annoy index using 1 thread, search_k = 8000
04:28:08 Annoy recall = 100%
04:28:14 Commencing smooth kNN distance calibration using 1 thread
04:28:25 Initializing from normalized Laplacian + noise
04:28:25 Commencing optimization for 500 epochs, with 113884 positive edges
04:28:34 Optimization finished
[1] "80 0.04"04:28:34 UMAP embedding parameters a = 1.786 b = 0.8316
04:28:34 Read 1203 rows and found 38 numeric columns
04:28:34 Using Annoy for neighbor search, n_neighbors = 80
04:28:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:28:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b3e06f7
04:28:35 Searching Annoy index using 1 thread, search_k = 8000
04:28:35 Annoy recall = 100%
04:28:41 Commencing smooth kNN distance calibration using 1 thread
04:28:52 Initializing from normalized Laplacian + noise
04:28:52 Commencing optimization for 500 epochs, with 113884 positive edges
04:29:01 Optimization finished
[1] "80 0.05"04:29:01 UMAP embedding parameters a = 1.75 b = 0.8421
04:29:01 Read 1203 rows and found 38 numeric columns
04:29:01 Using Annoy for neighbor search, n_neighbors = 80
04:29:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:29:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717db1f81
04:29:01 Searching Annoy index using 1 thread, search_k = 8000
04:29:02 Annoy recall = 100%
04:29:08 Commencing smooth kNN distance calibration using 1 thread
04:29:19 Initializing from normalized Laplacian + noise
04:29:19 Commencing optimization for 500 epochs, with 113884 positive edges
04:29:28 Optimization finished
[1] "80 0.06"04:29:28 UMAP embedding parameters a = 1.715 b = 0.8526
04:29:28 Read 1203 rows and found 38 numeric columns
04:29:28 Using Annoy for neighbor search, n_neighbors = 80
04:29:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:29:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722d50772
04:29:28 Searching Annoy index using 1 thread, search_k = 8000
04:29:29 Annoy recall = 100%
04:29:35 Commencing smooth kNN distance calibration using 1 thread
04:29:45 Initializing from normalized Laplacian + noise
04:29:46 Commencing optimization for 500 epochs, with 113884 positive edges
04:29:55 Optimization finished
[1] "80 0.07"04:29:55 UMAP embedding parameters a = 1.68 b = 0.8631
04:29:55 Read 1203 rows and found 38 numeric columns
04:29:55 Using Annoy for neighbor search, n_neighbors = 80
04:29:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:29:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87313672c3
04:29:55 Searching Annoy index using 1 thread, search_k = 8000
04:29:56 Annoy recall = 100%
04:30:01 Commencing smooth kNN distance calibration using 1 thread
04:30:12 Initializing from normalized Laplacian + noise
04:30:12 Commencing optimization for 500 epochs, with 113884 positive edges
04:30:21 Optimization finished
[1] "80 0.08"04:30:22 UMAP embedding parameters a = 1.645 b = 0.8737
04:30:22 Read 1203 rows and found 38 numeric columns
04:30:22 Using Annoy for neighbor search, n_neighbors = 80
04:30:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:30:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738b42813
04:30:22 Searching Annoy index using 1 thread, search_k = 8000
04:30:23 Annoy recall = 100%
04:30:28 Commencing smooth kNN distance calibration using 1 thread
04:30:39 Initializing from normalized Laplacian + noise
04:30:39 Commencing optimization for 500 epochs, with 113884 positive edges
04:30:48 Optimization finished
[1] "80 0.09"04:30:49 UMAP embedding parameters a = 1.611 b = 0.8844
04:30:49 Read 1203 rows and found 38 numeric columns
04:30:49 Using Annoy for neighbor search, n_neighbors = 80
04:30:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:30:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87251392e8
04:30:49 Searching Annoy index using 1 thread, search_k = 8000
04:30:50 Annoy recall = 100%
04:30:55 Commencing smooth kNN distance calibration using 1 thread
04:31:06 Initializing from normalized Laplacian + noise
04:31:06 Commencing optimization for 500 epochs, with 113884 positive edges
04:31:15 Optimization finished
[1] "80 0.1"04:31:16 UMAP embedding parameters a = 1.577 b = 0.8951
04:31:16 Read 1203 rows and found 38 numeric columns
04:31:16 Using Annoy for neighbor search, n_neighbors = 80
04:31:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:31:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757055952
04:31:16 Searching Annoy index using 1 thread, search_k = 8000
04:31:17 Annoy recall = 100%
04:31:22 Commencing smooth kNN distance calibration using 1 thread
04:31:33 Initializing from normalized Laplacian + noise
04:31:33 Commencing optimization for 500 epochs, with 113884 positive edges
04:31:42 Optimization finished
[1] "80 0.11"04:31:43 UMAP embedding parameters a = 1.544 b = 0.9058
04:31:43 Read 1203 rows and found 38 numeric columns
04:31:43 Using Annoy for neighbor search, n_neighbors = 80
04:31:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:31:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758c45c6d
04:31:43 Searching Annoy index using 1 thread, search_k = 8000
04:31:44 Annoy recall = 100%
04:31:49 Commencing smooth kNN distance calibration using 1 thread
04:32:00 Initializing from normalized Laplacian + noise
04:32:00 Commencing optimization for 500 epochs, with 113884 positive edges
04:32:09 Optimization finished
[1] "80 0.12"04:32:09 UMAP embedding parameters a = 1.51 b = 0.9165
04:32:09 Read 1203 rows and found 38 numeric columns
04:32:09 Using Annoy for neighbor search, n_neighbors = 80
04:32:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:32:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f7206cb
04:32:10 Searching Annoy index using 1 thread, search_k = 8000
04:32:10 Annoy recall = 100%
04:32:16 Commencing smooth kNN distance calibration using 1 thread
04:32:27 Initializing from normalized Laplacian + noise
04:32:27 Commencing optimization for 500 epochs, with 113884 positive edges
04:32:36 Optimization finished
[1] "80 0.13"04:32:37 UMAP embedding parameters a = 1.478 b = 0.9272
04:32:37 Read 1203 rows and found 38 numeric columns
04:32:37 Using Annoy for neighbor search, n_neighbors = 80
04:32:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:32:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717c84088
04:32:37 Searching Annoy index using 1 thread, search_k = 8000
04:32:38 Annoy recall = 100%
04:32:43 Commencing smooth kNN distance calibration using 1 thread
04:32:54 Initializing from normalized Laplacian + noise
04:32:54 Commencing optimization for 500 epochs, with 113884 positive edges
04:33:03 Optimization finished
[1] "80 0.14"04:33:03 UMAP embedding parameters a = 1.446 b = 0.938
04:33:03 Read 1203 rows and found 38 numeric columns
04:33:03 Using Annoy for neighbor search, n_neighbors = 80
04:33:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:33:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f6f41e9
04:33:04 Searching Annoy index using 1 thread, search_k = 8000
04:33:04 Annoy recall = 100%
04:33:10 Commencing smooth kNN distance calibration using 1 thread
04:33:21 Initializing from normalized Laplacian + noise
04:33:21 Commencing optimization for 500 epochs, with 113884 positive edges
04:33:30 Optimization finished
[1] "80 0.15"04:33:30 UMAP embedding parameters a = 1.414 b = 0.9488
04:33:30 Read 1203 rows and found 38 numeric columns
04:33:30 Using Annoy for neighbor search, n_neighbors = 80
04:33:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:33:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c47ffe6
04:33:31 Searching Annoy index using 1 thread, search_k = 8000
04:33:32 Annoy recall = 100%
04:33:37 Commencing smooth kNN distance calibration using 1 thread
04:33:48 Initializing from normalized Laplacian + noise
04:33:48 Commencing optimization for 500 epochs, with 113884 positive edges
04:33:57 Optimization finished
[1] "80 0.16"04:33:57 UMAP embedding parameters a = 1.383 b = 0.9596
04:33:57 Read 1203 rows and found 38 numeric columns
04:33:57 Using Annoy for neighbor search, n_neighbors = 80
04:33:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:33:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ec6018c
04:33:58 Searching Annoy index using 1 thread, search_k = 8000
04:33:58 Annoy recall = 100%
04:34:04 Commencing smooth kNN distance calibration using 1 thread
04:34:15 Initializing from normalized Laplacian + noise
04:34:15 Commencing optimization for 500 epochs, with 113884 positive edges
04:34:24 Optimization finished
[1] "80 0.17"04:34:24 UMAP embedding parameters a = 1.352 b = 0.9704
04:34:24 Read 1203 rows and found 38 numeric columns
04:34:24 Using Annoy for neighbor search, n_neighbors = 80
04:34:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:34:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873838f174
04:34:25 Searching Annoy index using 1 thread, search_k = 8000
04:34:25 Annoy recall = 100%
04:34:31 Commencing smooth kNN distance calibration using 1 thread
04:34:42 Initializing from normalized Laplacian + noise
04:34:42 Commencing optimization for 500 epochs, with 113884 positive edges
04:34:51 Optimization finished
[1] "80 0.18"04:34:52 UMAP embedding parameters a = 1.321 b = 0.9813
04:34:52 Read 1203 rows and found 38 numeric columns
04:34:52 Using Annoy for neighbor search, n_neighbors = 80
04:34:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:34:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765fe4dcf
04:34:52 Searching Annoy index using 1 thread, search_k = 8000
04:34:53 Annoy recall = 100%
04:34:58 Commencing smooth kNN distance calibration using 1 thread
04:35:09 Initializing from normalized Laplacian + noise
04:35:09 Commencing optimization for 500 epochs, with 113884 positive edges
04:35:18 Optimization finished
[1] "80 0.19"04:35:18 UMAP embedding parameters a = 1.292 b = 0.9921
04:35:18 Read 1203 rows and found 38 numeric columns
04:35:18 Using Annoy for neighbor search, n_neighbors = 80
04:35:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:35:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879bab7d5
04:35:19 Searching Annoy index using 1 thread, search_k = 8000
04:35:20 Annoy recall = 100%
04:35:25 Commencing smooth kNN distance calibration using 1 thread
04:35:36 Initializing from normalized Laplacian + noise
04:35:36 Commencing optimization for 500 epochs, with 113884 positive edges
04:35:45 Optimization finished
[1] "80 0.2"04:35:46 UMAP embedding parameters a = 1.262 b = 1.003
04:35:46 Read 1203 rows and found 38 numeric columns
04:35:46 Using Annoy for neighbor search, n_neighbors = 80
04:35:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:35:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f784fcb
04:35:46 Searching Annoy index using 1 thread, search_k = 8000
04:35:47 Annoy recall = 100%
04:35:52 Commencing smooth kNN distance calibration using 1 thread
04:36:03 Initializing from normalized Laplacian + noise
04:36:03 Commencing optimization for 500 epochs, with 113884 positive edges
04:36:12 Optimization finished
[1] "81 0"04:36:13 UMAP embedding parameters a = 1.933 b = 0.7905
04:36:13 Read 1203 rows and found 38 numeric columns
04:36:13 Using Annoy for neighbor search, n_neighbors = 81
04:36:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:36:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740700ed7
04:36:13 Searching Annoy index using 1 thread, search_k = 8100
04:36:14 Annoy recall = 100%
04:36:19 Commencing smooth kNN distance calibration using 1 thread
04:36:30 Initializing from normalized Laplacian + noise
04:36:30 Commencing optimization for 500 epochs, with 115220 positive edges
04:36:40 Optimization finished
[1] "81 0.01"04:36:40 UMAP embedding parameters a = 1.896 b = 0.8006
04:36:40 Read 1203 rows and found 38 numeric columns
04:36:40 Using Annoy for neighbor search, n_neighbors = 81
04:36:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:36:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768e0eb68
04:36:40 Searching Annoy index using 1 thread, search_k = 8100
04:36:41 Annoy recall = 100%
04:36:46 Commencing smooth kNN distance calibration using 1 thread
04:36:58 Initializing from normalized Laplacian + noise
04:36:58 Commencing optimization for 500 epochs, with 115220 positive edges
04:37:07 Optimization finished
[1] "81 0.02"04:37:07 UMAP embedding parameters a = 1.859 b = 0.8109
04:37:07 Read 1203 rows and found 38 numeric columns
04:37:07 Using Annoy for neighbor search, n_neighbors = 81
04:37:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:37:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87209131b4
04:37:07 Searching Annoy index using 1 thread, search_k = 8100
04:37:08 Annoy recall = 100%
04:37:14 Commencing smooth kNN distance calibration using 1 thread
04:37:25 Initializing from normalized Laplacian + noise
04:37:25 Commencing optimization for 500 epochs, with 115220 positive edges
04:37:34 Optimization finished
[1] "81 0.03"04:37:34 UMAP embedding parameters a = 1.822 b = 0.8212
04:37:34 Read 1203 rows and found 38 numeric columns
04:37:34 Using Annoy for neighbor search, n_neighbors = 81
04:37:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:37:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726dde435
04:37:34 Searching Annoy index using 1 thread, search_k = 8100
04:37:35 Annoy recall = 100%
04:37:41 Commencing smooth kNN distance calibration using 1 thread
04:37:52 Initializing from normalized Laplacian + noise
04:37:52 Commencing optimization for 500 epochs, with 115220 positive edges
04:38:01 Optimization finished
[1] "81 0.04"04:38:01 UMAP embedding parameters a = 1.786 b = 0.8316
04:38:01 Read 1203 rows and found 38 numeric columns
04:38:01 Using Annoy for neighbor search, n_neighbors = 81
04:38:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:38:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778bdf2ec
04:38:02 Searching Annoy index using 1 thread, search_k = 8100
04:38:02 Annoy recall = 100%
04:38:08 Commencing smooth kNN distance calibration using 1 thread
04:38:19 Initializing from normalized Laplacian + noise
04:38:19 Commencing optimization for 500 epochs, with 115220 positive edges
04:38:28 Optimization finished
[1] "81 0.05"04:38:28 UMAP embedding parameters a = 1.75 b = 0.8421
04:38:28 Read 1203 rows and found 38 numeric columns
04:38:28 Using Annoy for neighbor search, n_neighbors = 81
04:38:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:38:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759ef7a36
04:38:29 Searching Annoy index using 1 thread, search_k = 8100
04:38:29 Annoy recall = 100%
04:38:35 Commencing smooth kNN distance calibration using 1 thread
04:38:46 Initializing from normalized Laplacian + noise
04:38:46 Commencing optimization for 500 epochs, with 115220 positive edges
04:38:55 Optimization finished
[1] "81 0.06"04:38:56 UMAP embedding parameters a = 1.715 b = 0.8526
04:38:56 Read 1203 rows and found 38 numeric columns
04:38:56 Using Annoy for neighbor search, n_neighbors = 81
04:38:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:38:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e932e4f
04:38:56 Searching Annoy index using 1 thread, search_k = 8100
04:38:57 Annoy recall = 100%
04:39:02 Commencing smooth kNN distance calibration using 1 thread
04:39:13 Initializing from normalized Laplacian + noise
04:39:13 Commencing optimization for 500 epochs, with 115220 positive edges
04:39:22 Optimization finished
[1] "81 0.07"04:39:23 UMAP embedding parameters a = 1.68 b = 0.8631
04:39:23 Read 1203 rows and found 38 numeric columns
04:39:23 Using Annoy for neighbor search, n_neighbors = 81
04:39:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:39:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873464458a
04:39:23 Searching Annoy index using 1 thread, search_k = 8100
04:39:24 Annoy recall = 100%
04:39:29 Commencing smooth kNN distance calibration using 1 thread
04:39:41 Initializing from normalized Laplacian + noise
04:39:41 Commencing optimization for 500 epochs, with 115220 positive edges
04:39:50 Optimization finished
[1] "81 0.08"04:39:50 UMAP embedding parameters a = 1.645 b = 0.8737
04:39:50 Read 1203 rows and found 38 numeric columns
04:39:50 Using Annoy for neighbor search, n_neighbors = 81
04:39:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:39:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d0fe579
04:39:50 Searching Annoy index using 1 thread, search_k = 8100
04:39:51 Annoy recall = 100%
04:39:57 Commencing smooth kNN distance calibration using 1 thread
04:40:08 Initializing from normalized Laplacian + noise
04:40:08 Commencing optimization for 500 epochs, with 115220 positive edges
04:40:17 Optimization finished
[1] "81 0.09"04:40:17 UMAP embedding parameters a = 1.611 b = 0.8844
04:40:17 Read 1203 rows and found 38 numeric columns
04:40:17 Using Annoy for neighbor search, n_neighbors = 81
04:40:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:40:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728b44e52
04:40:18 Searching Annoy index using 1 thread, search_k = 8100
04:40:18 Annoy recall = 100%
04:40:24 Commencing smooth kNN distance calibration using 1 thread
04:40:35 Initializing from normalized Laplacian + noise
04:40:35 Commencing optimization for 500 epochs, with 115220 positive edges
04:40:44 Optimization finished
[1] "81 0.1"04:40:44 UMAP embedding parameters a = 1.577 b = 0.8951
04:40:44 Read 1203 rows and found 38 numeric columns
04:40:44 Using Annoy for neighbor search, n_neighbors = 81
04:40:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:40:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717e52aa5
04:40:45 Searching Annoy index using 1 thread, search_k = 8100
04:40:45 Annoy recall = 100%
04:40:51 Commencing smooth kNN distance calibration using 1 thread
04:41:02 Initializing from normalized Laplacian + noise
04:41:02 Commencing optimization for 500 epochs, with 115220 positive edges
04:41:11 Optimization finished
[1] "81 0.11"04:41:12 UMAP embedding parameters a = 1.544 b = 0.9058
04:41:12 Read 1203 rows and found 38 numeric columns
04:41:12 Using Annoy for neighbor search, n_neighbors = 81
04:41:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:41:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f484e17
04:41:12 Searching Annoy index using 1 thread, search_k = 8100
04:41:13 Annoy recall = 100%
04:41:18 Commencing smooth kNN distance calibration using 1 thread
04:41:29 Initializing from normalized Laplacian + noise
04:41:30 Commencing optimization for 500 epochs, with 115220 positive edges
04:41:39 Optimization finished
[1] "81 0.12"04:41:39 UMAP embedding parameters a = 1.51 b = 0.9165
04:41:39 Read 1203 rows and found 38 numeric columns
04:41:39 Using Annoy for neighbor search, n_neighbors = 81
04:41:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:41:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ca2a157
04:41:39 Searching Annoy index using 1 thread, search_k = 8100
04:41:40 Annoy recall = 100%
04:41:46 Commencing smooth kNN distance calibration using 1 thread
04:41:57 Initializing from normalized Laplacian + noise
04:41:57 Commencing optimization for 500 epochs, with 115220 positive edges
04:42:06 Optimization finished
[1] "81 0.13"04:42:06 UMAP embedding parameters a = 1.478 b = 0.9272
04:42:06 Read 1203 rows and found 38 numeric columns
04:42:06 Using Annoy for neighbor search, n_neighbors = 81
04:42:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:42:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735c95b8c
04:42:07 Searching Annoy index using 1 thread, search_k = 8100
04:42:07 Annoy recall = 100%
04:42:13 Commencing smooth kNN distance calibration using 1 thread
04:42:24 Initializing from normalized Laplacian + noise
04:42:24 Commencing optimization for 500 epochs, with 115220 positive edges
04:42:33 Optimization finished
[1] "81 0.14"04:42:33 UMAP embedding parameters a = 1.446 b = 0.938
04:42:33 Read 1203 rows and found 38 numeric columns
04:42:33 Using Annoy for neighbor search, n_neighbors = 81
04:42:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:42:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a86550e
04:42:34 Searching Annoy index using 1 thread, search_k = 8100
04:42:35 Annoy recall = 100%
04:42:40 Commencing smooth kNN distance calibration using 1 thread
04:42:51 Initializing from normalized Laplacian + noise
04:42:51 Commencing optimization for 500 epochs, with 115220 positive edges
04:43:01 Optimization finished
[1] "81 0.15"04:43:01 UMAP embedding parameters a = 1.414 b = 0.9488
04:43:01 Read 1203 rows and found 38 numeric columns
04:43:01 Using Annoy for neighbor search, n_neighbors = 81
04:43:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:43:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87447dc0d9
04:43:01 Searching Annoy index using 1 thread, search_k = 8100
04:43:02 Annoy recall = 100%
04:43:08 Commencing smooth kNN distance calibration using 1 thread
04:43:19 Initializing from normalized Laplacian + noise
04:43:19 Commencing optimization for 500 epochs, with 115220 positive edges
04:43:28 Optimization finished
[1] "81 0.16"04:43:28 UMAP embedding parameters a = 1.383 b = 0.9596
04:43:28 Read 1203 rows and found 38 numeric columns
04:43:28 Using Annoy for neighbor search, n_neighbors = 81
04:43:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:43:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87589e62fe
04:43:29 Searching Annoy index using 1 thread, search_k = 8100
04:43:29 Annoy recall = 100%
04:43:35 Commencing smooth kNN distance calibration using 1 thread
04:43:46 Initializing from normalized Laplacian + noise
04:43:46 Commencing optimization for 500 epochs, with 115220 positive edges
04:43:55 Optimization finished
[1] "81 0.17"04:43:55 UMAP embedding parameters a = 1.352 b = 0.9704
04:43:55 Read 1203 rows and found 38 numeric columns
04:43:55 Using Annoy for neighbor search, n_neighbors = 81
04:43:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:43:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bbcc7d1
04:43:56 Searching Annoy index using 1 thread, search_k = 8100
04:43:57 Annoy recall = 100%
04:44:02 Commencing smooth kNN distance calibration using 1 thread
04:44:13 Initializing from normalized Laplacian + noise
04:44:13 Commencing optimization for 500 epochs, with 115220 positive edges
04:44:23 Optimization finished
[1] "81 0.18"04:44:23 UMAP embedding parameters a = 1.321 b = 0.9813
04:44:23 Read 1203 rows and found 38 numeric columns
04:44:23 Using Annoy for neighbor search, n_neighbors = 81
04:44:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:44:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d31e8ec
04:44:23 Searching Annoy index using 1 thread, search_k = 8100
04:44:24 Annoy recall = 100%
04:44:29 Commencing smooth kNN distance calibration using 1 thread
04:44:41 Initializing from normalized Laplacian + noise
04:44:41 Commencing optimization for 500 epochs, with 115220 positive edges
04:44:50 Optimization finished
[1] "81 0.19"04:44:50 UMAP embedding parameters a = 1.292 b = 0.9921
04:44:50 Read 1203 rows and found 38 numeric columns
04:44:50 Using Annoy for neighbor search, n_neighbors = 81
04:44:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:44:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877db1f5e6
04:44:51 Searching Annoy index using 1 thread, search_k = 8100
04:44:51 Annoy recall = 100%
04:44:57 Commencing smooth kNN distance calibration using 1 thread
04:45:08 Initializing from normalized Laplacian + noise
04:45:08 Commencing optimization for 500 epochs, with 115220 positive edges
04:45:17 Optimization finished
[1] "81 0.2"04:45:17 UMAP embedding parameters a = 1.262 b = 1.003
04:45:18 Read 1203 rows and found 38 numeric columns
04:45:18 Using Annoy for neighbor search, n_neighbors = 81
04:45:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:45:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732c22124
04:45:18 Searching Annoy index using 1 thread, search_k = 8100
04:45:19 Annoy recall = 100%
04:45:24 Commencing smooth kNN distance calibration using 1 thread
04:45:35 Initializing from normalized Laplacian + noise
04:45:35 Commencing optimization for 500 epochs, with 115220 positive edges
04:45:45 Optimization finished
[1] "82 0"04:45:45 UMAP embedding parameters a = 1.933 b = 0.7905
04:45:45 Read 1203 rows and found 38 numeric columns
04:45:45 Using Annoy for neighbor search, n_neighbors = 82
04:45:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:45:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755f6455a
04:45:45 Searching Annoy index using 1 thread, search_k = 8200
04:45:46 Annoy recall = 100%
04:45:51 Commencing smooth kNN distance calibration using 1 thread
04:46:03 Initializing from normalized Laplacian + noise
04:46:03 Commencing optimization for 500 epochs, with 116520 positive edges
04:46:12 Optimization finished
[1] "82 0.01"04:46:12 UMAP embedding parameters a = 1.896 b = 0.8006
04:46:12 Read 1203 rows and found 38 numeric columns
04:46:12 Using Annoy for neighbor search, n_neighbors = 82
04:46:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:46:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d23fcb2
04:46:13 Searching Annoy index using 1 thread, search_k = 8200
04:46:13 Annoy recall = 100%
04:46:19 Commencing smooth kNN distance calibration using 1 thread
04:46:30 Initializing from normalized Laplacian + noise
04:46:30 Commencing optimization for 500 epochs, with 116520 positive edges
04:46:39 Optimization finished
[1] "82 0.02"04:46:40 UMAP embedding parameters a = 1.859 b = 0.8109
04:46:40 Read 1203 rows and found 38 numeric columns
04:46:40 Using Annoy for neighbor search, n_neighbors = 82
04:46:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:46:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a8a61ac
04:46:40 Searching Annoy index using 1 thread, search_k = 8200
04:46:41 Annoy recall = 100%
04:46:46 Commencing smooth kNN distance calibration using 1 thread
04:46:58 Initializing from normalized Laplacian + noise
04:46:58 Commencing optimization for 500 epochs, with 116520 positive edges
04:47:07 Optimization finished
[1] "82 0.03"04:47:07 UMAP embedding parameters a = 1.822 b = 0.8212
04:47:07 Read 1203 rows and found 38 numeric columns
04:47:07 Using Annoy for neighbor search, n_neighbors = 82
04:47:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:47:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715658743
04:47:08 Searching Annoy index using 1 thread, search_k = 8200
04:47:08 Annoy recall = 100%
04:47:14 Commencing smooth kNN distance calibration using 1 thread
04:47:25 Initializing from normalized Laplacian + noise
04:47:25 Commencing optimization for 500 epochs, with 116520 positive edges
04:47:34 Optimization finished
[1] "82 0.04"04:47:34 UMAP embedding parameters a = 1.786 b = 0.8316
04:47:34 Read 1203 rows and found 38 numeric columns
04:47:34 Using Annoy for neighbor search, n_neighbors = 82
04:47:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:47:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8796bfc98
04:47:35 Searching Annoy index using 1 thread, search_k = 8200
04:47:36 Annoy recall = 100%
04:47:41 Commencing smooth kNN distance calibration using 1 thread
04:47:52 Initializing from normalized Laplacian + noise
04:47:53 Commencing optimization for 500 epochs, with 116520 positive edges
04:48:02 Optimization finished
[1] "82 0.05"04:48:02 UMAP embedding parameters a = 1.75 b = 0.8421
04:48:02 Read 1203 rows and found 38 numeric columns
04:48:02 Using Annoy for neighbor search, n_neighbors = 82
04:48:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:48:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759506338
04:48:02 Searching Annoy index using 1 thread, search_k = 8200
04:48:03 Annoy recall = 100%
04:48:09 Commencing smooth kNN distance calibration using 1 thread
04:48:20 Initializing from normalized Laplacian + noise
04:48:20 Commencing optimization for 500 epochs, with 116520 positive edges
04:48:29 Optimization finished
[1] "82 0.06"04:48:29 UMAP embedding parameters a = 1.715 b = 0.8526
04:48:29 Read 1203 rows and found 38 numeric columns
04:48:29 Using Annoy for neighbor search, n_neighbors = 82
04:48:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:48:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d9e78b7
04:48:30 Searching Annoy index using 1 thread, search_k = 8200
04:48:31 Annoy recall = 100%
04:48:36 Commencing smooth kNN distance calibration using 1 thread
04:48:47 Initializing from normalized Laplacian + noise
04:48:47 Commencing optimization for 500 epochs, with 116520 positive edges
04:48:57 Optimization finished
[1] "82 0.07"04:48:57 UMAP embedding parameters a = 1.68 b = 0.8631
04:48:57 Read 1203 rows and found 38 numeric columns
04:48:57 Using Annoy for neighbor search, n_neighbors = 82
04:48:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:48:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f6a4a67
04:48:57 Searching Annoy index using 1 thread, search_k = 8200
04:48:58 Annoy recall = 100%
04:49:04 Commencing smooth kNN distance calibration using 1 thread
04:49:15 Initializing from normalized Laplacian + noise
04:49:15 Commencing optimization for 500 epochs, with 116520 positive edges
04:49:24 Optimization finished
[1] "82 0.08"04:49:25 UMAP embedding parameters a = 1.645 b = 0.8737
04:49:25 Read 1203 rows and found 38 numeric columns
04:49:25 Using Annoy for neighbor search, n_neighbors = 82
04:49:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:49:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87630b1b0d
04:49:25 Searching Annoy index using 1 thread, search_k = 8200
04:49:26 Annoy recall = 100%
04:49:31 Commencing smooth kNN distance calibration using 1 thread
04:49:42 Initializing from normalized Laplacian + noise
04:49:42 Commencing optimization for 500 epochs, with 116520 positive edges
04:49:52 Optimization finished
[1] "82 0.09"04:49:52 UMAP embedding parameters a = 1.611 b = 0.8844
04:49:52 Read 1203 rows and found 38 numeric columns
04:49:52 Using Annoy for neighbor search, n_neighbors = 82
04:49:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:49:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d16c882
04:49:52 Searching Annoy index using 1 thread, search_k = 8200
04:49:53 Annoy recall = 100%
04:49:59 Commencing smooth kNN distance calibration using 1 thread
04:50:10 Initializing from normalized Laplacian + noise
04:50:10 Commencing optimization for 500 epochs, with 116520 positive edges
04:50:19 Optimization finished
[1] "82 0.1"04:50:20 UMAP embedding parameters a = 1.577 b = 0.8951
04:50:20 Read 1203 rows and found 38 numeric columns
04:50:20 Using Annoy for neighbor search, n_neighbors = 82
04:50:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:50:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fda593e
04:50:20 Searching Annoy index using 1 thread, search_k = 8200
04:50:21 Annoy recall = 100%
04:50:26 Commencing smooth kNN distance calibration using 1 thread
04:50:37 Initializing from normalized Laplacian + noise
04:50:38 Commencing optimization for 500 epochs, with 116520 positive edges
04:50:47 Optimization finished
[1] "82 0.11"04:50:47 UMAP embedding parameters a = 1.544 b = 0.9058
04:50:47 Read 1203 rows and found 38 numeric columns
04:50:47 Using Annoy for neighbor search, n_neighbors = 82
04:50:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:50:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bec0675
04:50:47 Searching Annoy index using 1 thread, search_k = 8200
04:50:48 Annoy recall = 100%
04:50:54 Commencing smooth kNN distance calibration using 1 thread
04:51:05 Initializing from normalized Laplacian + noise
04:51:05 Commencing optimization for 500 epochs, with 116520 positive edges
04:51:14 Optimization finished
[1] "82 0.12"04:51:15 UMAP embedding parameters a = 1.51 b = 0.9165
04:51:15 Read 1203 rows and found 38 numeric columns
04:51:15 Using Annoy for neighbor search, n_neighbors = 82
04:51:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:51:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875da7fa37
04:51:15 Searching Annoy index using 1 thread, search_k = 8200
04:51:16 Annoy recall = 100%
04:51:21 Commencing smooth kNN distance calibration using 1 thread
04:51:33 Initializing from normalized Laplacian + noise
04:51:33 Commencing optimization for 500 epochs, with 116520 positive edges
04:51:42 Optimization finished
[1] "82 0.13"04:51:42 UMAP embedding parameters a = 1.478 b = 0.9272
04:51:42 Read 1203 rows and found 38 numeric columns
04:51:42 Using Annoy for neighbor search, n_neighbors = 82
04:51:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:51:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756b83d73
04:51:43 Searching Annoy index using 1 thread, search_k = 8200
04:51:43 Annoy recall = 100%
04:51:49 Commencing smooth kNN distance calibration using 1 thread
04:52:00 Initializing from normalized Laplacian + noise
04:52:00 Commencing optimization for 500 epochs, with 116520 positive edges
04:52:10 Optimization finished
[1] "82 0.14"04:52:10 UMAP embedding parameters a = 1.446 b = 0.938
04:52:10 Read 1203 rows and found 38 numeric columns
04:52:10 Using Annoy for neighbor search, n_neighbors = 82
04:52:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:52:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744a9f961
04:52:10 Searching Annoy index using 1 thread, search_k = 8200
04:52:11 Annoy recall = 100%
04:52:16 Commencing smooth kNN distance calibration using 1 thread
04:52:28 Initializing from normalized Laplacian + noise
04:52:28 Commencing optimization for 500 epochs, with 116520 positive edges
04:52:37 Optimization finished
[1] "82 0.15"04:52:37 UMAP embedding parameters a = 1.414 b = 0.9488
04:52:37 Read 1203 rows and found 38 numeric columns
04:52:37 Using Annoy for neighbor search, n_neighbors = 82
04:52:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:52:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873797746d
04:52:38 Searching Annoy index using 1 thread, search_k = 8200
04:52:38 Annoy recall = 100%
04:52:44 Commencing smooth kNN distance calibration using 1 thread
04:52:55 Initializing from normalized Laplacian + noise
04:52:55 Commencing optimization for 500 epochs, with 116520 positive edges
04:53:05 Optimization finished
[1] "82 0.16"04:53:05 UMAP embedding parameters a = 1.383 b = 0.9596
04:53:05 Read 1203 rows and found 38 numeric columns
04:53:05 Using Annoy for neighbor search, n_neighbors = 82
04:53:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:53:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87554b6bc3
04:53:05 Searching Annoy index using 1 thread, search_k = 8200
04:53:06 Annoy recall = 100%
04:53:12 Commencing smooth kNN distance calibration using 1 thread
04:53:23 Initializing from normalized Laplacian + noise
04:53:23 Commencing optimization for 500 epochs, with 116520 positive edges
04:53:32 Optimization finished
[1] "82 0.17"04:53:33 UMAP embedding parameters a = 1.352 b = 0.9704
04:53:33 Read 1203 rows and found 38 numeric columns
04:53:33 Using Annoy for neighbor search, n_neighbors = 82
04:53:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:53:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87790e3eec
04:53:33 Searching Annoy index using 1 thread, search_k = 8200
04:53:34 Annoy recall = 100%
04:53:39 Commencing smooth kNN distance calibration using 1 thread
04:53:51 Initializing from normalized Laplacian + noise
04:53:51 Commencing optimization for 500 epochs, with 116520 positive edges
04:54:00 Optimization finished
[1] "82 0.18"04:54:00 UMAP embedding parameters a = 1.321 b = 0.9813
04:54:00 Read 1203 rows and found 38 numeric columns
04:54:00 Using Annoy for neighbor search, n_neighbors = 82
04:54:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:54:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724a759e6
04:54:01 Searching Annoy index using 1 thread, search_k = 8200
04:54:01 Annoy recall = 100%
04:54:07 Commencing smooth kNN distance calibration using 1 thread
04:54:18 Initializing from normalized Laplacian + noise
04:54:18 Commencing optimization for 500 epochs, with 116520 positive edges
04:54:28 Optimization finished
[1] "82 0.19"04:54:28 UMAP embedding parameters a = 1.292 b = 0.9921
04:54:28 Read 1203 rows and found 38 numeric columns
04:54:28 Using Annoy for neighbor search, n_neighbors = 82
04:54:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:54:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877dffba15
04:54:28 Searching Annoy index using 1 thread, search_k = 8200
04:54:29 Annoy recall = 100%
04:54:35 Commencing smooth kNN distance calibration using 1 thread
04:54:46 Initializing from normalized Laplacian + noise
04:54:46 Commencing optimization for 500 epochs, with 116520 positive edges
04:54:56 Optimization finished
[1] "82 0.2"04:54:56 UMAP embedding parameters a = 1.262 b = 1.003
04:54:56 Read 1203 rows and found 38 numeric columns
04:54:56 Using Annoy for neighbor search, n_neighbors = 82
04:54:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:54:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710f36991
04:54:56 Searching Annoy index using 1 thread, search_k = 8200
04:54:57 Annoy recall = 100%
04:55:03 Commencing smooth kNN distance calibration using 1 thread
04:55:15 Initializing from normalized Laplacian + noise
04:55:15 Commencing optimization for 500 epochs, with 116520 positive edges
04:55:24 Optimization finished
[1] "83 0"04:55:24 UMAP embedding parameters a = 1.933 b = 0.7905
04:55:24 Read 1203 rows and found 38 numeric columns
04:55:24 Using Annoy for neighbor search, n_neighbors = 83
04:55:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:55:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713efa7fd
04:55:25 Searching Annoy index using 1 thread, search_k = 8300
04:55:25 Annoy recall = 100%
04:55:31 Commencing smooth kNN distance calibration using 1 thread
04:55:43 Initializing from normalized Laplacian + noise
04:55:43 Commencing optimization for 500 epochs, with 117840 positive edges
04:55:52 Optimization finished
[1] "83 0.01"04:55:53 UMAP embedding parameters a = 1.896 b = 0.8006
04:55:53 Read 1203 rows and found 38 numeric columns
04:55:53 Using Annoy for neighbor search, n_neighbors = 83
04:55:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:55:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872aa25b6d
04:55:53 Searching Annoy index using 1 thread, search_k = 8300
04:55:54 Annoy recall = 100%
04:56:00 Commencing smooth kNN distance calibration using 1 thread
04:56:11 Initializing from normalized Laplacian + noise
04:56:11 Commencing optimization for 500 epochs, with 117840 positive edges
04:56:21 Optimization finished
[1] "83 0.02"04:56:21 UMAP embedding parameters a = 1.859 b = 0.8109
04:56:21 Read 1203 rows and found 38 numeric columns
04:56:21 Using Annoy for neighbor search, n_neighbors = 83
04:56:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:56:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746bcc51e
04:56:21 Searching Annoy index using 1 thread, search_k = 8300
04:56:22 Annoy recall = 100%
04:56:28 Commencing smooth kNN distance calibration using 1 thread
04:56:40 Initializing from normalized Laplacian + noise
04:56:40 Commencing optimization for 500 epochs, with 117840 positive edges
04:56:49 Optimization finished
[1] "83 0.03"04:56:49 UMAP embedding parameters a = 1.822 b = 0.8212
04:56:49 Read 1203 rows and found 38 numeric columns
04:56:49 Using Annoy for neighbor search, n_neighbors = 83
04:56:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:56:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e75fd0c
04:56:50 Searching Annoy index using 1 thread, search_k = 8300
04:56:50 Annoy recall = 100%
04:56:56 Commencing smooth kNN distance calibration using 1 thread
04:57:08 Initializing from normalized Laplacian + noise
04:57:08 Commencing optimization for 500 epochs, with 117840 positive edges
04:57:17 Optimization finished
[1] "83 0.04"04:57:18 UMAP embedding parameters a = 1.786 b = 0.8316
04:57:18 Read 1203 rows and found 38 numeric columns
04:57:18 Using Annoy for neighbor search, n_neighbors = 83
04:57:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:57:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f201c46
04:57:18 Searching Annoy index using 1 thread, search_k = 8300
04:57:19 Annoy recall = 100%
04:57:25 Commencing smooth kNN distance calibration using 1 thread
04:57:36 Initializing from normalized Laplacian + noise
04:57:36 Commencing optimization for 500 epochs, with 117840 positive edges
04:57:46 Optimization finished
[1] "83 0.05"04:57:46 UMAP embedding parameters a = 1.75 b = 0.8421
04:57:46 Read 1203 rows and found 38 numeric columns
04:57:46 Using Annoy for neighbor search, n_neighbors = 83
04:57:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:57:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f5b281c
04:57:46 Searching Annoy index using 1 thread, search_k = 8300
04:57:47 Annoy recall = 100%
04:57:53 Commencing smooth kNN distance calibration using 1 thread
04:58:04 Initializing from normalized Laplacian + noise
04:58:05 Commencing optimization for 500 epochs, with 117840 positive edges
04:58:14 Optimization finished
[1] "83 0.06"04:58:14 UMAP embedding parameters a = 1.715 b = 0.8526
04:58:14 Read 1203 rows and found 38 numeric columns
04:58:14 Using Annoy for neighbor search, n_neighbors = 83
04:58:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:58:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a32c4dd
04:58:15 Searching Annoy index using 1 thread, search_k = 8300
04:58:15 Annoy recall = 100%
04:58:21 Commencing smooth kNN distance calibration using 1 thread
04:58:33 Initializing from normalized Laplacian + noise
04:58:33 Commencing optimization for 500 epochs, with 117840 positive edges
04:58:42 Optimization finished
[1] "83 0.07"04:58:42 UMAP embedding parameters a = 1.68 b = 0.8631
04:58:42 Read 1203 rows and found 38 numeric columns
04:58:42 Using Annoy for neighbor search, n_neighbors = 83
04:58:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:58:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c520532
04:58:43 Searching Annoy index using 1 thread, search_k = 8300
04:58:44 Annoy recall = 100%
04:58:49 Commencing smooth kNN distance calibration using 1 thread
04:59:01 Initializing from normalized Laplacian + noise
04:59:01 Commencing optimization for 500 epochs, with 117840 positive edges
04:59:10 Optimization finished
[1] "83 0.08"04:59:11 UMAP embedding parameters a = 1.645 b = 0.8737
04:59:11 Read 1203 rows and found 38 numeric columns
04:59:11 Using Annoy for neighbor search, n_neighbors = 83
04:59:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:59:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d0d1e03
04:59:11 Searching Annoy index using 1 thread, search_k = 8300
04:59:12 Annoy recall = 100%
04:59:17 Commencing smooth kNN distance calibration using 1 thread
04:59:29 Initializing from normalized Laplacian + noise
04:59:29 Commencing optimization for 500 epochs, with 117840 positive edges
04:59:39 Optimization finished
[1] "83 0.09"04:59:39 UMAP embedding parameters a = 1.611 b = 0.8844
04:59:39 Read 1203 rows and found 38 numeric columns
04:59:39 Using Annoy for neighbor search, n_neighbors = 83
04:59:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:59:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874cf4e601
04:59:39 Searching Annoy index using 1 thread, search_k = 8300
04:59:40 Annoy recall = 100%
04:59:46 Commencing smooth kNN distance calibration using 1 thread
04:59:57 Initializing from normalized Laplacian + noise
04:59:57 Commencing optimization for 500 epochs, with 117840 positive edges
05:00:07 Optimization finished
[1] "83 0.1"05:00:07 UMAP embedding parameters a = 1.577 b = 0.8951
05:00:07 Read 1203 rows and found 38 numeric columns
05:00:07 Using Annoy for neighbor search, n_neighbors = 83
05:00:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:00:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742484a8c
05:00:08 Searching Annoy index using 1 thread, search_k = 8300
05:00:08 Annoy recall = 100%
05:00:14 Commencing smooth kNN distance calibration using 1 thread
05:00:26 Initializing from normalized Laplacian + noise
05:00:26 Commencing optimization for 500 epochs, with 117840 positive edges
05:00:35 Optimization finished
[1] "83 0.11"05:00:36 UMAP embedding parameters a = 1.544 b = 0.9058
05:00:36 Read 1203 rows and found 38 numeric columns
05:00:36 Using Annoy for neighbor search, n_neighbors = 83
05:00:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:00:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a311ab5
05:00:36 Searching Annoy index using 1 thread, search_k = 8300
05:00:37 Annoy recall = 100%
05:00:42 Commencing smooth kNN distance calibration using 1 thread
05:00:54 Initializing from normalized Laplacian + noise
05:00:54 Commencing optimization for 500 epochs, with 117840 positive edges
05:01:03 Optimization finished
[1] "83 0.12"05:01:04 UMAP embedding parameters a = 1.51 b = 0.9165
05:01:04 Read 1203 rows and found 38 numeric columns
05:01:04 Using Annoy for neighbor search, n_neighbors = 83
05:01:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:01:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87177f47ad
05:01:04 Searching Annoy index using 1 thread, search_k = 8300
05:01:05 Annoy recall = 100%
05:01:11 Commencing smooth kNN distance calibration using 1 thread
05:01:22 Initializing from normalized Laplacian + noise
05:01:22 Commencing optimization for 500 epochs, with 117840 positive edges
05:01:32 Optimization finished
[1] "83 0.13"05:01:32 UMAP embedding parameters a = 1.478 b = 0.9272
05:01:32 Read 1203 rows and found 38 numeric columns
05:01:32 Using Annoy for neighbor search, n_neighbors = 83
05:01:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:01:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757add1d0
05:01:33 Searching Annoy index using 1 thread, search_k = 8300
05:01:33 Annoy recall = 100%
05:01:39 Commencing smooth kNN distance calibration using 1 thread
05:01:51 Initializing from normalized Laplacian + noise
05:01:51 Commencing optimization for 500 epochs, with 117840 positive edges
05:02:00 Optimization finished
[1] "83 0.14"05:02:00 UMAP embedding parameters a = 1.446 b = 0.938
05:02:00 Read 1203 rows and found 38 numeric columns
05:02:00 Using Annoy for neighbor search, n_neighbors = 83
05:02:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:02:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87239d174d
05:02:01 Searching Annoy index using 1 thread, search_k = 8300
05:02:01 Annoy recall = 100%
05:02:07 Commencing smooth kNN distance calibration using 1 thread
05:02:19 Initializing from normalized Laplacian + noise
05:02:19 Commencing optimization for 500 epochs, with 117840 positive edges
05:02:28 Optimization finished
[1] "83 0.15"05:02:29 UMAP embedding parameters a = 1.414 b = 0.9488
05:02:29 Read 1203 rows and found 38 numeric columns
05:02:29 Using Annoy for neighbor search, n_neighbors = 83
05:02:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:02:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770cfaae5
05:02:29 Searching Annoy index using 1 thread, search_k = 8300
05:02:30 Annoy recall = 100%
05:02:36 Commencing smooth kNN distance calibration using 1 thread
05:02:47 Initializing from normalized Laplacian + noise
05:02:47 Commencing optimization for 500 epochs, with 117840 positive edges
05:02:57 Optimization finished
[1] "83 0.16"05:02:57 UMAP embedding parameters a = 1.383 b = 0.9596
05:02:57 Read 1203 rows and found 38 numeric columns
05:02:57 Using Annoy for neighbor search, n_neighbors = 83
05:02:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:02:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87254c4a87
05:02:57 Searching Annoy index using 1 thread, search_k = 8300
05:02:58 Annoy recall = 100%
05:03:04 Commencing smooth kNN distance calibration using 1 thread
05:03:16 Initializing from normalized Laplacian + noise
05:03:16 Commencing optimization for 500 epochs, with 117840 positive edges
05:03:25 Optimization finished
[1] "83 0.17"05:03:25 UMAP embedding parameters a = 1.352 b = 0.9704
05:03:25 Read 1203 rows and found 38 numeric columns
05:03:25 Using Annoy for neighbor search, n_neighbors = 83
05:03:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:03:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87130761b4
05:03:26 Searching Annoy index using 1 thread, search_k = 8300
05:03:26 Annoy recall = 100%
05:03:32 Commencing smooth kNN distance calibration using 1 thread
05:03:44 Initializing from normalized Laplacian + noise
05:03:44 Commencing optimization for 500 epochs, with 117840 positive edges
05:03:54 Optimization finished
[1] "83 0.18"05:03:54 UMAP embedding parameters a = 1.321 b = 0.9813
05:03:54 Read 1203 rows and found 38 numeric columns
05:03:54 Using Annoy for neighbor search, n_neighbors = 83
05:03:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:03:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753dac5f3
05:03:54 Searching Annoy index using 1 thread, search_k = 8300
05:03:55 Annoy recall = 100%
05:04:01 Commencing smooth kNN distance calibration using 1 thread
05:04:12 Initializing from normalized Laplacian + noise
05:04:12 Commencing optimization for 500 epochs, with 117840 positive edges
05:04:22 Optimization finished
[1] "83 0.19"05:04:22 UMAP embedding parameters a = 1.292 b = 0.9921
05:04:22 Read 1203 rows and found 38 numeric columns
05:04:22 Using Annoy for neighbor search, n_neighbors = 83
05:04:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:04:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876263130a
05:04:23 Searching Annoy index using 1 thread, search_k = 8300
05:04:23 Annoy recall = 100%
05:04:29 Commencing smooth kNN distance calibration using 1 thread
05:04:41 Initializing from normalized Laplacian + noise
05:04:41 Commencing optimization for 500 epochs, with 117840 positive edges
05:04:50 Optimization finished
[1] "83 0.2"05:04:51 UMAP embedding parameters a = 1.262 b = 1.003
05:04:51 Read 1203 rows and found 38 numeric columns
05:04:51 Using Annoy for neighbor search, n_neighbors = 83
05:04:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:04:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742e1baf3
05:04:51 Searching Annoy index using 1 thread, search_k = 8300
05:04:52 Annoy recall = 100%
05:04:58 Commencing smooth kNN distance calibration using 1 thread
05:05:09 Initializing from normalized Laplacian + noise
05:05:09 Commencing optimization for 500 epochs, with 117840 positive edges
05:05:19 Optimization finished
[1] "84 0"05:05:19 UMAP embedding parameters a = 1.933 b = 0.7905
05:05:19 Read 1203 rows and found 38 numeric columns
05:05:19 Using Annoy for neighbor search, n_neighbors = 84
05:05:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:05:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871fc6cc68
05:05:19 Searching Annoy index using 1 thread, search_k = 8400
05:05:20 Annoy recall = 100%
05:05:26 Commencing smooth kNN distance calibration using 1 thread
05:05:38 Initializing from normalized Laplacian + noise
05:05:38 Commencing optimization for 500 epochs, with 119146 positive edges
05:05:47 Optimization finished
[1] "84 0.01"05:05:47 UMAP embedding parameters a = 1.896 b = 0.8006
05:05:47 Read 1203 rows and found 38 numeric columns
05:05:47 Using Annoy for neighbor search, n_neighbors = 84
05:05:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:05:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87400b0d41
05:05:48 Searching Annoy index using 1 thread, search_k = 8400
05:05:49 Annoy recall = 100%
05:05:54 Commencing smooth kNN distance calibration using 1 thread
05:06:06 Initializing from normalized Laplacian + noise
05:06:06 Commencing optimization for 500 epochs, with 119146 positive edges
05:06:16 Optimization finished
[1] "84 0.02"05:06:16 UMAP embedding parameters a = 1.859 b = 0.8109
05:06:16 Read 1203 rows and found 38 numeric columns
05:06:16 Using Annoy for neighbor search, n_neighbors = 84
05:06:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:06:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871999f866
05:06:16 Searching Annoy index using 1 thread, search_k = 8400
05:06:17 Annoy recall = 100%
05:06:23 Commencing smooth kNN distance calibration using 1 thread
05:06:35 Initializing from normalized Laplacian + noise
05:06:35 Commencing optimization for 500 epochs, with 119146 positive edges
05:06:44 Optimization finished
[1] "84 0.03"05:06:44 UMAP embedding parameters a = 1.822 b = 0.8212
05:06:44 Read 1203 rows and found 38 numeric columns
05:06:44 Using Annoy for neighbor search, n_neighbors = 84
05:06:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:06:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876470c5ca
05:06:45 Searching Annoy index using 1 thread, search_k = 8400
05:06:45 Annoy recall = 100%
05:06:51 Commencing smooth kNN distance calibration using 1 thread
05:07:03 Initializing from normalized Laplacian + noise
05:07:03 Commencing optimization for 500 epochs, with 119146 positive edges
05:07:13 Optimization finished
[1] "84 0.04"05:07:13 UMAP embedding parameters a = 1.786 b = 0.8316
05:07:13 Read 1203 rows and found 38 numeric columns
05:07:13 Using Annoy for neighbor search, n_neighbors = 84
05:07:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:07:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777a281ae
05:07:13 Searching Annoy index using 1 thread, search_k = 8400
05:07:14 Annoy recall = 100%
05:07:20 Commencing smooth kNN distance calibration using 1 thread
05:07:32 Initializing from normalized Laplacian + noise
05:07:32 Commencing optimization for 500 epochs, with 119146 positive edges
05:07:41 Optimization finished
[1] "84 0.05"05:07:41 UMAP embedding parameters a = 1.75 b = 0.8421
05:07:41 Read 1203 rows and found 38 numeric columns
05:07:41 Using Annoy for neighbor search, n_neighbors = 84
05:07:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:07:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ee56429
05:07:42 Searching Annoy index using 1 thread, search_k = 8400
05:07:42 Annoy recall = 100%
05:07:48 Commencing smooth kNN distance calibration using 1 thread
05:08:00 Initializing from normalized Laplacian + noise
05:08:00 Commencing optimization for 500 epochs, with 119146 positive edges
05:08:10 Optimization finished
[1] "84 0.06"05:08:10 UMAP embedding parameters a = 1.715 b = 0.8526
05:08:10 Read 1203 rows and found 38 numeric columns
05:08:10 Using Annoy for neighbor search, n_neighbors = 84
05:08:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:08:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d7f04b6
05:08:10 Searching Annoy index using 1 thread, search_k = 8400
05:08:11 Annoy recall = 100%
05:08:17 Commencing smooth kNN distance calibration using 1 thread
05:08:29 Initializing from normalized Laplacian + noise
05:08:29 Commencing optimization for 500 epochs, with 119146 positive edges
05:08:38 Optimization finished
[1] "84 0.07"05:08:38 UMAP embedding parameters a = 1.68 b = 0.8631
05:08:38 Read 1203 rows and found 38 numeric columns
05:08:38 Using Annoy for neighbor search, n_neighbors = 84
05:08:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:08:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c49db94
05:08:39 Searching Annoy index using 1 thread, search_k = 8400
05:08:40 Annoy recall = 100%
05:08:45 Commencing smooth kNN distance calibration using 1 thread
05:08:57 Initializing from normalized Laplacian + noise
05:08:57 Commencing optimization for 500 epochs, with 119146 positive edges
05:09:07 Optimization finished
[1] "84 0.08"05:09:07 UMAP embedding parameters a = 1.645 b = 0.8737
05:09:07 Read 1203 rows and found 38 numeric columns
05:09:07 Using Annoy for neighbor search, n_neighbors = 84
05:09:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:09:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ce51e3f
05:09:08 Searching Annoy index using 1 thread, search_k = 8400
05:09:08 Annoy recall = 100%
05:09:14 Commencing smooth kNN distance calibration using 1 thread
05:09:26 Initializing from normalized Laplacian + noise
05:09:26 Commencing optimization for 500 epochs, with 119146 positive edges
05:09:35 Optimization finished
[1] "84 0.09"05:09:36 UMAP embedding parameters a = 1.611 b = 0.8844
05:09:36 Read 1203 rows and found 38 numeric columns
05:09:36 Using Annoy for neighbor search, n_neighbors = 84
05:09:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:09:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e726e47
05:09:36 Searching Annoy index using 1 thread, search_k = 8400
05:09:37 Annoy recall = 100%
05:09:43 Commencing smooth kNN distance calibration using 1 thread
05:09:54 Initializing from normalized Laplacian + noise
05:09:55 Commencing optimization for 500 epochs, with 119146 positive edges
05:10:04 Optimization finished
[1] "84 0.1"05:10:04 UMAP embedding parameters a = 1.577 b = 0.8951
05:10:04 Read 1203 rows and found 38 numeric columns
05:10:04 Using Annoy for neighbor search, n_neighbors = 84
05:10:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:10:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730398392
05:10:05 Searching Annoy index using 1 thread, search_k = 8400
05:10:05 Annoy recall = 100%
05:10:11 Commencing smooth kNN distance calibration using 1 thread
05:10:23 Initializing from normalized Laplacian + noise
05:10:23 Commencing optimization for 500 epochs, with 119146 positive edges
05:10:32 Optimization finished
[1] "84 0.11"05:10:33 UMAP embedding parameters a = 1.544 b = 0.9058
05:10:33 Read 1203 rows and found 38 numeric columns
05:10:33 Using Annoy for neighbor search, n_neighbors = 84
05:10:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:10:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87178779ac
05:10:33 Searching Annoy index using 1 thread, search_k = 8400
05:10:34 Annoy recall = 100%
05:10:40 Commencing smooth kNN distance calibration using 1 thread
05:10:52 Initializing from normalized Laplacian + noise
05:10:52 Commencing optimization for 500 epochs, with 119146 positive edges
05:11:01 Optimization finished
[1] "84 0.12"05:11:01 UMAP embedding parameters a = 1.51 b = 0.9165
05:11:01 Read 1203 rows and found 38 numeric columns
05:11:01 Using Annoy for neighbor search, n_neighbors = 84
05:11:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:11:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87352f3365
05:11:02 Searching Annoy index using 1 thread, search_k = 8400
05:11:03 Annoy recall = 100%
05:11:08 Commencing smooth kNN distance calibration using 1 thread
05:11:20 Initializing from normalized Laplacian + noise
05:11:20 Commencing optimization for 500 epochs, with 119146 positive edges
05:11:30 Optimization finished
[1] "84 0.13"05:11:30 UMAP embedding parameters a = 1.478 b = 0.9272
05:11:30 Read 1203 rows and found 38 numeric columns
05:11:30 Using Annoy for neighbor search, n_neighbors = 84
05:11:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:11:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876eaf809e
05:11:30 Searching Annoy index using 1 thread, search_k = 8400
05:11:31 Annoy recall = 100%
05:11:37 Commencing smooth kNN distance calibration using 1 thread
05:11:49 Initializing from normalized Laplacian + noise
05:11:49 Commencing optimization for 500 epochs, with 119146 positive edges
05:11:58 Optimization finished
[1] "84 0.14"05:11:59 UMAP embedding parameters a = 1.446 b = 0.938
05:11:59 Read 1203 rows and found 38 numeric columns
05:11:59 Using Annoy for neighbor search, n_neighbors = 84
05:11:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:11:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a795f2
05:11:59 Searching Annoy index using 1 thread, search_k = 8400
05:12:00 Annoy recall = 100%
05:12:06 Commencing smooth kNN distance calibration using 1 thread
05:12:18 Initializing from normalized Laplacian + noise
05:12:18 Commencing optimization for 500 epochs, with 119146 positive edges
05:12:27 Optimization finished
[1] "84 0.15"05:12:27 UMAP embedding parameters a = 1.414 b = 0.9488
05:12:27 Read 1203 rows and found 38 numeric columns
05:12:27 Using Annoy for neighbor search, n_neighbors = 84
05:12:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:12:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87548a5b82
05:12:28 Searching Annoy index using 1 thread, search_k = 8400
05:12:28 Annoy recall = 100%
05:12:34 Commencing smooth kNN distance calibration using 1 thread
05:12:46 Initializing from normalized Laplacian + noise
05:12:46 Commencing optimization for 500 epochs, with 119146 positive edges
05:12:56 Optimization finished
[1] "84 0.16"05:12:56 UMAP embedding parameters a = 1.383 b = 0.9596
05:12:56 Read 1203 rows and found 38 numeric columns
05:12:56 Using Annoy for neighbor search, n_neighbors = 84
05:12:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:12:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878e2457b
05:12:56 Searching Annoy index using 1 thread, search_k = 8400
05:12:57 Annoy recall = 100%
05:13:03 Commencing smooth kNN distance calibration using 1 thread
05:13:15 Initializing from normalized Laplacian + noise
05:13:15 Commencing optimization for 500 epochs, with 119146 positive edges
05:13:24 Optimization finished
[1] "84 0.17"05:13:25 UMAP embedding parameters a = 1.352 b = 0.9704
05:13:25 Read 1203 rows and found 38 numeric columns
05:13:25 Using Annoy for neighbor search, n_neighbors = 84
05:13:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:13:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772f99b24
05:13:25 Searching Annoy index using 1 thread, search_k = 8400
05:13:26 Annoy recall = 100%
05:13:32 Commencing smooth kNN distance calibration using 1 thread
05:13:43 Initializing from normalized Laplacian + noise
05:13:44 Commencing optimization for 500 epochs, with 119146 positive edges
05:13:53 Optimization finished
[1] "84 0.18"05:13:53 UMAP embedding parameters a = 1.321 b = 0.9813
05:13:53 Read 1203 rows and found 38 numeric columns
05:13:53 Using Annoy for neighbor search, n_neighbors = 84
05:13:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:13:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771977985
05:13:54 Searching Annoy index using 1 thread, search_k = 8400
05:13:54 Annoy recall = 100%
05:14:00 Commencing smooth kNN distance calibration using 1 thread
05:14:12 Initializing from normalized Laplacian + noise
05:14:12 Commencing optimization for 500 epochs, with 119146 positive edges
05:14:22 Optimization finished
[1] "84 0.19"05:14:22 UMAP embedding parameters a = 1.292 b = 0.9921
05:14:22 Read 1203 rows and found 38 numeric columns
05:14:22 Using Annoy for neighbor search, n_neighbors = 84
05:14:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:14:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755d72b7d
05:14:23 Searching Annoy index using 1 thread, search_k = 8400
05:14:23 Annoy recall = 100%
05:14:29 Commencing smooth kNN distance calibration using 1 thread
05:14:41 Initializing from normalized Laplacian + noise
05:14:41 Commencing optimization for 500 epochs, with 119146 positive edges
05:14:50 Optimization finished
[1] "84 0.2"05:14:51 UMAP embedding parameters a = 1.262 b = 1.003
05:14:51 Read 1203 rows and found 38 numeric columns
05:14:51 Using Annoy for neighbor search, n_neighbors = 84
05:14:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:14:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873541e5b1
05:14:51 Searching Annoy index using 1 thread, search_k = 8400
05:14:52 Annoy recall = 100%
05:14:58 Commencing smooth kNN distance calibration using 1 thread
05:15:10 Initializing from normalized Laplacian + noise
05:15:10 Commencing optimization for 500 epochs, with 119146 positive edges
05:15:19 Optimization finished
[1] "85 0"05:15:19 UMAP embedding parameters a = 1.933 b = 0.7905
05:15:20 Read 1203 rows and found 38 numeric columns
05:15:20 Using Annoy for neighbor search, n_neighbors = 85
05:15:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:15:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bc8943a
05:15:20 Searching Annoy index using 1 thread, search_k = 8500
05:15:21 Annoy recall = 100%
05:15:26 Commencing smooth kNN distance calibration using 1 thread
05:15:38 Initializing from normalized Laplacian + noise
05:15:38 Commencing optimization for 500 epochs, with 120478 positive edges
05:15:48 Optimization finished
[1] "85 0.01"05:15:48 UMAP embedding parameters a = 1.896 b = 0.8006
05:15:48 Read 1203 rows and found 38 numeric columns
05:15:48 Using Annoy for neighbor search, n_neighbors = 85
05:15:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:15:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d56732a
05:15:49 Searching Annoy index using 1 thread, search_k = 8500
05:15:49 Annoy recall = 100%
05:15:55 Commencing smooth kNN distance calibration using 1 thread
05:16:07 Initializing from normalized Laplacian + noise
05:16:07 Commencing optimization for 500 epochs, with 120478 positive edges
05:16:17 Optimization finished
[1] "85 0.02"05:16:17 UMAP embedding parameters a = 1.859 b = 0.8109
05:16:17 Read 1203 rows and found 38 numeric columns
05:16:17 Using Annoy for neighbor search, n_neighbors = 85
05:16:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:16:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cefb781
05:16:17 Searching Annoy index using 1 thread, search_k = 8500
05:16:18 Annoy recall = 100%
05:16:24 Commencing smooth kNN distance calibration using 1 thread
05:16:36 Initializing from normalized Laplacian + noise
05:16:36 Commencing optimization for 500 epochs, with 120478 positive edges
05:16:45 Optimization finished
[1] "85 0.03"05:16:46 UMAP embedding parameters a = 1.822 b = 0.8212
05:16:46 Read 1203 rows and found 38 numeric columns
05:16:46 Using Annoy for neighbor search, n_neighbors = 85
05:16:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:16:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f65ab87
05:16:46 Searching Annoy index using 1 thread, search_k = 8500
05:16:47 Annoy recall = 100%
05:16:53 Commencing smooth kNN distance calibration using 1 thread
05:17:05 Initializing from normalized Laplacian + noise
05:17:05 Commencing optimization for 500 epochs, with 120478 positive edges
05:17:14 Optimization finished
[1] "85 0.04"05:17:15 UMAP embedding parameters a = 1.786 b = 0.8316
05:17:15 Read 1203 rows and found 38 numeric columns
05:17:15 Using Annoy for neighbor search, n_neighbors = 85
05:17:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:17:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e261e10
05:17:15 Searching Annoy index using 1 thread, search_k = 8500
05:17:16 Annoy recall = 100%
05:17:22 Commencing smooth kNN distance calibration using 1 thread
05:17:33 Initializing from normalized Laplacian + noise
05:17:34 Commencing optimization for 500 epochs, with 120478 positive edges
05:17:43 Optimization finished
[1] "85 0.05"05:17:43 UMAP embedding parameters a = 1.75 b = 0.8421
05:17:43 Read 1203 rows and found 38 numeric columns
05:17:43 Using Annoy for neighbor search, n_neighbors = 85
05:17:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:17:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87323c0208
05:17:44 Searching Annoy index using 1 thread, search_k = 8500
05:17:44 Annoy recall = 100%
05:17:50 Commencing smooth kNN distance calibration using 1 thread
05:18:02 Initializing from normalized Laplacian + noise
05:18:02 Commencing optimization for 500 epochs, with 120478 positive edges
05:18:12 Optimization finished
[1] "85 0.06"05:18:12 UMAP embedding parameters a = 1.715 b = 0.8526
05:18:12 Read 1203 rows and found 38 numeric columns
05:18:12 Using Annoy for neighbor search, n_neighbors = 85
05:18:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:18:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87426d0d3b
05:18:13 Searching Annoy index using 1 thread, search_k = 8500
05:18:13 Annoy recall = 100%
05:18:19 Commencing smooth kNN distance calibration using 1 thread
05:18:31 Initializing from normalized Laplacian + noise
05:18:31 Commencing optimization for 500 epochs, with 120478 positive edges
05:18:41 Optimization finished
[1] "85 0.07"05:18:41 UMAP embedding parameters a = 1.68 b = 0.8631
05:18:41 Read 1203 rows and found 38 numeric columns
05:18:41 Using Annoy for neighbor search, n_neighbors = 85
05:18:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:18:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873200e403
05:18:41 Searching Annoy index using 1 thread, search_k = 8500
05:18:42 Annoy recall = 100%
05:18:48 Commencing smooth kNN distance calibration using 1 thread
05:19:00 Initializing from normalized Laplacian + noise
05:19:00 Commencing optimization for 500 epochs, with 120478 positive edges
05:19:10 Optimization finished
[1] "85 0.08"05:19:10 UMAP embedding parameters a = 1.645 b = 0.8737
05:19:10 Read 1203 rows and found 38 numeric columns
05:19:10 Using Annoy for neighbor search, n_neighbors = 85
05:19:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:19:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87149f1512
05:19:10 Searching Annoy index using 1 thread, search_k = 8500
05:19:11 Annoy recall = 100%
05:19:17 Commencing smooth kNN distance calibration using 1 thread
05:19:29 Initializing from normalized Laplacian + noise
05:19:29 Commencing optimization for 500 epochs, with 120478 positive edges
05:19:38 Optimization finished
[1] "85 0.09"05:19:39 UMAP embedding parameters a = 1.611 b = 0.8844
05:19:39 Read 1203 rows and found 38 numeric columns
05:19:39 Using Annoy for neighbor search, n_neighbors = 85
05:19:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:19:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754ec82e
05:19:39 Searching Annoy index using 1 thread, search_k = 8500
05:19:40 Annoy recall = 100%
05:19:46 Commencing smooth kNN distance calibration using 1 thread
05:19:58 Initializing from normalized Laplacian + noise
05:19:58 Commencing optimization for 500 epochs, with 120478 positive edges
05:20:07 Optimization finished
[1] "85 0.1"05:20:08 UMAP embedding parameters a = 1.577 b = 0.8951
05:20:08 Read 1203 rows and found 38 numeric columns
05:20:08 Using Annoy for neighbor search, n_neighbors = 85
05:20:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:20:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751c7b06b
05:20:08 Searching Annoy index using 1 thread, search_k = 8500
05:20:09 Annoy recall = 100%
05:20:15 Commencing smooth kNN distance calibration using 1 thread
05:20:27 Initializing from normalized Laplacian + noise
05:20:27 Commencing optimization for 500 epochs, with 120478 positive edges
05:20:36 Optimization finished
[1] "85 0.11"05:20:36 UMAP embedding parameters a = 1.544 b = 0.9058
05:20:36 Read 1203 rows and found 38 numeric columns
05:20:36 Using Annoy for neighbor search, n_neighbors = 85
05:20:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:20:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754aa2253
05:20:37 Searching Annoy index using 1 thread, search_k = 8500
05:20:38 Annoy recall = 100%
05:20:43 Commencing smooth kNN distance calibration using 1 thread
05:20:55 Initializing from normalized Laplacian + noise
05:20:55 Commencing optimization for 500 epochs, with 120478 positive edges
05:21:05 Optimization finished
[1] "85 0.12"05:21:05 UMAP embedding parameters a = 1.51 b = 0.9165
05:21:05 Read 1203 rows and found 38 numeric columns
05:21:05 Using Annoy for neighbor search, n_neighbors = 85
05:21:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:21:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ee8c095
05:21:06 Searching Annoy index using 1 thread, search_k = 8500
05:21:06 Annoy recall = 100%
05:21:12 Commencing smooth kNN distance calibration using 1 thread
05:21:24 Initializing from normalized Laplacian + noise
05:21:25 Commencing optimization for 500 epochs, with 120478 positive edges
05:21:34 Optimization finished
[1] "85 0.13"05:21:34 UMAP embedding parameters a = 1.478 b = 0.9272
05:21:34 Read 1203 rows and found 38 numeric columns
05:21:34 Using Annoy for neighbor search, n_neighbors = 85
05:21:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:21:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736387635
05:21:35 Searching Annoy index using 1 thread, search_k = 8500
05:21:35 Annoy recall = 100%
05:21:41 Commencing smooth kNN distance calibration using 1 thread
05:21:53 Initializing from normalized Laplacian + noise
05:21:53 Commencing optimization for 500 epochs, with 120478 positive edges
05:22:03 Optimization finished
[1] "85 0.14"05:22:03 UMAP embedding parameters a = 1.446 b = 0.938
05:22:03 Read 1203 rows and found 38 numeric columns
05:22:03 Using Annoy for neighbor search, n_neighbors = 85
05:22:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:22:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c4ca401
05:22:04 Searching Annoy index using 1 thread, search_k = 8500
05:22:04 Annoy recall = 100%
05:22:10 Commencing smooth kNN distance calibration using 1 thread
05:22:22 Initializing from normalized Laplacian + noise
05:22:22 Commencing optimization for 500 epochs, with 120478 positive edges
05:22:32 Optimization finished
[1] "85 0.15"05:22:32 UMAP embedding parameters a = 1.414 b = 0.9488
05:22:32 Read 1203 rows and found 38 numeric columns
05:22:32 Using Annoy for neighbor search, n_neighbors = 85
05:22:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:22:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dce24be
05:22:33 Searching Annoy index using 1 thread, search_k = 8500
05:22:33 Annoy recall = 100%
05:22:39 Commencing smooth kNN distance calibration using 1 thread
05:22:51 Initializing from normalized Laplacian + noise
05:22:51 Commencing optimization for 500 epochs, with 120478 positive edges
05:23:01 Optimization finished
[1] "85 0.16"05:23:01 UMAP embedding parameters a = 1.383 b = 0.9596
05:23:01 Read 1203 rows and found 38 numeric columns
05:23:01 Using Annoy for neighbor search, n_neighbors = 85
05:23:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:23:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713b77aeb
05:23:01 Searching Annoy index using 1 thread, search_k = 8500
05:23:02 Annoy recall = 100%
05:23:08 Commencing smooth kNN distance calibration using 1 thread
05:23:20 Initializing from normalized Laplacian + noise
05:23:20 Commencing optimization for 500 epochs, with 120478 positive edges
05:23:30 Optimization finished
[1] "85 0.17"05:23:30 UMAP embedding parameters a = 1.352 b = 0.9704
05:23:30 Read 1203 rows and found 38 numeric columns
05:23:30 Using Annoy for neighbor search, n_neighbors = 85
05:23:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:23:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768967f96
05:23:31 Searching Annoy index using 1 thread, search_k = 8500
05:23:31 Annoy recall = 100%
05:23:37 Commencing smooth kNN distance calibration using 1 thread
05:23:49 Initializing from normalized Laplacian + noise
05:23:49 Commencing optimization for 500 epochs, with 120478 positive edges
05:23:59 Optimization finished
[1] "85 0.18"05:23:59 UMAP embedding parameters a = 1.321 b = 0.9813
05:23:59 Read 1203 rows and found 38 numeric columns
05:23:59 Using Annoy for neighbor search, n_neighbors = 85
05:23:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:23:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ab342fd
05:23:59 Searching Annoy index using 1 thread, search_k = 8500
05:24:00 Annoy recall = 100%
05:24:06 Commencing smooth kNN distance calibration using 1 thread
05:24:18 Initializing from normalized Laplacian + noise
05:24:18 Commencing optimization for 500 epochs, with 120478 positive edges
05:24:28 Optimization finished
[1] "85 0.19"05:24:28 UMAP embedding parameters a = 1.292 b = 0.9921
05:24:28 Read 1203 rows and found 38 numeric columns
05:24:28 Using Annoy for neighbor search, n_neighbors = 85
05:24:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:24:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87229e933
05:24:29 Searching Annoy index using 1 thread, search_k = 8500
05:24:29 Annoy recall = 100%
05:24:35 Commencing smooth kNN distance calibration using 1 thread
05:24:47 Initializing from normalized Laplacian + noise
05:24:47 Commencing optimization for 500 epochs, with 120478 positive edges
05:24:57 Optimization finished
[1] "85 0.2"05:24:57 UMAP embedding parameters a = 1.262 b = 1.003
05:24:57 Read 1203 rows and found 38 numeric columns
05:24:57 Using Annoy for neighbor search, n_neighbors = 85
05:24:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:24:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718d00328
05:24:57 Searching Annoy index using 1 thread, search_k = 8500
05:24:58 Annoy recall = 100%
05:25:04 Commencing smooth kNN distance calibration using 1 thread
05:25:16 Initializing from normalized Laplacian + noise
05:25:16 Commencing optimization for 500 epochs, with 120478 positive edges
05:25:26 Optimization finished
[1] "86 0"05:25:26 UMAP embedding parameters a = 1.933 b = 0.7905
05:25:26 Read 1203 rows and found 38 numeric columns
05:25:26 Using Annoy for neighbor search, n_neighbors = 86
05:25:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:25:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87123abca9
05:25:27 Searching Annoy index using 1 thread, search_k = 8600
05:25:27 Annoy recall = 100%
05:25:33 Commencing smooth kNN distance calibration using 1 thread
05:25:45 Initializing from normalized Laplacian + noise
05:25:45 Commencing optimization for 500 epochs, with 121806 positive edges
05:25:55 Optimization finished
[1] "86 0.01"05:25:55 UMAP embedding parameters a = 1.896 b = 0.8006
05:25:55 Read 1203 rows and found 38 numeric columns
05:25:55 Using Annoy for neighbor search, n_neighbors = 86
05:25:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:25:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737591c98
05:25:56 Searching Annoy index using 1 thread, search_k = 8600
05:25:56 Annoy recall = 100%
05:26:02 Commencing smooth kNN distance calibration using 1 thread
05:26:14 Initializing from normalized Laplacian + noise
05:26:14 Commencing optimization for 500 epochs, with 121806 positive edges
05:26:24 Optimization finished
[1] "86 0.02"05:26:24 UMAP embedding parameters a = 1.859 b = 0.8109
05:26:24 Read 1203 rows and found 38 numeric columns
05:26:24 Using Annoy for neighbor search, n_neighbors = 86
05:26:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:26:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777f83c6
05:26:25 Searching Annoy index using 1 thread, search_k = 8600
05:26:25 Annoy recall = 100%
05:26:31 Commencing smooth kNN distance calibration using 1 thread
05:26:43 Initializing from normalized Laplacian + noise
05:26:44 Commencing optimization for 500 epochs, with 121806 positive edges
05:26:53 Optimization finished
[1] "86 0.03"05:26:53 UMAP embedding parameters a = 1.822 b = 0.8212
05:26:53 Read 1203 rows and found 38 numeric columns
05:26:53 Using Annoy for neighbor search, n_neighbors = 86
05:26:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:26:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718e2529b
05:26:54 Searching Annoy index using 1 thread, search_k = 8600
05:26:54 Annoy recall = 100%
05:27:00 Commencing smooth kNN distance calibration using 1 thread
05:27:12 Initializing from normalized Laplacian + noise
05:27:12 Commencing optimization for 500 epochs, with 121806 positive edges
05:27:22 Optimization finished
[1] "86 0.04"05:27:22 UMAP embedding parameters a = 1.786 b = 0.8316
05:27:22 Read 1203 rows and found 38 numeric columns
05:27:22 Using Annoy for neighbor search, n_neighbors = 86
05:27:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:27:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87be3781a
05:27:23 Searching Annoy index using 1 thread, search_k = 8600
05:27:23 Annoy recall = 100%
05:27:30 Commencing smooth kNN distance calibration using 1 thread
05:27:42 Initializing from normalized Laplacian + noise
05:27:42 Commencing optimization for 500 epochs, with 121806 positive edges
05:27:51 Optimization finished
[1] "86 0.05"05:27:52 UMAP embedding parameters a = 1.75 b = 0.8421
05:27:52 Read 1203 rows and found 38 numeric columns
05:27:52 Using Annoy for neighbor search, n_neighbors = 86
05:27:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:27:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871061c941
05:27:52 Searching Annoy index using 1 thread, search_k = 8600
05:27:53 Annoy recall = 100%
05:27:58 Commencing smooth kNN distance calibration using 1 thread
05:28:10 Initializing from normalized Laplacian + noise
05:28:10 Commencing optimization for 500 epochs, with 121806 positive edges
05:28:20 Optimization finished
[1] "86 0.06"05:28:20 UMAP embedding parameters a = 1.715 b = 0.8526
05:28:20 Read 1203 rows and found 38 numeric columns
05:28:20 Using Annoy for neighbor search, n_neighbors = 86
05:28:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:28:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bdbedc0
05:28:20 Searching Annoy index using 1 thread, search_k = 8600
05:28:21 Annoy recall = 100%
05:28:27 Commencing smooth kNN distance calibration using 1 thread
05:28:39 Initializing from normalized Laplacian + noise
05:28:39 Commencing optimization for 500 epochs, with 121806 positive edges
05:28:49 Optimization finished
[1] "86 0.07"05:28:49 UMAP embedding parameters a = 1.68 b = 0.8631
05:28:49 Read 1203 rows and found 38 numeric columns
05:28:49 Using Annoy for neighbor search, n_neighbors = 86
05:28:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:28:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d7af19f
05:28:49 Searching Annoy index using 1 thread, search_k = 8600
05:28:50 Annoy recall = 100%
05:28:56 Commencing smooth kNN distance calibration using 1 thread
05:29:07 Initializing from normalized Laplacian + noise
05:29:08 Commencing optimization for 500 epochs, with 121806 positive edges
05:29:17 Optimization finished
[1] "86 0.08"05:29:17 UMAP embedding parameters a = 1.645 b = 0.8737
05:29:17 Read 1203 rows and found 38 numeric columns
05:29:17 Using Annoy for neighbor search, n_neighbors = 86
05:29:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:29:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876638f4be
05:29:18 Searching Annoy index using 1 thread, search_k = 8600
05:29:18 Annoy recall = 100%
05:29:24 Commencing smooth kNN distance calibration using 1 thread
05:29:36 Initializing from normalized Laplacian + noise
05:29:36 Commencing optimization for 500 epochs, with 121806 positive edges
05:29:46 Optimization finished
[1] "86 0.09"05:29:46 UMAP embedding parameters a = 1.611 b = 0.8844
05:29:46 Read 1203 rows and found 38 numeric columns
05:29:46 Using Annoy for neighbor search, n_neighbors = 86
05:29:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:29:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87411dd371
05:29:46 Searching Annoy index using 1 thread, search_k = 8600
05:29:47 Annoy recall = 100%
05:29:53 Commencing smooth kNN distance calibration using 1 thread
05:30:05 Initializing from normalized Laplacian + noise
05:30:05 Commencing optimization for 500 epochs, with 121806 positive edges
05:30:14 Optimization finished
[1] "86 0.1"05:30:15 UMAP embedding parameters a = 1.577 b = 0.8951
05:30:15 Read 1203 rows and found 38 numeric columns
05:30:15 Using Annoy for neighbor search, n_neighbors = 86
05:30:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:30:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8794385d9
05:30:15 Searching Annoy index using 1 thread, search_k = 8600
05:30:16 Annoy recall = 100%
05:30:22 Commencing smooth kNN distance calibration using 1 thread
05:30:33 Initializing from normalized Laplacian + noise
05:30:34 Commencing optimization for 500 epochs, with 121806 positive edges
05:30:43 Optimization finished
[1] "86 0.11"05:30:43 UMAP embedding parameters a = 1.544 b = 0.9058
05:30:43 Read 1203 rows and found 38 numeric columns
05:30:43 Using Annoy for neighbor search, n_neighbors = 86
05:30:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:30:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87538f67e9
05:30:44 Searching Annoy index using 1 thread, search_k = 8600
05:30:44 Annoy recall = 100%
05:30:50 Commencing smooth kNN distance calibration using 1 thread
05:31:02 Initializing from normalized Laplacian + noise
05:31:02 Commencing optimization for 500 epochs, with 121806 positive edges
05:31:12 Optimization finished
[1] "86 0.12"05:31:12 UMAP embedding parameters a = 1.51 b = 0.9165
05:31:12 Read 1203 rows and found 38 numeric columns
05:31:12 Using Annoy for neighbor search, n_neighbors = 86
05:31:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:31:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e0d8af2
05:31:12 Searching Annoy index using 1 thread, search_k = 8600
05:31:13 Annoy recall = 100%
05:31:19 Commencing smooth kNN distance calibration using 1 thread
05:31:31 Initializing from normalized Laplacian + noise
05:31:31 Commencing optimization for 500 epochs, with 121806 positive edges
05:31:40 Optimization finished
[1] "86 0.13"05:31:41 UMAP embedding parameters a = 1.478 b = 0.9272
05:31:41 Read 1203 rows and found 38 numeric columns
05:31:41 Using Annoy for neighbor search, n_neighbors = 86
05:31:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:31:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738a93160
05:31:41 Searching Annoy index using 1 thread, search_k = 8600
05:31:42 Annoy recall = 100%
05:31:48 Commencing smooth kNN distance calibration using 1 thread
05:32:00 Initializing from normalized Laplacian + noise
05:32:00 Commencing optimization for 500 epochs, with 121806 positive edges
05:32:09 Optimization finished
[1] "86 0.14"05:32:09 UMAP embedding parameters a = 1.446 b = 0.938
05:32:09 Read 1203 rows and found 38 numeric columns
05:32:09 Using Annoy for neighbor search, n_neighbors = 86
05:32:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:32:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731b585f9
05:32:10 Searching Annoy index using 1 thread, search_k = 8600
05:32:10 Annoy recall = 100%
05:32:16 Commencing smooth kNN distance calibration using 1 thread
05:32:28 Initializing from normalized Laplacian + noise
05:32:28 Commencing optimization for 500 epochs, with 121806 positive edges
05:32:38 Optimization finished
[1] "86 0.15"05:32:38 UMAP embedding parameters a = 1.414 b = 0.9488
05:32:38 Read 1203 rows and found 38 numeric columns
05:32:38 Using Annoy for neighbor search, n_neighbors = 86
05:32:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:32:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87498cfa
05:32:38 Searching Annoy index using 1 thread, search_k = 8600
05:32:39 Annoy recall = 100%
05:32:45 Commencing smooth kNN distance calibration using 1 thread
05:32:57 Initializing from normalized Laplacian + noise
05:32:57 Commencing optimization for 500 epochs, with 121806 positive edges
05:33:07 Optimization finished
[1] "86 0.16"05:33:07 UMAP embedding parameters a = 1.383 b = 0.9596
05:33:07 Read 1203 rows and found 38 numeric columns
05:33:07 Using Annoy for neighbor search, n_neighbors = 86
05:33:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:33:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b163e9c
05:33:07 Searching Annoy index using 1 thread, search_k = 8600
05:33:08 Annoy recall = 100%
05:33:14 Commencing smooth kNN distance calibration using 1 thread
05:33:26 Initializing from normalized Laplacian + noise
05:33:26 Commencing optimization for 500 epochs, with 121806 positive edges
05:33:35 Optimization finished
[1] "86 0.17"05:33:35 UMAP embedding parameters a = 1.352 b = 0.9704
05:33:35 Read 1203 rows and found 38 numeric columns
05:33:36 Using Annoy for neighbor search, n_neighbors = 86
05:33:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:33:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763b669fc
05:33:36 Searching Annoy index using 1 thread, search_k = 8600
05:33:37 Annoy recall = 100%
05:33:43 Commencing smooth kNN distance calibration using 1 thread
05:33:55 Initializing from normalized Laplacian + noise
05:33:55 Commencing optimization for 500 epochs, with 121806 positive edges
05:34:04 Optimization finished
[1] "86 0.18"05:34:05 UMAP embedding parameters a = 1.321 b = 0.9813
05:34:05 Read 1203 rows and found 38 numeric columns
05:34:05 Using Annoy for neighbor search, n_neighbors = 86
05:34:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:34:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714e8a20d
05:34:05 Searching Annoy index using 1 thread, search_k = 8600
05:34:06 Annoy recall = 100%
05:34:12 Commencing smooth kNN distance calibration using 1 thread
05:34:23 Initializing from normalized Laplacian + noise
05:34:24 Commencing optimization for 500 epochs, with 121806 positive edges
05:34:33 Optimization finished
[1] "86 0.19"05:34:34 UMAP embedding parameters a = 1.292 b = 0.9921
05:34:34 Read 1203 rows and found 38 numeric columns
05:34:34 Using Annoy for neighbor search, n_neighbors = 86
05:34:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:34:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876506ca
05:34:34 Searching Annoy index using 1 thread, search_k = 8600
05:34:35 Annoy recall = 100%
05:34:41 Commencing smooth kNN distance calibration using 1 thread
05:34:53 Initializing from normalized Laplacian + noise
05:34:53 Commencing optimization for 500 epochs, with 121806 positive edges
05:35:03 Optimization finished
[1] "86 0.2"05:35:03 UMAP embedding parameters a = 1.262 b = 1.003
05:35:03 Read 1203 rows and found 38 numeric columns
05:35:03 Using Annoy for neighbor search, n_neighbors = 86
05:35:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:35:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87357e1a67
05:35:04 Searching Annoy index using 1 thread, search_k = 8600
05:35:05 Annoy recall = 100%
05:35:11 Commencing smooth kNN distance calibration using 1 thread
05:35:23 Initializing from normalized Laplacian + noise
05:35:23 Commencing optimization for 500 epochs, with 121806 positive edges
05:35:33 Optimization finished
[1] "87 0"05:35:34 UMAP embedding parameters a = 1.933 b = 0.7905
05:35:34 Read 1203 rows and found 38 numeric columns
05:35:34 Using Annoy for neighbor search, n_neighbors = 87
05:35:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:35:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876992c460
05:35:34 Searching Annoy index using 1 thread, search_k = 8700
05:35:35 Annoy recall = 100%
05:35:41 Commencing smooth kNN distance calibration using 1 thread
05:35:53 Initializing from normalized Laplacian + noise
05:35:53 Commencing optimization for 500 epochs, with 123122 positive edges
05:36:03 Optimization finished
[1] "87 0.01"05:36:03 UMAP embedding parameters a = 1.896 b = 0.8006
05:36:03 Read 1203 rows and found 38 numeric columns
05:36:03 Using Annoy for neighbor search, n_neighbors = 87
05:36:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:36:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f4dc75f
05:36:04 Searching Annoy index using 1 thread, search_k = 8700
05:36:04 Annoy recall = 100%
05:36:10 Commencing smooth kNN distance calibration using 1 thread
05:36:23 Initializing from normalized Laplacian + noise
05:36:23 Commencing optimization for 500 epochs, with 123122 positive edges
05:36:32 Optimization finished
[1] "87 0.02"05:36:33 UMAP embedding parameters a = 1.859 b = 0.8109
05:36:33 Read 1203 rows and found 38 numeric columns
05:36:33 Using Annoy for neighbor search, n_neighbors = 87
05:36:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:36:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bb6909d
05:36:33 Searching Annoy index using 1 thread, search_k = 8700
05:36:34 Annoy recall = 100%
05:36:40 Commencing smooth kNN distance calibration using 1 thread
05:36:52 Initializing from normalized Laplacian + noise
05:36:52 Commencing optimization for 500 epochs, with 123122 positive edges
05:37:02 Optimization finished
[1] "87 0.03"05:37:02 UMAP embedding parameters a = 1.822 b = 0.8212
05:37:02 Read 1203 rows and found 38 numeric columns
05:37:02 Using Annoy for neighbor search, n_neighbors = 87
05:37:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:37:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735df6862
05:37:03 Searching Annoy index using 1 thread, search_k = 8700
05:37:04 Annoy recall = 100%
05:37:10 Commencing smooth kNN distance calibration using 1 thread
05:37:22 Initializing from normalized Laplacian + noise
05:37:22 Commencing optimization for 500 epochs, with 123122 positive edges
05:37:32 Optimization finished
[1] "87 0.04"05:37:32 UMAP embedding parameters a = 1.786 b = 0.8316
05:37:32 Read 1203 rows and found 38 numeric columns
05:37:32 Using Annoy for neighbor search, n_neighbors = 87
05:37:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:37:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d1bec1e
05:37:32 Searching Annoy index using 1 thread, search_k = 8700
05:37:33 Annoy recall = 100%
05:37:39 Commencing smooth kNN distance calibration using 1 thread
05:37:52 Initializing from normalized Laplacian + noise
05:37:52 Commencing optimization for 500 epochs, with 123122 positive edges
05:38:01 Optimization finished
[1] "87 0.05"05:38:02 UMAP embedding parameters a = 1.75 b = 0.8421
05:38:02 Read 1203 rows and found 38 numeric columns
05:38:02 Using Annoy for neighbor search, n_neighbors = 87
05:38:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:38:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f6e0b88
05:38:02 Searching Annoy index using 1 thread, search_k = 8700
05:38:03 Annoy recall = 100%
05:38:09 Commencing smooth kNN distance calibration using 1 thread
05:38:21 Initializing from normalized Laplacian + noise
05:38:21 Commencing optimization for 500 epochs, with 123122 positive edges
05:38:31 Optimization finished
[1] "87 0.06"05:38:31 UMAP embedding parameters a = 1.715 b = 0.8526
05:38:31 Read 1203 rows and found 38 numeric columns
05:38:31 Using Annoy for neighbor search, n_neighbors = 87
05:38:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:38:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e75e7f8
05:38:32 Searching Annoy index using 1 thread, search_k = 8700
05:38:32 Annoy recall = 100%
05:38:38 Commencing smooth kNN distance calibration using 1 thread
05:38:51 Initializing from normalized Laplacian + noise
05:38:51 Commencing optimization for 500 epochs, with 123122 positive edges
05:39:01 Optimization finished
[1] "87 0.07"05:39:01 UMAP embedding parameters a = 1.68 b = 0.8631
05:39:01 Read 1203 rows and found 38 numeric columns
05:39:01 Using Annoy for neighbor search, n_neighbors = 87
05:39:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:39:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727cf2f1b
05:39:01 Searching Annoy index using 1 thread, search_k = 8700
05:39:02 Annoy recall = 100%
05:39:08 Commencing smooth kNN distance calibration using 1 thread
05:39:20 Initializing from normalized Laplacian + noise
05:39:21 Commencing optimization for 500 epochs, with 123122 positive edges
05:39:30 Optimization finished
[1] "87 0.08"05:39:30 UMAP embedding parameters a = 1.645 b = 0.8737
05:39:30 Read 1203 rows and found 38 numeric columns
05:39:30 Using Annoy for neighbor search, n_neighbors = 87
05:39:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:39:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87197f4bb
05:39:31 Searching Annoy index using 1 thread, search_k = 8700
05:39:32 Annoy recall = 100%
05:39:38 Commencing smooth kNN distance calibration using 1 thread
05:39:50 Initializing from normalized Laplacian + noise
05:39:50 Commencing optimization for 500 epochs, with 123122 positive edges
05:40:00 Optimization finished
[1] "87 0.09"05:40:00 UMAP embedding parameters a = 1.611 b = 0.8844
05:40:00 Read 1203 rows and found 38 numeric columns
05:40:00 Using Annoy for neighbor search, n_neighbors = 87
05:40:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:40:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873745eb20
05:40:01 Searching Annoy index using 1 thread, search_k = 8700
05:40:01 Annoy recall = 100%
05:40:07 Commencing smooth kNN distance calibration using 1 thread
05:40:20 Initializing from normalized Laplacian + noise
05:40:20 Commencing optimization for 500 epochs, with 123122 positive edges
05:40:30 Optimization finished
[1] "87 0.1"05:40:30 UMAP embedding parameters a = 1.577 b = 0.8951
05:40:30 Read 1203 rows and found 38 numeric columns
05:40:30 Using Annoy for neighbor search, n_neighbors = 87
05:40:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:40:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a09ebc5
05:40:30 Searching Annoy index using 1 thread, search_k = 8700
05:40:31 Annoy recall = 100%
05:40:37 Commencing smooth kNN distance calibration using 1 thread
05:40:49 Initializing from normalized Laplacian + noise
05:40:49 Commencing optimization for 500 epochs, with 123122 positive edges
05:40:59 Optimization finished
[1] "87 0.11"05:41:00 UMAP embedding parameters a = 1.544 b = 0.9058
05:41:00 Read 1203 rows and found 38 numeric columns
05:41:00 Using Annoy for neighbor search, n_neighbors = 87
05:41:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:41:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738f11154
05:41:00 Searching Annoy index using 1 thread, search_k = 8700
05:41:01 Annoy recall = 100%
05:41:07 Commencing smooth kNN distance calibration using 1 thread
05:41:19 Initializing from normalized Laplacian + noise
05:41:19 Commencing optimization for 500 epochs, with 123122 positive edges
05:41:29 Optimization finished
[1] "87 0.12"05:41:29 UMAP embedding parameters a = 1.51 b = 0.9165
05:41:29 Read 1203 rows and found 38 numeric columns
05:41:29 Using Annoy for neighbor search, n_neighbors = 87
05:41:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:41:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ec56ee6
05:41:30 Searching Annoy index using 1 thread, search_k = 8700
05:41:30 Annoy recall = 100%
05:41:36 Commencing smooth kNN distance calibration using 1 thread
05:41:49 Initializing from normalized Laplacian + noise
05:41:49 Commencing optimization for 500 epochs, with 123122 positive edges
05:41:59 Optimization finished
[1] "87 0.13"05:41:59 UMAP embedding parameters a = 1.478 b = 0.9272
05:41:59 Read 1203 rows and found 38 numeric columns
05:41:59 Using Annoy for neighbor search, n_neighbors = 87
05:41:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:41:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752ec3e60
05:41:59 Searching Annoy index using 1 thread, search_k = 8700
05:42:00 Annoy recall = 100%
05:42:06 Commencing smooth kNN distance calibration using 1 thread
05:42:19 Initializing from normalized Laplacian + noise
05:42:19 Commencing optimization for 500 epochs, with 123122 positive edges
05:42:29 Optimization finished
[1] "87 0.14"05:42:29 UMAP embedding parameters a = 1.446 b = 0.938
05:42:29 Read 1203 rows and found 38 numeric columns
05:42:29 Using Annoy for neighbor search, n_neighbors = 87
05:42:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:42:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744d4896e
05:42:29 Searching Annoy index using 1 thread, search_k = 8700
05:42:30 Annoy recall = 100%
05:42:37 Commencing smooth kNN distance calibration using 1 thread
05:42:52 Initializing from normalized Laplacian + noise
05:42:52 Commencing optimization for 500 epochs, with 123122 positive edges
05:43:04 Optimization finished
[1] "87 0.15"05:43:04 UMAP embedding parameters a = 1.414 b = 0.9488
05:43:04 Read 1203 rows and found 38 numeric columns
05:43:04 Using Annoy for neighbor search, n_neighbors = 87
05:43:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:43:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f273827
05:43:05 Searching Annoy index using 1 thread, search_k = 8700
05:43:05 Annoy recall = 100%
05:43:14 Commencing smooth kNN distance calibration using 1 thread
05:43:29 Initializing from normalized Laplacian + noise
05:43:29 Commencing optimization for 500 epochs, with 123122 positive edges
05:43:41 Optimization finished
[1] "87 0.16"05:43:41 UMAP embedding parameters a = 1.383 b = 0.9596
05:43:41 Read 1203 rows and found 38 numeric columns
05:43:41 Using Annoy for neighbor search, n_neighbors = 87
05:43:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:43:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ec82c20
05:43:42 Searching Annoy index using 1 thread, search_k = 8700
05:43:43 Annoy recall = 100%
05:43:51 Commencing smooth kNN distance calibration using 1 thread
05:44:05 Initializing from normalized Laplacian + noise
05:44:05 Commencing optimization for 500 epochs, with 123122 positive edges
05:44:16 Optimization finished
[1] "87 0.17"05:44:16 UMAP embedding parameters a = 1.352 b = 0.9704
05:44:16 Read 1203 rows and found 38 numeric columns
05:44:16 Using Annoy for neighbor search, n_neighbors = 87
05:44:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:44:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87424f7b0e
05:44:17 Searching Annoy index using 1 thread, search_k = 8700
05:44:18 Annoy recall = 100%
05:44:24 Commencing smooth kNN distance calibration using 1 thread
05:44:37 Initializing from normalized Laplacian + noise
05:44:37 Commencing optimization for 500 epochs, with 123122 positive edges
05:44:49 Optimization finished
[1] "87 0.18"05:44:49 UMAP embedding parameters a = 1.321 b = 0.9813
05:44:49 Read 1203 rows and found 38 numeric columns
05:44:49 Using Annoy for neighbor search, n_neighbors = 87
05:44:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:44:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735602ce6
05:44:50 Searching Annoy index using 1 thread, search_k = 8700
05:44:50 Annoy recall = 100%
05:44:57 Commencing smooth kNN distance calibration using 1 thread
05:45:10 Initializing from normalized Laplacian + noise
05:45:10 Commencing optimization for 500 epochs, with 123122 positive edges
05:45:20 Optimization finished
[1] "87 0.19"05:45:20 UMAP embedding parameters a = 1.292 b = 0.9921
05:45:20 Read 1203 rows and found 38 numeric columns
05:45:20 Using Annoy for neighbor search, n_neighbors = 87
05:45:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:45:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871fe5ff91
05:45:21 Searching Annoy index using 1 thread, search_k = 8700
05:45:22 Annoy recall = 100%
05:45:28 Commencing smooth kNN distance calibration using 1 thread
05:45:40 Initializing from normalized Laplacian + noise
05:45:40 Commencing optimization for 500 epochs, with 123122 positive edges
05:45:50 Optimization finished
[1] "87 0.2"05:45:50 UMAP embedding parameters a = 1.262 b = 1.003
05:45:50 Read 1203 rows and found 38 numeric columns
05:45:50 Using Annoy for neighbor search, n_neighbors = 87
05:45:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:45:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b9300e7
05:45:51 Searching Annoy index using 1 thread, search_k = 8700
05:45:51 Annoy recall = 100%
05:45:57 Commencing smooth kNN distance calibration using 1 thread
05:46:10 Initializing from normalized Laplacian + noise
05:46:10 Commencing optimization for 500 epochs, with 123122 positive edges
05:46:19 Optimization finished
[1] "88 0"05:46:19 UMAP embedding parameters a = 1.933 b = 0.7905
05:46:19 Read 1203 rows and found 38 numeric columns
05:46:19 Using Annoy for neighbor search, n_neighbors = 88
05:46:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:46:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878ef94cf
05:46:20 Searching Annoy index using 1 thread, search_k = 8800
05:46:21 Annoy recall = 100%
05:46:27 Commencing smooth kNN distance calibration using 1 thread
05:46:39 Initializing from normalized Laplacian + noise
05:46:39 Commencing optimization for 500 epochs, with 124442 positive edges
05:46:49 Optimization finished
[1] "88 0.01"05:46:49 UMAP embedding parameters a = 1.896 b = 0.8006
05:46:49 Read 1203 rows and found 38 numeric columns
05:46:49 Using Annoy for neighbor search, n_neighbors = 88
05:46:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:46:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876df38a83
05:46:49 Searching Annoy index using 1 thread, search_k = 8800
05:46:50 Annoy recall = 100%
05:46:56 Commencing smooth kNN distance calibration using 1 thread
05:47:10 Initializing from normalized Laplacian + noise
05:47:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:47:20 Optimization finished
[1] "88 0.02"05:47:20 UMAP embedding parameters a = 1.859 b = 0.8109
05:47:20 Read 1203 rows and found 38 numeric columns
05:47:20 Using Annoy for neighbor search, n_neighbors = 88
05:47:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:47:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743c3248
05:47:21 Searching Annoy index using 1 thread, search_k = 8800
05:47:21 Annoy recall = 100%
05:47:27 Commencing smooth kNN distance calibration using 1 thread
05:47:40 Initializing from normalized Laplacian + noise
05:47:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:47:50 Optimization finished
[1] "88 0.03"05:47:50 UMAP embedding parameters a = 1.822 b = 0.8212
05:47:50 Read 1203 rows and found 38 numeric columns
05:47:50 Using Annoy for neighbor search, n_neighbors = 88
05:47:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:47:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873aa51ac8
05:47:51 Searching Annoy index using 1 thread, search_k = 8800
05:47:51 Annoy recall = 100%
05:47:57 Commencing smooth kNN distance calibration using 1 thread
05:48:10 Initializing from normalized Laplacian + noise
05:48:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:48:20 Optimization finished
[1] "88 0.04"05:48:20 UMAP embedding parameters a = 1.786 b = 0.8316
05:48:20 Read 1203 rows and found 38 numeric columns
05:48:20 Using Annoy for neighbor search, n_neighbors = 88
05:48:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:48:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e3d177e
05:48:20 Searching Annoy index using 1 thread, search_k = 8800
05:48:21 Annoy recall = 100%
05:48:27 Commencing smooth kNN distance calibration using 1 thread
05:48:40 Initializing from normalized Laplacian + noise
05:48:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:48:50 Optimization finished
[1] "88 0.05"05:48:50 UMAP embedding parameters a = 1.75 b = 0.8421
05:48:50 Read 1203 rows and found 38 numeric columns
05:48:50 Using Annoy for neighbor search, n_neighbors = 88
05:48:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:48:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f5270e4
05:48:50 Searching Annoy index using 1 thread, search_k = 8800
05:48:51 Annoy recall = 100%
05:48:57 Commencing smooth kNN distance calibration using 1 thread
05:49:10 Initializing from normalized Laplacian + noise
05:49:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:49:20 Optimization finished
[1] "88 0.06"05:49:20 UMAP embedding parameters a = 1.715 b = 0.8526
05:49:20 Read 1203 rows and found 38 numeric columns
05:49:20 Using Annoy for neighbor search, n_neighbors = 88
05:49:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:49:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e5b84c4
05:49:20 Searching Annoy index using 1 thread, search_k = 8800
05:49:21 Annoy recall = 100%
05:49:27 Commencing smooth kNN distance calibration using 1 thread
05:49:40 Initializing from normalized Laplacian + noise
05:49:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:49:50 Optimization finished
[1] "88 0.07"05:49:50 UMAP embedding parameters a = 1.68 b = 0.8631
05:49:50 Read 1203 rows and found 38 numeric columns
05:49:50 Using Annoy for neighbor search, n_neighbors = 88
05:49:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:49:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87325b98b
05:49:50 Searching Annoy index using 1 thread, search_k = 8800
05:49:51 Annoy recall = 100%
05:49:57 Commencing smooth kNN distance calibration using 1 thread
05:50:10 Initializing from normalized Laplacian + noise
05:50:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:50:19 Optimization finished
[1] "88 0.08"05:50:20 UMAP embedding parameters a = 1.645 b = 0.8737
05:50:20 Read 1203 rows and found 38 numeric columns
05:50:20 Using Annoy for neighbor search, n_neighbors = 88
05:50:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:50:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fb777ae
05:50:20 Searching Annoy index using 1 thread, search_k = 8800
05:50:21 Annoy recall = 100%
05:50:27 Commencing smooth kNN distance calibration using 1 thread
05:50:40 Initializing from normalized Laplacian + noise
05:50:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:50:50 Optimization finished
[1] "88 0.09"05:50:50 UMAP embedding parameters a = 1.611 b = 0.8844
05:50:50 Read 1203 rows and found 38 numeric columns
05:50:50 Using Annoy for neighbor search, n_neighbors = 88
05:50:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:50:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753d99f2b
05:50:50 Searching Annoy index using 1 thread, search_k = 8800
05:50:51 Annoy recall = 100%
05:50:57 Commencing smooth kNN distance calibration using 1 thread
05:51:10 Initializing from normalized Laplacian + noise
05:51:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:51:19 Optimization finished
[1] "88 0.1"05:51:20 UMAP embedding parameters a = 1.577 b = 0.8951
05:51:20 Read 1203 rows and found 38 numeric columns
05:51:20 Using Annoy for neighbor search, n_neighbors = 88
05:51:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:51:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cb87deb
05:51:20 Searching Annoy index using 1 thread, search_k = 8800
05:51:21 Annoy recall = 100%
05:51:27 Commencing smooth kNN distance calibration using 1 thread
05:51:40 Initializing from normalized Laplacian + noise
05:51:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:51:50 Optimization finished
[1] "88 0.11"05:51:50 UMAP embedding parameters a = 1.544 b = 0.9058
05:51:50 Read 1203 rows and found 38 numeric columns
05:51:50 Using Annoy for neighbor search, n_neighbors = 88
05:51:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:51:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f053f0e
05:51:50 Searching Annoy index using 1 thread, search_k = 8800
05:51:51 Annoy recall = 100%
05:51:57 Commencing smooth kNN distance calibration using 1 thread
05:52:10 Initializing from normalized Laplacian + noise
05:52:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:52:19 Optimization finished
[1] "88 0.12"05:52:20 UMAP embedding parameters a = 1.51 b = 0.9165
05:52:20 Read 1203 rows and found 38 numeric columns
05:52:20 Using Annoy for neighbor search, n_neighbors = 88
05:52:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:52:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f902fc8
05:52:20 Searching Annoy index using 1 thread, search_k = 8800
05:52:21 Annoy recall = 100%
05:52:27 Commencing smooth kNN distance calibration using 1 thread
05:52:40 Initializing from normalized Laplacian + noise
05:52:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:52:50 Optimization finished
[1] "88 0.13"05:52:50 UMAP embedding parameters a = 1.478 b = 0.9272
05:52:50 Read 1203 rows and found 38 numeric columns
05:52:50 Using Annoy for neighbor search, n_neighbors = 88
05:52:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:52:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872297e64d
05:52:50 Searching Annoy index using 1 thread, search_k = 8800
05:52:51 Annoy recall = 100%
05:52:57 Commencing smooth kNN distance calibration using 1 thread
05:53:10 Initializing from normalized Laplacian + noise
05:53:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:53:20 Optimization finished
[1] "88 0.14"05:53:20 UMAP embedding parameters a = 1.446 b = 0.938
05:53:20 Read 1203 rows and found 38 numeric columns
05:53:20 Using Annoy for neighbor search, n_neighbors = 88
05:53:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:53:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c212b2c
05:53:20 Searching Annoy index using 1 thread, search_k = 8800
05:53:21 Annoy recall = 100%
05:53:27 Commencing smooth kNN distance calibration using 1 thread
05:53:40 Initializing from normalized Laplacian + noise
05:53:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:53:50 Optimization finished
[1] "88 0.15"05:53:50 UMAP embedding parameters a = 1.414 b = 0.9488
05:53:50 Read 1203 rows and found 38 numeric columns
05:53:50 Using Annoy for neighbor search, n_neighbors = 88
05:53:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:53:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873efe3b51
05:53:50 Searching Annoy index using 1 thread, search_k = 8800
05:53:51 Annoy recall = 100%
05:53:57 Commencing smooth kNN distance calibration using 1 thread
05:54:10 Initializing from normalized Laplacian + noise
05:54:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:54:20 Optimization finished
[1] "88 0.16"05:54:20 UMAP embedding parameters a = 1.383 b = 0.9596
05:54:20 Read 1203 rows and found 38 numeric columns
05:54:20 Using Annoy for neighbor search, n_neighbors = 88
05:54:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:54:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87410dce45
05:54:20 Searching Annoy index using 1 thread, search_k = 8800
05:54:21 Annoy recall = 100%
05:54:27 Commencing smooth kNN distance calibration using 1 thread
05:54:40 Initializing from normalized Laplacian + noise
05:54:40 Commencing optimization for 500 epochs, with 124442 positive edges
05:54:50 Optimization finished
[1] "88 0.17"05:54:50 UMAP embedding parameters a = 1.352 b = 0.9704
05:54:50 Read 1203 rows and found 38 numeric columns
05:54:50 Using Annoy for neighbor search, n_neighbors = 88
05:54:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:54:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773f05a47
05:54:51 Searching Annoy index using 1 thread, search_k = 8800
05:54:51 Annoy recall = 100%
05:54:57 Commencing smooth kNN distance calibration using 1 thread
05:55:10 Initializing from normalized Laplacian + noise
05:55:10 Commencing optimization for 500 epochs, with 124442 positive edges
05:55:20 Optimization finished
[1] "88 0.18"05:55:20 UMAP embedding parameters a = 1.321 b = 0.9813
05:55:20 Read 1203 rows and found 38 numeric columns
05:55:20 Using Annoy for neighbor search, n_neighbors = 88
05:55:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:55:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874096300c
05:55:21 Searching Annoy index using 1 thread, search_k = 8800
05:55:21 Annoy recall = 100%
05:55:28 Commencing smooth kNN distance calibration using 1 thread
05:55:42 Initializing from normalized Laplacian + noise
05:55:42 Commencing optimization for 500 epochs, with 124442 positive edges
05:55:52 Optimization finished
[1] "88 0.19"05:55:53 UMAP embedding parameters a = 1.292 b = 0.9921
05:55:53 Read 1203 rows and found 38 numeric columns
05:55:53 Using Annoy for neighbor search, n_neighbors = 88
05:55:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:55:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877853b965
05:55:53 Searching Annoy index using 1 thread, search_k = 8800
05:55:54 Annoy recall = 100%
05:56:02 Commencing smooth kNN distance calibration using 1 thread
05:56:15 Initializing from normalized Laplacian + noise
05:56:15 Commencing optimization for 500 epochs, with 124442 positive edges
05:56:26 Optimization finished
[1] "88 0.2"05:56:26 UMAP embedding parameters a = 1.262 b = 1.003
05:56:26 Read 1203 rows and found 38 numeric columns
05:56:26 Using Annoy for neighbor search, n_neighbors = 88
05:56:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:56:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dfa460c
05:56:26 Searching Annoy index using 1 thread, search_k = 8800
05:56:27 Annoy recall = 100%
05:56:34 Commencing smooth kNN distance calibration using 1 thread
05:56:46 Initializing from normalized Laplacian + noise
05:56:46 Commencing optimization for 500 epochs, with 124442 positive edges
05:56:56 Optimization finished
[1] "89 0"05:56:56 UMAP embedding parameters a = 1.933 b = 0.7905
05:56:56 Read 1203 rows and found 38 numeric columns
05:56:56 Using Annoy for neighbor search, n_neighbors = 89
05:56:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:56:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779874160
05:56:57 Searching Annoy index using 1 thread, search_k = 8900
05:56:57 Annoy recall = 100%
05:57:04 Commencing smooth kNN distance calibration using 1 thread
05:57:16 Initializing from normalized Laplacian + noise
05:57:16 Commencing optimization for 500 epochs, with 125696 positive edges
05:57:26 Optimization finished
[1] "89 0.01"05:57:27 UMAP embedding parameters a = 1.896 b = 0.8006
05:57:27 Read 1203 rows and found 38 numeric columns
05:57:27 Using Annoy for neighbor search, n_neighbors = 89
05:57:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:57:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873719284b
05:57:27 Searching Annoy index using 1 thread, search_k = 8900
05:57:28 Annoy recall = 100%
05:57:36 Commencing smooth kNN distance calibration using 1 thread
05:57:50 Initializing from normalized Laplacian + noise
05:57:50 Commencing optimization for 500 epochs, with 125696 positive edges
05:58:02 Optimization finished
[1] "89 0.02"05:58:02 UMAP embedding parameters a = 1.859 b = 0.8109
05:58:02 Read 1203 rows and found 38 numeric columns
05:58:02 Using Annoy for neighbor search, n_neighbors = 89
05:58:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:58:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e6846d
05:58:03 Searching Annoy index using 1 thread, search_k = 8900
05:58:03 Annoy recall = 100%
05:58:11 Commencing smooth kNN distance calibration using 1 thread
05:58:25 Initializing from normalized Laplacian + noise
05:58:25 Commencing optimization for 500 epochs, with 125696 positive edges
05:58:35 Optimization finished
[1] "89 0.03"05:58:35 UMAP embedding parameters a = 1.822 b = 0.8212
05:58:36 Read 1203 rows and found 38 numeric columns
05:58:36 Using Annoy for neighbor search, n_neighbors = 89
05:58:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:58:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e5bcacf
05:58:36 Searching Annoy index using 1 thread, search_k = 8900
05:58:37 Annoy recall = 100%
05:58:43 Commencing smooth kNN distance calibration using 1 thread
05:58:56 Initializing from normalized Laplacian + noise
05:58:56 Commencing optimization for 500 epochs, with 125696 positive edges
05:59:06 Optimization finished
[1] "89 0.04"05:59:07 UMAP embedding parameters a = 1.786 b = 0.8316
05:59:07 Read 1203 rows and found 38 numeric columns
05:59:07 Using Annoy for neighbor search, n_neighbors = 89
05:59:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:59:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876406073
05:59:07 Searching Annoy index using 1 thread, search_k = 8900
05:59:08 Annoy recall = 100%
05:59:14 Commencing smooth kNN distance calibration using 1 thread
05:59:27 Initializing from normalized Laplacian + noise
05:59:27 Commencing optimization for 500 epochs, with 125696 positive edges
05:59:37 Optimization finished
[1] "89 0.05"05:59:37 UMAP embedding parameters a = 1.75 b = 0.8421
05:59:37 Read 1203 rows and found 38 numeric columns
05:59:37 Using Annoy for neighbor search, n_neighbors = 89
05:59:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:59:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875faeb08d
05:59:38 Searching Annoy index using 1 thread, search_k = 8900
05:59:38 Annoy recall = 100%
05:59:45 Commencing smooth kNN distance calibration using 1 thread
05:59:58 Initializing from normalized Laplacian + noise
05:59:58 Commencing optimization for 500 epochs, with 125696 positive edges
06:00:10 Optimization finished
[1] "89 0.06"06:00:10 UMAP embedding parameters a = 1.715 b = 0.8526
06:00:10 Read 1203 rows and found 38 numeric columns
06:00:10 Using Annoy for neighbor search, n_neighbors = 89
06:00:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:00:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ab45dd
06:00:11 Searching Annoy index using 1 thread, search_k = 8900
06:00:12 Annoy recall = 100%
06:00:19 Commencing smooth kNN distance calibration using 1 thread
06:00:33 Initializing from normalized Laplacian + noise
06:00:33 Commencing optimization for 500 epochs, with 125696 positive edges
06:00:44 Optimization finished
[1] "89 0.07"06:00:44 UMAP embedding parameters a = 1.68 b = 0.8631
06:00:44 Read 1203 rows and found 38 numeric columns
06:00:44 Using Annoy for neighbor search, n_neighbors = 89
06:00:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:00:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ba08d59
06:00:45 Searching Annoy index using 1 thread, search_k = 8900
06:00:46 Annoy recall = 100%
06:00:53 Commencing smooth kNN distance calibration using 1 thread
06:01:06 Initializing from normalized Laplacian + noise
06:01:06 Commencing optimization for 500 epochs, with 125696 positive edges
06:01:17 Optimization finished
[1] "89 0.08"06:01:17 UMAP embedding parameters a = 1.645 b = 0.8737
06:01:17 Read 1203 rows and found 38 numeric columns
06:01:17 Using Annoy for neighbor search, n_neighbors = 89
06:01:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:01:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f94b01f
06:01:18 Searching Annoy index using 1 thread, search_k = 8900
06:01:19 Annoy recall = 100%
06:01:25 Commencing smooth kNN distance calibration using 1 thread
06:01:38 Initializing from normalized Laplacian + noise
06:01:38 Commencing optimization for 500 epochs, with 125696 positive edges
06:01:50 Optimization finished
[1] "89 0.09"06:01:50 UMAP embedding parameters a = 1.611 b = 0.8844
06:01:50 Read 1203 rows and found 38 numeric columns
06:01:50 Using Annoy for neighbor search, n_neighbors = 89
06:01:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:01:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c3e46c4
06:01:51 Searching Annoy index using 1 thread, search_k = 8900
06:01:52 Annoy recall = 100%
06:01:59 Commencing smooth kNN distance calibration using 1 thread
06:02:13 Initializing from normalized Laplacian + noise
06:02:13 Commencing optimization for 500 epochs, with 125696 positive edges
06:02:24 Optimization finished
[1] "89 0.1"06:02:24 UMAP embedding parameters a = 1.577 b = 0.8951
06:02:24 Read 1203 rows and found 38 numeric columns
06:02:24 Using Annoy for neighbor search, n_neighbors = 89
06:02:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:02:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744902228
06:02:25 Searching Annoy index using 1 thread, search_k = 8900
06:02:26 Annoy recall = 100%
06:02:32 Commencing smooth kNN distance calibration using 1 thread
06:02:46 Initializing from normalized Laplacian + noise
06:02:46 Commencing optimization for 500 epochs, with 125696 positive edges
06:02:56 Optimization finished
[1] "89 0.11"06:02:56 UMAP embedding parameters a = 1.544 b = 0.9058
06:02:56 Read 1203 rows and found 38 numeric columns
06:02:56 Using Annoy for neighbor search, n_neighbors = 89
06:02:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:02:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d883aa2
06:02:57 Searching Annoy index using 1 thread, search_k = 8900
06:02:57 Annoy recall = 100%
06:03:04 Commencing smooth kNN distance calibration using 1 thread
06:03:16 Initializing from normalized Laplacian + noise
06:03:16 Commencing optimization for 500 epochs, with 125696 positive edges
06:03:27 Optimization finished
[1] "89 0.12"06:03:27 UMAP embedding parameters a = 1.51 b = 0.9165
06:03:27 Read 1203 rows and found 38 numeric columns
06:03:27 Using Annoy for neighbor search, n_neighbors = 89
06:03:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:03:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87507a790c
06:03:27 Searching Annoy index using 1 thread, search_k = 8900
06:03:28 Annoy recall = 100%
06:03:34 Commencing smooth kNN distance calibration using 1 thread
06:03:47 Initializing from normalized Laplacian + noise
06:03:47 Commencing optimization for 500 epochs, with 125696 positive edges
06:03:57 Optimization finished
[1] "89 0.13"06:03:57 UMAP embedding parameters a = 1.478 b = 0.9272
06:03:57 Read 1203 rows and found 38 numeric columns
06:03:57 Using Annoy for neighbor search, n_neighbors = 89
06:03:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:03:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f353cf0
06:03:57 Searching Annoy index using 1 thread, search_k = 8900
06:03:58 Annoy recall = 100%
06:04:04 Commencing smooth kNN distance calibration using 1 thread
06:04:17 Initializing from normalized Laplacian + noise
06:04:17 Commencing optimization for 500 epochs, with 125696 positive edges
06:04:27 Optimization finished
[1] "89 0.14"06:04:27 UMAP embedding parameters a = 1.446 b = 0.938
06:04:27 Read 1203 rows and found 38 numeric columns
06:04:27 Using Annoy for neighbor search, n_neighbors = 89
06:04:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:04:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bc55220
06:04:27 Searching Annoy index using 1 thread, search_k = 8900
06:04:28 Annoy recall = 100%
06:04:34 Commencing smooth kNN distance calibration using 1 thread
06:04:46 Initializing from normalized Laplacian + noise
06:04:46 Commencing optimization for 500 epochs, with 125696 positive edges
06:04:56 Optimization finished
[1] "89 0.15"06:04:57 UMAP embedding parameters a = 1.414 b = 0.9488
06:04:57 Read 1203 rows and found 38 numeric columns
06:04:57 Using Annoy for neighbor search, n_neighbors = 89
06:04:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:04:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fcce9f0
06:04:57 Searching Annoy index using 1 thread, search_k = 8900
06:04:58 Annoy recall = 100%
06:05:04 Commencing smooth kNN distance calibration using 1 thread
06:05:16 Initializing from normalized Laplacian + noise
06:05:16 Commencing optimization for 500 epochs, with 125696 positive edges
06:05:26 Optimization finished
[1] "89 0.16"06:05:26 UMAP embedding parameters a = 1.383 b = 0.9596
06:05:26 Read 1203 rows and found 38 numeric columns
06:05:26 Using Annoy for neighbor search, n_neighbors = 89
06:05:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:05:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d90c1b4
06:05:27 Searching Annoy index using 1 thread, search_k = 8900
06:05:28 Annoy recall = 100%
06:05:34 Commencing smooth kNN distance calibration using 1 thread
06:05:46 Initializing from normalized Laplacian + noise
06:05:46 Commencing optimization for 500 epochs, with 125696 positive edges
06:05:56 Optimization finished
[1] "89 0.17"06:05:56 UMAP embedding parameters a = 1.352 b = 0.9704
06:05:56 Read 1203 rows and found 38 numeric columns
06:05:56 Using Annoy for neighbor search, n_neighbors = 89
06:05:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:05:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875eeb0bab
06:05:57 Searching Annoy index using 1 thread, search_k = 8900
06:05:57 Annoy recall = 100%
06:06:04 Commencing smooth kNN distance calibration using 1 thread
06:06:16 Initializing from normalized Laplacian + noise
06:06:16 Commencing optimization for 500 epochs, with 125696 positive edges
06:06:26 Optimization finished
[1] "89 0.18"06:06:26 UMAP embedding parameters a = 1.321 b = 0.9813
06:06:26 Read 1203 rows and found 38 numeric columns
06:06:26 Using Annoy for neighbor search, n_neighbors = 89
06:06:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:06:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f84619f
06:06:27 Searching Annoy index using 1 thread, search_k = 8900
06:06:27 Annoy recall = 100%
06:06:33 Commencing smooth kNN distance calibration using 1 thread
06:06:46 Initializing from normalized Laplacian + noise
06:06:46 Commencing optimization for 500 epochs, with 125696 positive edges
06:06:56 Optimization finished
[1] "89 0.19"06:06:56 UMAP embedding parameters a = 1.292 b = 0.9921
06:06:56 Read 1203 rows and found 38 numeric columns
06:06:56 Using Annoy for neighbor search, n_neighbors = 89
06:06:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:06:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87716a60df
06:06:56 Searching Annoy index using 1 thread, search_k = 8900
06:06:57 Annoy recall = 100%
06:07:03 Commencing smooth kNN distance calibration using 1 thread
06:07:16 Initializing from normalized Laplacian + noise
06:07:16 Commencing optimization for 500 epochs, with 125696 positive edges
06:07:26 Optimization finished
[1] "89 0.2"06:07:26 UMAP embedding parameters a = 1.262 b = 1.003
06:07:26 Read 1203 rows and found 38 numeric columns
06:07:26 Using Annoy for neighbor search, n_neighbors = 89
06:07:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:07:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ba38997
06:07:26 Searching Annoy index using 1 thread, search_k = 8900
06:07:27 Annoy recall = 100%
06:07:33 Commencing smooth kNN distance calibration using 1 thread
06:07:45 Initializing from normalized Laplacian + noise
06:07:45 Commencing optimization for 500 epochs, with 125696 positive edges
06:07:55 Optimization finished
[1] "90 0"06:07:56 UMAP embedding parameters a = 1.933 b = 0.7905
06:07:56 Read 1203 rows and found 38 numeric columns
06:07:56 Using Annoy for neighbor search, n_neighbors = 90
06:07:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:07:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e89a0ad
06:07:56 Searching Annoy index using 1 thread, search_k = 9000
06:07:57 Annoy recall = 100%
06:08:03 Commencing smooth kNN distance calibration using 1 thread
06:08:15 Initializing from normalized Laplacian + noise
06:08:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:08:25 Optimization finished
[1] "90 0.01"06:08:26 UMAP embedding parameters a = 1.896 b = 0.8006
06:08:26 Read 1203 rows and found 38 numeric columns
06:08:26 Using Annoy for neighbor search, n_neighbors = 90
06:08:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:08:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730fa90a8
06:08:26 Searching Annoy index using 1 thread, search_k = 9000
06:08:27 Annoy recall = 100%
06:08:33 Commencing smooth kNN distance calibration using 1 thread
06:08:45 Initializing from normalized Laplacian + noise
06:08:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:08:55 Optimization finished
[1] "90 0.02"06:08:55 UMAP embedding parameters a = 1.859 b = 0.8109
06:08:55 Read 1203 rows and found 38 numeric columns
06:08:55 Using Annoy for neighbor search, n_neighbors = 90
06:08:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:08:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e3b6fe4
06:08:56 Searching Annoy index using 1 thread, search_k = 9000
06:08:56 Annoy recall = 100%
06:09:03 Commencing smooth kNN distance calibration using 1 thread
06:09:15 Initializing from normalized Laplacian + noise
06:09:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:09:25 Optimization finished
[1] "90 0.03"06:09:25 UMAP embedding parameters a = 1.822 b = 0.8212
06:09:25 Read 1203 rows and found 38 numeric columns
06:09:25 Using Annoy for neighbor search, n_neighbors = 90
06:09:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:09:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873aaacbd9
06:09:26 Searching Annoy index using 1 thread, search_k = 9000
06:09:27 Annoy recall = 100%
06:09:33 Commencing smooth kNN distance calibration using 1 thread
06:09:45 Initializing from normalized Laplacian + noise
06:09:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:09:55 Optimization finished
[1] "90 0.04"06:09:55 UMAP embedding parameters a = 1.786 b = 0.8316
06:09:55 Read 1203 rows and found 38 numeric columns
06:09:55 Using Annoy for neighbor search, n_neighbors = 90
06:09:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:09:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ff8cbf9
06:09:56 Searching Annoy index using 1 thread, search_k = 9000
06:09:56 Annoy recall = 100%
06:10:02 Commencing smooth kNN distance calibration using 1 thread
06:10:15 Initializing from normalized Laplacian + noise
06:10:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:10:25 Optimization finished
[1] "90 0.05"06:10:25 UMAP embedding parameters a = 1.75 b = 0.8421
06:10:25 Read 1203 rows and found 38 numeric columns
06:10:25 Using Annoy for neighbor search, n_neighbors = 90
06:10:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:10:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f493e2a
06:10:26 Searching Annoy index using 1 thread, search_k = 9000
06:10:26 Annoy recall = 100%
06:10:33 Commencing smooth kNN distance calibration using 1 thread
06:10:45 Initializing from normalized Laplacian + noise
06:10:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:10:55 Optimization finished
[1] "90 0.06"06:10:55 UMAP embedding parameters a = 1.715 b = 0.8526
06:10:55 Read 1203 rows and found 38 numeric columns
06:10:55 Using Annoy for neighbor search, n_neighbors = 90
06:10:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:10:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e9b2620
06:10:56 Searching Annoy index using 1 thread, search_k = 9000
06:10:56 Annoy recall = 100%
06:11:02 Commencing smooth kNN distance calibration using 1 thread
06:11:15 Initializing from normalized Laplacian + noise
06:11:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:11:25 Optimization finished
[1] "90 0.07"06:11:25 UMAP embedding parameters a = 1.68 b = 0.8631
06:11:25 Read 1203 rows and found 38 numeric columns
06:11:25 Using Annoy for neighbor search, n_neighbors = 90
06:11:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:11:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87308efc05
06:11:26 Searching Annoy index using 1 thread, search_k = 9000
06:11:26 Annoy recall = 100%
06:11:33 Commencing smooth kNN distance calibration using 1 thread
06:11:45 Initializing from normalized Laplacian + noise
06:11:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:11:55 Optimization finished
[1] "90 0.08"06:11:55 UMAP embedding parameters a = 1.645 b = 0.8737
06:11:55 Read 1203 rows and found 38 numeric columns
06:11:55 Using Annoy for neighbor search, n_neighbors = 90
06:11:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:11:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87279cf78f
06:11:56 Searching Annoy index using 1 thread, search_k = 9000
06:11:56 Annoy recall = 100%
06:12:02 Commencing smooth kNN distance calibration using 1 thread
06:12:15 Initializing from normalized Laplacian + noise
06:12:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:12:25 Optimization finished
[1] "90 0.09"06:12:25 UMAP embedding parameters a = 1.611 b = 0.8844
06:12:25 Read 1203 rows and found 38 numeric columns
06:12:25 Using Annoy for neighbor search, n_neighbors = 90
06:12:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:12:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c956c2d
06:12:26 Searching Annoy index using 1 thread, search_k = 9000
06:12:26 Annoy recall = 100%
06:12:33 Commencing smooth kNN distance calibration using 1 thread
06:12:45 Initializing from normalized Laplacian + noise
06:12:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:12:55 Optimization finished
[1] "90 0.1"06:12:55 UMAP embedding parameters a = 1.577 b = 0.8951
06:12:55 Read 1203 rows and found 38 numeric columns
06:12:55 Using Annoy for neighbor search, n_neighbors = 90
06:12:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:12:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a163d66
06:12:56 Searching Annoy index using 1 thread, search_k = 9000
06:12:56 Annoy recall = 100%
06:13:02 Commencing smooth kNN distance calibration using 1 thread
06:13:15 Initializing from normalized Laplacian + noise
06:13:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:13:25 Optimization finished
[1] "90 0.11"06:13:25 UMAP embedding parameters a = 1.544 b = 0.9058
06:13:25 Read 1203 rows and found 38 numeric columns
06:13:25 Using Annoy for neighbor search, n_neighbors = 90
06:13:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:13:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875eb61fdb
06:13:26 Searching Annoy index using 1 thread, search_k = 9000
06:13:26 Annoy recall = 100%
06:13:33 Commencing smooth kNN distance calibration using 1 thread
06:13:45 Initializing from normalized Laplacian + noise
06:13:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:13:55 Optimization finished
[1] "90 0.12"06:13:55 UMAP embedding parameters a = 1.51 b = 0.9165
06:13:55 Read 1203 rows and found 38 numeric columns
06:13:55 Using Annoy for neighbor search, n_neighbors = 90
06:13:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:13:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d7bf09a
06:13:56 Searching Annoy index using 1 thread, search_k = 9000
06:13:56 Annoy recall = 100%
06:14:02 Commencing smooth kNN distance calibration using 1 thread
06:14:15 Initializing from normalized Laplacian + noise
06:14:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:14:25 Optimization finished
[1] "90 0.13"06:14:25 UMAP embedding parameters a = 1.478 b = 0.9272
06:14:25 Read 1203 rows and found 38 numeric columns
06:14:25 Using Annoy for neighbor search, n_neighbors = 90
06:14:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:14:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768720835
06:14:26 Searching Annoy index using 1 thread, search_k = 9000
06:14:26 Annoy recall = 100%
06:14:33 Commencing smooth kNN distance calibration using 1 thread
06:14:45 Initializing from normalized Laplacian + noise
06:14:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:14:55 Optimization finished
[1] "90 0.14"06:14:55 UMAP embedding parameters a = 1.446 b = 0.938
06:14:55 Read 1203 rows and found 38 numeric columns
06:14:55 Using Annoy for neighbor search, n_neighbors = 90
06:14:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:14:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764f6804e
06:14:56 Searching Annoy index using 1 thread, search_k = 9000
06:14:56 Annoy recall = 100%
06:15:03 Commencing smooth kNN distance calibration using 1 thread
06:15:15 Initializing from normalized Laplacian + noise
06:15:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:15:25 Optimization finished
[1] "90 0.15"06:15:25 UMAP embedding parameters a = 1.414 b = 0.9488
06:15:25 Read 1203 rows and found 38 numeric columns
06:15:25 Using Annoy for neighbor search, n_neighbors = 90
06:15:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:15:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d2aa127
06:15:26 Searching Annoy index using 1 thread, search_k = 9000
06:15:27 Annoy recall = 100%
06:15:33 Commencing smooth kNN distance calibration using 1 thread
06:15:45 Initializing from normalized Laplacian + noise
06:15:45 Commencing optimization for 500 epochs, with 127010 positive edges
06:15:55 Optimization finished
[1] "90 0.16"06:15:55 UMAP embedding parameters a = 1.383 b = 0.9596
06:15:55 Read 1203 rows and found 38 numeric columns
06:15:55 Using Annoy for neighbor search, n_neighbors = 90
06:15:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:15:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87691d4e12
06:15:56 Searching Annoy index using 1 thread, search_k = 9000
06:15:57 Annoy recall = 100%
06:16:03 Commencing smooth kNN distance calibration using 1 thread
06:16:15 Initializing from normalized Laplacian + noise
06:16:15 Commencing optimization for 500 epochs, with 127010 positive edges
06:16:25 Optimization finished
[1] "90 0.17"06:16:26 UMAP embedding parameters a = 1.352 b = 0.9704
06:16:26 Read 1203 rows and found 38 numeric columns
06:16:26 Using Annoy for neighbor search, n_neighbors = 90
06:16:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:16:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720970da7
06:16:26 Searching Annoy index using 1 thread, search_k = 9000
06:16:27 Annoy recall = 100%
06:16:33 Commencing smooth kNN distance calibration using 1 thread
06:16:46 Initializing from normalized Laplacian + noise
06:16:46 Commencing optimization for 500 epochs, with 127010 positive edges
06:16:56 Optimization finished
[1] "90 0.18"06:16:56 UMAP embedding parameters a = 1.321 b = 0.9813
06:16:56 Read 1203 rows and found 38 numeric columns
06:16:56 Using Annoy for neighbor search, n_neighbors = 90
06:16:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:16:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cbf5146
06:16:56 Searching Annoy index using 1 thread, search_k = 9000
06:16:57 Annoy recall = 100%
06:17:03 Commencing smooth kNN distance calibration using 1 thread
06:17:16 Initializing from normalized Laplacian + noise
06:17:16 Commencing optimization for 500 epochs, with 127010 positive edges
06:17:26 Optimization finished
[1] "90 0.19"06:17:26 UMAP embedding parameters a = 1.292 b = 0.9921
06:17:26 Read 1203 rows and found 38 numeric columns
06:17:26 Using Annoy for neighbor search, n_neighbors = 90
06:17:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:17:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87355b94d6
06:17:26 Searching Annoy index using 1 thread, search_k = 9000
06:17:27 Annoy recall = 100%
06:17:33 Commencing smooth kNN distance calibration using 1 thread
06:17:46 Initializing from normalized Laplacian + noise
06:17:46 Commencing optimization for 500 epochs, with 127010 positive edges
06:17:56 Optimization finished
[1] "90 0.2"06:17:56 UMAP embedding parameters a = 1.262 b = 1.003
06:17:56 Read 1203 rows and found 38 numeric columns
06:17:56 Using Annoy for neighbor search, n_neighbors = 90
06:17:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:17:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765272fcf
06:17:57 Searching Annoy index using 1 thread, search_k = 9000
06:17:57 Annoy recall = 100%
06:18:03 Commencing smooth kNN distance calibration using 1 thread
06:18:16 Initializing from normalized Laplacian + noise
06:18:16 Commencing optimization for 500 epochs, with 127010 positive edges
06:18:26 Optimization finished
[1] "91 0"06:18:26 UMAP embedding parameters a = 1.933 b = 0.7905
06:18:26 Read 1203 rows and found 38 numeric columns
06:18:26 Using Annoy for neighbor search, n_neighbors = 91
06:18:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:18:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a478be9
06:18:27 Searching Annoy index using 1 thread, search_k = 9100
06:18:27 Annoy recall = 100%
06:18:34 Commencing smooth kNN distance calibration using 1 thread
06:18:46 Initializing from normalized Laplacian + noise
06:18:46 Commencing optimization for 500 epochs, with 128290 positive edges
06:18:56 Optimization finished
[1] "91 0.01"06:18:56 UMAP embedding parameters a = 1.896 b = 0.8006
06:18:56 Read 1203 rows and found 38 numeric columns
06:18:56 Using Annoy for neighbor search, n_neighbors = 91
06:18:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:18:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d60de3
06:18:57 Searching Annoy index using 1 thread, search_k = 9100
06:18:58 Annoy recall = 100%
06:19:04 Commencing smooth kNN distance calibration using 1 thread
06:19:16 Initializing from normalized Laplacian + noise
06:19:16 Commencing optimization for 500 epochs, with 128290 positive edges
06:19:26 Optimization finished
[1] "91 0.02"06:19:26 UMAP embedding parameters a = 1.859 b = 0.8109
06:19:26 Read 1203 rows and found 38 numeric columns
06:19:27 Using Annoy for neighbor search, n_neighbors = 91
06:19:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:19:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87645c6cbf
06:19:27 Searching Annoy index using 1 thread, search_k = 9100
06:19:28 Annoy recall = 100%
06:19:34 Commencing smooth kNN distance calibration using 1 thread
06:19:47 Initializing from normalized Laplacian + noise
06:19:47 Commencing optimization for 500 epochs, with 128290 positive edges
06:19:57 Optimization finished
[1] "91 0.03"06:19:57 UMAP embedding parameters a = 1.822 b = 0.8212
06:19:57 Read 1203 rows and found 38 numeric columns
06:19:57 Using Annoy for neighbor search, n_neighbors = 91
06:19:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:19:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760cde09
06:19:57 Searching Annoy index using 1 thread, search_k = 9100
06:19:58 Annoy recall = 100%
06:20:04 Commencing smooth kNN distance calibration using 1 thread
06:20:17 Initializing from normalized Laplacian + noise
06:20:17 Commencing optimization for 500 epochs, with 128290 positive edges
06:20:27 Optimization finished
[1] "91 0.04"06:20:27 UMAP embedding parameters a = 1.786 b = 0.8316
06:20:27 Read 1203 rows and found 38 numeric columns
06:20:27 Using Annoy for neighbor search, n_neighbors = 91
06:20:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:20:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755a2f7d3
06:20:27 Searching Annoy index using 1 thread, search_k = 9100
06:20:28 Annoy recall = 100%
06:20:34 Commencing smooth kNN distance calibration using 1 thread
06:20:47 Initializing from normalized Laplacian + noise
06:20:47 Commencing optimization for 500 epochs, with 128290 positive edges
06:20:57 Optimization finished
[1] "91 0.05"06:20:57 UMAP embedding parameters a = 1.75 b = 0.8421
06:20:57 Read 1203 rows and found 38 numeric columns
06:20:57 Using Annoy for neighbor search, n_neighbors = 91
06:20:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:20:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ed2e73
06:20:58 Searching Annoy index using 1 thread, search_k = 9100
06:20:58 Annoy recall = 100%
06:21:05 Commencing smooth kNN distance calibration using 1 thread
06:21:17 Initializing from normalized Laplacian + noise
06:21:17 Commencing optimization for 500 epochs, with 128290 positive edges
06:21:27 Optimization finished
[1] "91 0.06"06:21:27 UMAP embedding parameters a = 1.715 b = 0.8526
06:21:27 Read 1203 rows and found 38 numeric columns
06:21:27 Using Annoy for neighbor search, n_neighbors = 91
06:21:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:21:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764f7e9b5
06:21:28 Searching Annoy index using 1 thread, search_k = 9100
06:21:29 Annoy recall = 100%
06:21:35 Commencing smooth kNN distance calibration using 1 thread
06:21:48 Initializing from normalized Laplacian + noise
06:21:48 Commencing optimization for 500 epochs, with 128290 positive edges
06:21:58 Optimization finished
[1] "91 0.07"06:21:58 UMAP embedding parameters a = 1.68 b = 0.8631
06:21:58 Read 1203 rows and found 38 numeric columns
06:21:58 Using Annoy for neighbor search, n_neighbors = 91
06:21:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:21:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725275972
06:21:58 Searching Annoy index using 1 thread, search_k = 9100
06:21:59 Annoy recall = 100%
06:22:05 Commencing smooth kNN distance calibration using 1 thread
06:22:18 Initializing from normalized Laplacian + noise
06:22:18 Commencing optimization for 500 epochs, with 128290 positive edges
06:22:28 Optimization finished
[1] "91 0.08"06:22:28 UMAP embedding parameters a = 1.645 b = 0.8737
06:22:28 Read 1203 rows and found 38 numeric columns
06:22:28 Using Annoy for neighbor search, n_neighbors = 91
06:22:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:22:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773578f52
06:22:28 Searching Annoy index using 1 thread, search_k = 9100
06:22:29 Annoy recall = 100%
06:22:35 Commencing smooth kNN distance calibration using 1 thread
06:22:48 Initializing from normalized Laplacian + noise
06:22:48 Commencing optimization for 500 epochs, with 128290 positive edges
06:22:58 Optimization finished
[1] "91 0.09"06:22:58 UMAP embedding parameters a = 1.611 b = 0.8844
06:22:58 Read 1203 rows and found 38 numeric columns
06:22:58 Using Annoy for neighbor search, n_neighbors = 91
06:22:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:22:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87309b734c
06:22:59 Searching Annoy index using 1 thread, search_k = 9100
06:23:00 Annoy recall = 100%
06:23:06 Commencing smooth kNN distance calibration using 1 thread
06:23:18 Initializing from normalized Laplacian + noise
06:23:18 Commencing optimization for 500 epochs, with 128290 positive edges
06:23:28 Optimization finished
[1] "91 0.1"06:23:28 UMAP embedding parameters a = 1.577 b = 0.8951
06:23:28 Read 1203 rows and found 38 numeric columns
06:23:29 Using Annoy for neighbor search, n_neighbors = 91
06:23:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:23:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713b0fa1f
06:23:29 Searching Annoy index using 1 thread, search_k = 9100
06:23:30 Annoy recall = 100%
06:23:36 Commencing smooth kNN distance calibration using 1 thread
06:23:49 Initializing from normalized Laplacian + noise
06:23:49 Commencing optimization for 500 epochs, with 128290 positive edges
06:23:59 Optimization finished
[1] "91 0.11"06:23:59 UMAP embedding parameters a = 1.544 b = 0.9058
06:23:59 Read 1203 rows and found 38 numeric columns
06:23:59 Using Annoy for neighbor search, n_neighbors = 91
06:23:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:23:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724521ffa
06:23:59 Searching Annoy index using 1 thread, search_k = 9100
06:24:00 Annoy recall = 100%
06:24:06 Commencing smooth kNN distance calibration using 1 thread
06:24:19 Initializing from normalized Laplacian + noise
06:24:19 Commencing optimization for 500 epochs, with 128290 positive edges
06:24:29 Optimization finished
[1] "91 0.12"06:24:29 UMAP embedding parameters a = 1.51 b = 0.9165
06:24:29 Read 1203 rows and found 38 numeric columns
06:24:29 Using Annoy for neighbor search, n_neighbors = 91
06:24:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:24:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ed6e330
06:24:30 Searching Annoy index using 1 thread, search_k = 9100
06:24:30 Annoy recall = 100%
06:24:37 Commencing smooth kNN distance calibration using 1 thread
06:24:49 Initializing from normalized Laplacian + noise
06:24:49 Commencing optimization for 500 epochs, with 128290 positive edges
06:24:59 Optimization finished
[1] "91 0.13"06:25:00 UMAP embedding parameters a = 1.478 b = 0.9272
06:25:00 Read 1203 rows and found 38 numeric columns
06:25:00 Using Annoy for neighbor search, n_neighbors = 91
06:25:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:25:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e5bc5f8
06:25:00 Searching Annoy index using 1 thread, search_k = 9100
06:25:01 Annoy recall = 100%
06:25:07 Commencing smooth kNN distance calibration using 1 thread
06:25:20 Initializing from normalized Laplacian + noise
06:25:20 Commencing optimization for 500 epochs, with 128290 positive edges
06:25:30 Optimization finished
[1] "91 0.14"06:25:30 UMAP embedding parameters a = 1.446 b = 0.938
06:25:30 Read 1203 rows and found 38 numeric columns
06:25:30 Using Annoy for neighbor search, n_neighbors = 91
06:25:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:25:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87144aebf3
06:25:30 Searching Annoy index using 1 thread, search_k = 9100
06:25:31 Annoy recall = 100%
06:25:37 Commencing smooth kNN distance calibration using 1 thread
06:25:50 Initializing from normalized Laplacian + noise
06:25:50 Commencing optimization for 500 epochs, with 128290 positive edges
06:26:00 Optimization finished
[1] "91 0.15"06:26:00 UMAP embedding parameters a = 1.414 b = 0.9488
06:26:00 Read 1203 rows and found 38 numeric columns
06:26:00 Using Annoy for neighbor search, n_neighbors = 91
06:26:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:26:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e20215a
06:26:01 Searching Annoy index using 1 thread, search_k = 9100
06:26:01 Annoy recall = 100%
06:26:08 Commencing smooth kNN distance calibration using 1 thread
06:26:20 Initializing from normalized Laplacian + noise
06:26:20 Commencing optimization for 500 epochs, with 128290 positive edges
06:26:30 Optimization finished
[1] "91 0.16"06:26:31 UMAP embedding parameters a = 1.383 b = 0.9596
06:26:31 Read 1203 rows and found 38 numeric columns
06:26:31 Using Annoy for neighbor search, n_neighbors = 91
06:26:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:26:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877cf6ec19
06:26:31 Searching Annoy index using 1 thread, search_k = 9100
06:26:32 Annoy recall = 100%
06:26:38 Commencing smooth kNN distance calibration using 1 thread
06:26:51 Initializing from normalized Laplacian + noise
06:26:51 Commencing optimization for 500 epochs, with 128290 positive edges
06:27:01 Optimization finished
[1] "91 0.17"06:27:01 UMAP embedding parameters a = 1.352 b = 0.9704
06:27:01 Read 1203 rows and found 38 numeric columns
06:27:01 Using Annoy for neighbor search, n_neighbors = 91
06:27:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:27:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744d9e7f9
06:27:02 Searching Annoy index using 1 thread, search_k = 9100
06:27:02 Annoy recall = 100%
06:27:09 Commencing smooth kNN distance calibration using 1 thread
06:27:21 Initializing from normalized Laplacian + noise
06:27:21 Commencing optimization for 500 epochs, with 128290 positive edges
06:27:31 Optimization finished
[1] "91 0.18"06:27:31 UMAP embedding parameters a = 1.321 b = 0.9813
06:27:31 Read 1203 rows and found 38 numeric columns
06:27:31 Using Annoy for neighbor search, n_neighbors = 91
06:27:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:27:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775bd18ea
06:27:32 Searching Annoy index using 1 thread, search_k = 9100
06:27:33 Annoy recall = 100%
06:27:39 Commencing smooth kNN distance calibration using 1 thread
06:27:52 Initializing from normalized Laplacian + noise
06:27:52 Commencing optimization for 500 epochs, with 128290 positive edges
06:28:02 Optimization finished
[1] "91 0.19"06:28:02 UMAP embedding parameters a = 1.292 b = 0.9921
06:28:02 Read 1203 rows and found 38 numeric columns
06:28:02 Using Annoy for neighbor search, n_neighbors = 91
06:28:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:28:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87598c5846
06:28:02 Searching Annoy index using 1 thread, search_k = 9100
06:28:03 Annoy recall = 100%
06:28:09 Commencing smooth kNN distance calibration using 1 thread
06:28:22 Initializing from normalized Laplacian + noise
06:28:22 Commencing optimization for 500 epochs, with 128290 positive edges
06:28:32 Optimization finished
[1] "91 0.2"06:28:32 UMAP embedding parameters a = 1.262 b = 1.003
06:28:32 Read 1203 rows and found 38 numeric columns
06:28:32 Using Annoy for neighbor search, n_neighbors = 91
06:28:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:28:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ef0255f
06:28:33 Searching Annoy index using 1 thread, search_k = 9100
06:28:33 Annoy recall = 100%
06:28:40 Commencing smooth kNN distance calibration using 1 thread
06:28:52 Initializing from normalized Laplacian + noise
06:28:53 Commencing optimization for 500 epochs, with 128290 positive edges
06:29:03 Optimization finished
[1] "92 0"06:29:03 UMAP embedding parameters a = 1.933 b = 0.7905
06:29:03 Read 1203 rows and found 38 numeric columns
06:29:03 Using Annoy for neighbor search, n_neighbors = 92
06:29:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:29:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87547338c5
06:29:03 Searching Annoy index using 1 thread, search_k = 9200
06:29:04 Annoy recall = 100%
06:29:10 Commencing smooth kNN distance calibration using 1 thread
06:29:23 Initializing from normalized Laplacian + noise
06:29:23 Commencing optimization for 500 epochs, with 129564 positive edges
06:29:33 Optimization finished
[1] "92 0.01"06:29:33 UMAP embedding parameters a = 1.896 b = 0.8006
06:29:33 Read 1203 rows and found 38 numeric columns
06:29:33 Using Annoy for neighbor search, n_neighbors = 92
06:29:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:29:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87370848e0
06:29:34 Searching Annoy index using 1 thread, search_k = 9200
06:29:34 Annoy recall = 100%
06:29:41 Commencing smooth kNN distance calibration using 1 thread
06:29:53 Initializing from normalized Laplacian + noise
06:29:53 Commencing optimization for 500 epochs, with 129564 positive edges
06:30:04 Optimization finished
[1] "92 0.02"06:30:04 UMAP embedding parameters a = 1.859 b = 0.8109
06:30:04 Read 1203 rows and found 38 numeric columns
06:30:04 Using Annoy for neighbor search, n_neighbors = 92
06:30:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:30:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757622d94
06:30:04 Searching Annoy index using 1 thread, search_k = 9200
06:30:05 Annoy recall = 100%
06:30:11 Commencing smooth kNN distance calibration using 1 thread
06:30:24 Initializing from normalized Laplacian + noise
06:30:24 Commencing optimization for 500 epochs, with 129564 positive edges
06:30:34 Optimization finished
[1] "92 0.03"06:30:34 UMAP embedding parameters a = 1.822 b = 0.8212
06:30:34 Read 1203 rows and found 38 numeric columns
06:30:34 Using Annoy for neighbor search, n_neighbors = 92
06:30:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:30:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873969b913
06:30:35 Searching Annoy index using 1 thread, search_k = 9200
06:30:35 Annoy recall = 100%
06:30:42 Commencing smooth kNN distance calibration using 1 thread
06:30:54 Initializing from normalized Laplacian + noise
06:30:55 Commencing optimization for 500 epochs, with 129564 positive edges
06:31:05 Optimization finished
[1] "92 0.04"06:31:05 UMAP embedding parameters a = 1.786 b = 0.8316
06:31:05 Read 1203 rows and found 38 numeric columns
06:31:05 Using Annoy for neighbor search, n_neighbors = 92
06:31:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:31:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877432ea07
06:31:05 Searching Annoy index using 1 thread, search_k = 9200
06:31:06 Annoy recall = 100%
06:31:12 Commencing smooth kNN distance calibration using 1 thread
06:31:25 Initializing from normalized Laplacian + noise
06:31:25 Commencing optimization for 500 epochs, with 129564 positive edges
06:31:35 Optimization finished
[1] "92 0.05"06:31:35 UMAP embedding parameters a = 1.75 b = 0.8421
06:31:35 Read 1203 rows and found 38 numeric columns
06:31:35 Using Annoy for neighbor search, n_neighbors = 92
06:31:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:31:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87407f7ba6
06:31:36 Searching Annoy index using 1 thread, search_k = 9200
06:31:36 Annoy recall = 100%
06:31:43 Commencing smooth kNN distance calibration using 1 thread
06:31:55 Initializing from normalized Laplacian + noise
06:31:56 Commencing optimization for 500 epochs, with 129564 positive edges
06:32:06 Optimization finished
[1] "92 0.06"06:32:06 UMAP embedding parameters a = 1.715 b = 0.8526
06:32:06 Read 1203 rows and found 38 numeric columns
06:32:06 Using Annoy for neighbor search, n_neighbors = 92
06:32:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:32:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a00c6ba
06:32:06 Searching Annoy index using 1 thread, search_k = 9200
06:32:07 Annoy recall = 100%
06:32:13 Commencing smooth kNN distance calibration using 1 thread
06:32:26 Initializing from normalized Laplacian + noise
06:32:26 Commencing optimization for 500 epochs, with 129564 positive edges
06:32:36 Optimization finished
[1] "92 0.07"06:32:36 UMAP embedding parameters a = 1.68 b = 0.8631
06:32:36 Read 1203 rows and found 38 numeric columns
06:32:36 Using Annoy for neighbor search, n_neighbors = 92
06:32:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:32:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730f23b4e
06:32:37 Searching Annoy index using 1 thread, search_k = 9200
06:32:38 Annoy recall = 100%
06:32:44 Commencing smooth kNN distance calibration using 1 thread
06:32:57 Initializing from normalized Laplacian + noise
06:32:57 Commencing optimization for 500 epochs, with 129564 positive edges
06:33:07 Optimization finished
[1] "92 0.08"06:33:07 UMAP embedding parameters a = 1.645 b = 0.8737
06:33:07 Read 1203 rows and found 38 numeric columns
06:33:07 Using Annoy for neighbor search, n_neighbors = 92
06:33:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:33:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775db107c
06:33:08 Searching Annoy index using 1 thread, search_k = 9200
06:33:08 Annoy recall = 100%
06:33:15 Commencing smooth kNN distance calibration using 1 thread
06:33:27 Initializing from normalized Laplacian + noise
06:33:27 Commencing optimization for 500 epochs, with 129564 positive edges
06:33:37 Optimization finished
[1] "92 0.09"06:33:38 UMAP embedding parameters a = 1.611 b = 0.8844
06:33:38 Read 1203 rows and found 38 numeric columns
06:33:38 Using Annoy for neighbor search, n_neighbors = 92
06:33:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:33:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f27f689
06:33:38 Searching Annoy index using 1 thread, search_k = 9200
06:33:39 Annoy recall = 100%
06:33:45 Commencing smooth kNN distance calibration using 1 thread
06:33:58 Initializing from normalized Laplacian + noise
06:33:58 Commencing optimization for 500 epochs, with 129564 positive edges
06:34:08 Optimization finished
[1] "92 0.1"06:34:08 UMAP embedding parameters a = 1.577 b = 0.8951
06:34:08 Read 1203 rows and found 38 numeric columns
06:34:08 Using Annoy for neighbor search, n_neighbors = 92
06:34:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:34:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b39c737
06:34:09 Searching Annoy index using 1 thread, search_k = 9200
06:34:09 Annoy recall = 100%
06:34:16 Commencing smooth kNN distance calibration using 1 thread
06:34:29 Initializing from normalized Laplacian + noise
06:34:29 Commencing optimization for 500 epochs, with 129564 positive edges
06:34:39 Optimization finished
[1] "92 0.11"06:34:39 UMAP embedding parameters a = 1.544 b = 0.9058
06:34:39 Read 1203 rows and found 38 numeric columns
06:34:39 Using Annoy for neighbor search, n_neighbors = 92
06:34:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:34:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bb11e5f
06:34:39 Searching Annoy index using 1 thread, search_k = 9200
06:34:40 Annoy recall = 100%
06:34:46 Commencing smooth kNN distance calibration using 1 thread
06:34:59 Initializing from normalized Laplacian + noise
06:34:59 Commencing optimization for 500 epochs, with 129564 positive edges
06:35:09 Optimization finished
[1] "92 0.12"06:35:09 UMAP embedding parameters a = 1.51 b = 0.9165
06:35:09 Read 1203 rows and found 38 numeric columns
06:35:09 Using Annoy for neighbor search, n_neighbors = 92
06:35:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:35:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723846348
06:35:10 Searching Annoy index using 1 thread, search_k = 9200
06:35:11 Annoy recall = 100%
06:35:17 Commencing smooth kNN distance calibration using 1 thread
06:35:30 Initializing from normalized Laplacian + noise
06:35:30 Commencing optimization for 500 epochs, with 129564 positive edges
06:35:40 Optimization finished
[1] "92 0.13"06:35:40 UMAP embedding parameters a = 1.478 b = 0.9272
06:35:40 Read 1203 rows and found 38 numeric columns
06:35:40 Using Annoy for neighbor search, n_neighbors = 92
06:35:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:35:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876146a540
06:35:40 Searching Annoy index using 1 thread, search_k = 9200
06:35:41 Annoy recall = 100%
06:35:47 Commencing smooth kNN distance calibration using 1 thread
06:36:00 Initializing from normalized Laplacian + noise
06:36:00 Commencing optimization for 500 epochs, with 129564 positive edges
06:36:10 Optimization finished
[1] "92 0.14"06:36:11 UMAP embedding parameters a = 1.446 b = 0.938
06:36:11 Read 1203 rows and found 38 numeric columns
06:36:11 Using Annoy for neighbor search, n_neighbors = 92
06:36:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:36:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751541633
06:36:11 Searching Annoy index using 1 thread, search_k = 9200
06:36:12 Annoy recall = 100%
06:36:18 Commencing smooth kNN distance calibration using 1 thread
06:36:32 Initializing from normalized Laplacian + noise
06:36:32 Commencing optimization for 500 epochs, with 129564 positive edges
06:36:42 Optimization finished
[1] "92 0.15"06:36:42 UMAP embedding parameters a = 1.414 b = 0.9488
06:36:42 Read 1203 rows and found 38 numeric columns
06:36:42 Using Annoy for neighbor search, n_neighbors = 92
06:36:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:36:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87257191bb
06:36:42 Searching Annoy index using 1 thread, search_k = 9200
06:36:43 Annoy recall = 100%
06:36:50 Commencing smooth kNN distance calibration using 1 thread
06:37:03 Initializing from normalized Laplacian + noise
06:37:03 Commencing optimization for 500 epochs, with 129564 positive edges
06:37:13 Optimization finished
[1] "92 0.16"06:37:13 UMAP embedding parameters a = 1.383 b = 0.9596
06:37:13 Read 1203 rows and found 38 numeric columns
06:37:13 Using Annoy for neighbor search, n_neighbors = 92
06:37:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:37:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87463e8ef5
06:37:14 Searching Annoy index using 1 thread, search_k = 9200
06:37:14 Annoy recall = 100%
06:37:21 Commencing smooth kNN distance calibration using 1 thread
06:37:34 Initializing from normalized Laplacian + noise
06:37:34 Commencing optimization for 500 epochs, with 129564 positive edges
06:37:44 Optimization finished
[1] "92 0.17"06:37:45 UMAP embedding parameters a = 1.352 b = 0.9704
06:37:45 Read 1203 rows and found 38 numeric columns
06:37:45 Using Annoy for neighbor search, n_neighbors = 92
06:37:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:37:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87767b6fa5
06:37:45 Searching Annoy index using 1 thread, search_k = 9200
06:37:46 Annoy recall = 100%
06:37:52 Commencing smooth kNN distance calibration using 1 thread
06:38:05 Initializing from normalized Laplacian + noise
06:38:05 Commencing optimization for 500 epochs, with 129564 positive edges
06:38:16 Optimization finished
[1] "92 0.18"06:38:16 UMAP embedding parameters a = 1.321 b = 0.9813
06:38:16 Read 1203 rows and found 38 numeric columns
06:38:16 Using Annoy for neighbor search, n_neighbors = 92
06:38:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:38:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718c9210d
06:38:16 Searching Annoy index using 1 thread, search_k = 9200
06:38:17 Annoy recall = 100%
06:38:24 Commencing smooth kNN distance calibration using 1 thread
06:38:37 Initializing from normalized Laplacian + noise
06:38:37 Commencing optimization for 500 epochs, with 129564 positive edges
06:38:47 Optimization finished
[1] "92 0.19"06:38:47 UMAP embedding parameters a = 1.292 b = 0.9921
06:38:47 Read 1203 rows and found 38 numeric columns
06:38:47 Using Annoy for neighbor search, n_neighbors = 92
06:38:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:38:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776da0241
06:38:48 Searching Annoy index using 1 thread, search_k = 9200
06:38:48 Annoy recall = 100%
06:38:55 Commencing smooth kNN distance calibration using 1 thread
06:39:08 Initializing from normalized Laplacian + noise
06:39:08 Commencing optimization for 500 epochs, with 129564 positive edges
06:39:18 Optimization finished
[1] "92 0.2"06:39:19 UMAP embedding parameters a = 1.262 b = 1.003
06:39:19 Read 1203 rows and found 38 numeric columns
06:39:19 Using Annoy for neighbor search, n_neighbors = 92
06:39:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:39:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a2c69c5
06:39:19 Searching Annoy index using 1 thread, search_k = 9200
06:39:20 Annoy recall = 100%
06:39:26 Commencing smooth kNN distance calibration using 1 thread
06:39:39 Initializing from normalized Laplacian + noise
06:39:40 Commencing optimization for 500 epochs, with 129564 positive edges
06:39:50 Optimization finished
[1] "93 0"06:39:50 UMAP embedding parameters a = 1.933 b = 0.7905
06:39:50 Read 1203 rows and found 38 numeric columns
06:39:50 Using Annoy for neighbor search, n_neighbors = 93
06:39:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:39:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d1b4108
06:39:50 Searching Annoy index using 1 thread, search_k = 9300
06:39:51 Annoy recall = 100%
06:39:58 Commencing smooth kNN distance calibration using 1 thread
06:40:11 Initializing from normalized Laplacian + noise
06:40:11 Commencing optimization for 500 epochs, with 130892 positive edges
06:40:21 Optimization finished
[1] "93 0.01"06:40:21 UMAP embedding parameters a = 1.896 b = 0.8006
06:40:21 Read 1203 rows and found 38 numeric columns
06:40:21 Using Annoy for neighbor search, n_neighbors = 93
06:40:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:40:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715b0e572
06:40:22 Searching Annoy index using 1 thread, search_k = 9300
06:40:22 Annoy recall = 100%
06:40:29 Commencing smooth kNN distance calibration using 1 thread
06:40:42 Initializing from normalized Laplacian + noise
06:40:42 Commencing optimization for 500 epochs, with 130892 positive edges
06:40:52 Optimization finished
[1] "93 0.02"06:40:53 UMAP embedding parameters a = 1.859 b = 0.8109
06:40:53 Read 1203 rows and found 38 numeric columns
06:40:53 Using Annoy for neighbor search, n_neighbors = 93
06:40:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:40:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758882fbd
06:40:53 Searching Annoy index using 1 thread, search_k = 9300
06:40:54 Annoy recall = 100%
06:41:00 Commencing smooth kNN distance calibration using 1 thread
06:41:14 Initializing from normalized Laplacian + noise
06:41:14 Commencing optimization for 500 epochs, with 130892 positive edges
06:41:24 Optimization finished
[1] "93 0.03"06:41:24 UMAP embedding parameters a = 1.822 b = 0.8212
06:41:24 Read 1203 rows and found 38 numeric columns
06:41:24 Using Annoy for neighbor search, n_neighbors = 93
06:41:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:41:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751662cfb
06:41:25 Searching Annoy index using 1 thread, search_k = 9300
06:41:25 Annoy recall = 100%
06:41:32 Commencing smooth kNN distance calibration using 1 thread
06:41:45 Initializing from normalized Laplacian + noise
06:41:45 Commencing optimization for 500 epochs, with 130892 positive edges
06:41:55 Optimization finished
[1] "93 0.04"06:41:55 UMAP embedding parameters a = 1.786 b = 0.8316
06:41:55 Read 1203 rows and found 38 numeric columns
06:41:55 Using Annoy for neighbor search, n_neighbors = 93
06:41:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:41:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763d106cc
06:41:56 Searching Annoy index using 1 thread, search_k = 9300
06:41:57 Annoy recall = 100%
06:42:03 Commencing smooth kNN distance calibration using 1 thread
06:42:16 Initializing from normalized Laplacian + noise
06:42:16 Commencing optimization for 500 epochs, with 130892 positive edges
06:42:27 Optimization finished
[1] "93 0.05"06:42:27 UMAP embedding parameters a = 1.75 b = 0.8421
06:42:27 Read 1203 rows and found 38 numeric columns
06:42:27 Using Annoy for neighbor search, n_neighbors = 93
06:42:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:42:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87557f1bd6
06:42:27 Searching Annoy index using 1 thread, search_k = 9300
06:42:28 Annoy recall = 100%
06:42:35 Commencing smooth kNN distance calibration using 1 thread
06:42:48 Initializing from normalized Laplacian + noise
06:42:48 Commencing optimization for 500 epochs, with 130892 positive edges
06:42:58 Optimization finished
[1] "93 0.06"06:42:58 UMAP embedding parameters a = 1.715 b = 0.8526
06:42:58 Read 1203 rows and found 38 numeric columns
06:42:58 Using Annoy for neighbor search, n_neighbors = 93
06:42:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:42:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87164014f4
06:42:59 Searching Annoy index using 1 thread, search_k = 9300
06:43:00 Annoy recall = 100%
06:43:06 Commencing smooth kNN distance calibration using 1 thread
06:43:19 Initializing from normalized Laplacian + noise
06:43:19 Commencing optimization for 500 epochs, with 130892 positive edges
06:43:30 Optimization finished
[1] "93 0.07"06:43:30 UMAP embedding parameters a = 1.68 b = 0.8631
06:43:30 Read 1203 rows and found 38 numeric columns
06:43:30 Using Annoy for neighbor search, n_neighbors = 93
06:43:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:43:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87598e1fb6
06:43:30 Searching Annoy index using 1 thread, search_k = 9300
06:43:31 Annoy recall = 100%
06:43:38 Commencing smooth kNN distance calibration using 1 thread
06:43:51 Initializing from normalized Laplacian + noise
06:43:51 Commencing optimization for 500 epochs, with 130892 positive edges
06:44:01 Optimization finished
[1] "93 0.08"06:44:01 UMAP embedding parameters a = 1.645 b = 0.8737
06:44:01 Read 1203 rows and found 38 numeric columns
06:44:01 Using Annoy for neighbor search, n_neighbors = 93
06:44:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:44:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f0b741c
06:44:02 Searching Annoy index using 1 thread, search_k = 9300
06:44:02 Annoy recall = 100%
06:44:09 Commencing smooth kNN distance calibration using 1 thread
06:44:22 Initializing from normalized Laplacian + noise
06:44:22 Commencing optimization for 500 epochs, with 130892 positive edges
06:44:33 Optimization finished
[1] "93 0.09"06:44:33 UMAP embedding parameters a = 1.611 b = 0.8844
06:44:33 Read 1203 rows and found 38 numeric columns
06:44:33 Using Annoy for neighbor search, n_neighbors = 93
06:44:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:44:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875303a53
06:44:33 Searching Annoy index using 1 thread, search_k = 9300
06:44:34 Annoy recall = 100%
06:44:41 Commencing smooth kNN distance calibration using 1 thread
06:44:54 Initializing from normalized Laplacian + noise
06:44:54 Commencing optimization for 500 epochs, with 130892 positive edges
06:45:04 Optimization finished
[1] "93 0.1"06:45:04 UMAP embedding parameters a = 1.577 b = 0.8951
06:45:04 Read 1203 rows and found 38 numeric columns
06:45:04 Using Annoy for neighbor search, n_neighbors = 93
06:45:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:45:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e01587b
06:45:05 Searching Annoy index using 1 thread, search_k = 9300
06:45:05 Annoy recall = 100%
06:45:12 Commencing smooth kNN distance calibration using 1 thread
06:45:25 Initializing from normalized Laplacian + noise
06:45:25 Commencing optimization for 500 epochs, with 130892 positive edges
06:45:35 Optimization finished
[1] "93 0.11"06:45:36 UMAP embedding parameters a = 1.544 b = 0.9058
06:45:36 Read 1203 rows and found 38 numeric columns
06:45:36 Using Annoy for neighbor search, n_neighbors = 93
06:45:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:45:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876613bcfc
06:45:36 Searching Annoy index using 1 thread, search_k = 9300
06:45:37 Annoy recall = 100%
06:45:43 Commencing smooth kNN distance calibration using 1 thread
06:45:56 Initializing from normalized Laplacian + noise
06:45:57 Commencing optimization for 500 epochs, with 130892 positive edges
06:46:07 Optimization finished
[1] "93 0.12"06:46:07 UMAP embedding parameters a = 1.51 b = 0.9165
06:46:07 Read 1203 rows and found 38 numeric columns
06:46:07 Using Annoy for neighbor search, n_neighbors = 93
06:46:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:46:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c9267e7
06:46:07 Searching Annoy index using 1 thread, search_k = 9300
06:46:08 Annoy recall = 100%
06:46:14 Commencing smooth kNN distance calibration using 1 thread
06:46:27 Initializing from normalized Laplacian + noise
06:46:27 Commencing optimization for 500 epochs, with 130892 positive edges
06:46:38 Optimization finished
[1] "93 0.13"06:46:38 UMAP embedding parameters a = 1.478 b = 0.9272
06:46:38 Read 1203 rows and found 38 numeric columns
06:46:38 Using Annoy for neighbor search, n_neighbors = 93
06:46:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:46:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87676b118e
06:46:38 Searching Annoy index using 1 thread, search_k = 9300
06:46:39 Annoy recall = 100%
06:46:45 Commencing smooth kNN distance calibration using 1 thread
06:46:58 Initializing from normalized Laplacian + noise
06:46:58 Commencing optimization for 500 epochs, with 130892 positive edges
06:47:08 Optimization finished
[1] "93 0.14"06:47:09 UMAP embedding parameters a = 1.446 b = 0.938
06:47:09 Read 1203 rows and found 38 numeric columns
06:47:09 Using Annoy for neighbor search, n_neighbors = 93
06:47:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:47:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a46a704
06:47:09 Searching Annoy index using 1 thread, search_k = 9300
06:47:10 Annoy recall = 100%
06:47:16 Commencing smooth kNN distance calibration using 1 thread
06:47:29 Initializing from normalized Laplacian + noise
06:47:29 Commencing optimization for 500 epochs, with 130892 positive edges
06:47:39 Optimization finished
[1] "93 0.15"06:47:39 UMAP embedding parameters a = 1.414 b = 0.9488
06:47:39 Read 1203 rows and found 38 numeric columns
06:47:39 Using Annoy for neighbor search, n_neighbors = 93
06:47:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:47:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d11e38d
06:47:40 Searching Annoy index using 1 thread, search_k = 9300
06:47:41 Annoy recall = 100%
06:47:47 Commencing smooth kNN distance calibration using 1 thread
06:48:00 Initializing from normalized Laplacian + noise
06:48:00 Commencing optimization for 500 epochs, with 130892 positive edges
06:48:10 Optimization finished
[1] "93 0.16"06:48:10 UMAP embedding parameters a = 1.383 b = 0.9596
06:48:10 Read 1203 rows and found 38 numeric columns
06:48:10 Using Annoy for neighbor search, n_neighbors = 93
06:48:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:48:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87416bd848
06:48:11 Searching Annoy index using 1 thread, search_k = 9300
06:48:11 Annoy recall = 100%
06:48:18 Commencing smooth kNN distance calibration using 1 thread
06:48:31 Initializing from normalized Laplacian + noise
06:48:31 Commencing optimization for 500 epochs, with 130892 positive edges
06:48:41 Optimization finished
[1] "93 0.17"06:48:41 UMAP embedding parameters a = 1.352 b = 0.9704
06:48:41 Read 1203 rows and found 38 numeric columns
06:48:41 Using Annoy for neighbor search, n_neighbors = 93
06:48:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:48:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b38e252
06:48:42 Searching Annoy index using 1 thread, search_k = 9300
06:48:42 Annoy recall = 100%
06:48:49 Commencing smooth kNN distance calibration using 1 thread
06:49:02 Initializing from normalized Laplacian + noise
06:49:02 Commencing optimization for 500 epochs, with 130892 positive edges
06:49:12 Optimization finished
[1] "93 0.18"06:49:12 UMAP embedding parameters a = 1.321 b = 0.9813
06:49:12 Read 1203 rows and found 38 numeric columns
06:49:12 Using Annoy for neighbor search, n_neighbors = 93
06:49:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:49:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712ecf40a
06:49:13 Searching Annoy index using 1 thread, search_k = 9300
06:49:13 Annoy recall = 100%
06:49:20 Commencing smooth kNN distance calibration using 1 thread
06:49:32 Initializing from normalized Laplacian + noise
06:49:33 Commencing optimization for 500 epochs, with 130892 positive edges
06:49:43 Optimization finished
[1] "93 0.19"06:49:43 UMAP embedding parameters a = 1.292 b = 0.9921
06:49:43 Read 1203 rows and found 38 numeric columns
06:49:43 Using Annoy for neighbor search, n_neighbors = 93
06:49:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:49:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8793ced1
06:49:43 Searching Annoy index using 1 thread, search_k = 9300
06:49:44 Annoy recall = 100%
06:49:51 Commencing smooth kNN distance calibration using 1 thread
06:50:04 Initializing from normalized Laplacian + noise
06:50:04 Commencing optimization for 500 epochs, with 130892 positive edges
06:50:14 Optimization finished
[1] "93 0.2"06:50:14 UMAP embedding parameters a = 1.262 b = 1.003
06:50:14 Read 1203 rows and found 38 numeric columns
06:50:14 Using Annoy for neighbor search, n_neighbors = 93
06:50:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:50:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876672a989
06:50:14 Searching Annoy index using 1 thread, search_k = 9300
06:50:15 Annoy recall = 100%
06:50:21 Commencing smooth kNN distance calibration using 1 thread
06:50:34 Initializing from normalized Laplacian + noise
06:50:34 Commencing optimization for 500 epochs, with 130892 positive edges
06:50:45 Optimization finished
[1] "94 0"06:50:45 UMAP embedding parameters a = 1.933 b = 0.7905
06:50:45 Read 1203 rows and found 38 numeric columns
06:50:45 Using Annoy for neighbor search, n_neighbors = 94
06:50:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:50:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e9e1269
06:50:45 Searching Annoy index using 1 thread, search_k = 9400
06:50:46 Annoy recall = 100%
06:50:52 Commencing smooth kNN distance calibration using 1 thread
06:51:05 Initializing from normalized Laplacian + noise
06:51:05 Commencing optimization for 500 epochs, with 132176 positive edges
06:51:15 Optimization finished
[1] "94 0.01"06:51:16 UMAP embedding parameters a = 1.896 b = 0.8006
06:51:16 Read 1203 rows and found 38 numeric columns
06:51:16 Using Annoy for neighbor search, n_neighbors = 94
06:51:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:51:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724183219
06:51:16 Searching Annoy index using 1 thread, search_k = 9400
06:51:17 Annoy recall = 100%
06:51:23 Commencing smooth kNN distance calibration using 1 thread
06:51:36 Initializing from normalized Laplacian + noise
06:51:36 Commencing optimization for 500 epochs, with 132176 positive edges
06:51:46 Optimization finished
[1] "94 0.02"06:51:47 UMAP embedding parameters a = 1.859 b = 0.8109
06:51:47 Read 1203 rows and found 38 numeric columns
06:51:47 Using Annoy for neighbor search, n_neighbors = 94
06:51:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:51:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747b94ec9
06:51:47 Searching Annoy index using 1 thread, search_k = 9400
06:51:48 Annoy recall = 100%
06:51:54 Commencing smooth kNN distance calibration using 1 thread
06:52:07 Initializing from normalized Laplacian + noise
06:52:07 Commencing optimization for 500 epochs, with 132176 positive edges
06:52:17 Optimization finished
[1] "94 0.03"06:52:18 UMAP embedding parameters a = 1.822 b = 0.8212
06:52:18 Read 1203 rows and found 38 numeric columns
06:52:18 Using Annoy for neighbor search, n_neighbors = 94
06:52:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:52:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ff2289c
06:52:18 Searching Annoy index using 1 thread, search_k = 9400
06:52:19 Annoy recall = 100%
06:52:25 Commencing smooth kNN distance calibration using 1 thread
06:52:38 Initializing from normalized Laplacian + noise
06:52:38 Commencing optimization for 500 epochs, with 132176 positive edges
06:52:48 Optimization finished
[1] "94 0.04"06:52:49 UMAP embedding parameters a = 1.786 b = 0.8316
06:52:49 Read 1203 rows and found 38 numeric columns
06:52:49 Using Annoy for neighbor search, n_neighbors = 94
06:52:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:52:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874989c3d4
06:52:49 Searching Annoy index using 1 thread, search_k = 9400
06:52:50 Annoy recall = 100%
06:52:56 Commencing smooth kNN distance calibration using 1 thread
06:53:09 Initializing from normalized Laplacian + noise
06:53:09 Commencing optimization for 500 epochs, with 132176 positive edges
06:53:19 Optimization finished
[1] "94 0.05"06:53:20 UMAP embedding parameters a = 1.75 b = 0.8421
06:53:20 Read 1203 rows and found 38 numeric columns
06:53:20 Using Annoy for neighbor search, n_neighbors = 94
06:53:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:53:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87df7ddbf
06:53:20 Searching Annoy index using 1 thread, search_k = 9400
06:53:21 Annoy recall = 100%
06:53:27 Commencing smooth kNN distance calibration using 1 thread
06:53:40 Initializing from normalized Laplacian + noise
06:53:40 Commencing optimization for 500 epochs, with 132176 positive edges
06:53:50 Optimization finished
[1] "94 0.06"06:53:51 UMAP embedding parameters a = 1.715 b = 0.8526
06:53:51 Read 1203 rows and found 38 numeric columns
06:53:51 Using Annoy for neighbor search, n_neighbors = 94
06:53:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:53:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87566d9842
06:53:51 Searching Annoy index using 1 thread, search_k = 9400
06:53:52 Annoy recall = 100%
06:53:58 Commencing smooth kNN distance calibration using 1 thread
06:54:11 Initializing from normalized Laplacian + noise
06:54:11 Commencing optimization for 500 epochs, with 132176 positive edges
06:54:21 Optimization finished
[1] "94 0.07"06:54:22 UMAP embedding parameters a = 1.68 b = 0.8631
06:54:22 Read 1203 rows and found 38 numeric columns
06:54:22 Using Annoy for neighbor search, n_neighbors = 94
06:54:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:54:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876252e4e2
06:54:22 Searching Annoy index using 1 thread, search_k = 9400
06:54:23 Annoy recall = 100%
06:54:29 Commencing smooth kNN distance calibration using 1 thread
06:54:42 Initializing from normalized Laplacian + noise
06:54:42 Commencing optimization for 500 epochs, with 132176 positive edges
06:54:52 Optimization finished
[1] "94 0.08"06:54:53 UMAP embedding parameters a = 1.645 b = 0.8737
06:54:53 Read 1203 rows and found 38 numeric columns
06:54:53 Using Annoy for neighbor search, n_neighbors = 94
06:54:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:54:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d1e000
06:54:53 Searching Annoy index using 1 thread, search_k = 9400
06:54:54 Annoy recall = 100%
06:55:00 Commencing smooth kNN distance calibration using 1 thread
06:55:13 Initializing from normalized Laplacian + noise
06:55:13 Commencing optimization for 500 epochs, with 132176 positive edges
06:55:24 Optimization finished
[1] "94 0.09"06:55:24 UMAP embedding parameters a = 1.611 b = 0.8844
06:55:24 Read 1203 rows and found 38 numeric columns
06:55:24 Using Annoy for neighbor search, n_neighbors = 94
06:55:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:55:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87609a0207
06:55:24 Searching Annoy index using 1 thread, search_k = 9400
06:55:25 Annoy recall = 100%
06:55:31 Commencing smooth kNN distance calibration using 1 thread
06:55:44 Initializing from normalized Laplacian + noise
06:55:44 Commencing optimization for 500 epochs, with 132176 positive edges
06:55:55 Optimization finished
[1] "94 0.1"06:55:55 UMAP embedding parameters a = 1.577 b = 0.8951
06:55:55 Read 1203 rows and found 38 numeric columns
06:55:55 Using Annoy for neighbor search, n_neighbors = 94
06:55:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:55:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f6e25ea
06:55:55 Searching Annoy index using 1 thread, search_k = 9400
06:55:56 Annoy recall = 100%
06:56:03 Commencing smooth kNN distance calibration using 1 thread
06:56:16 Initializing from normalized Laplacian + noise
06:56:16 Commencing optimization for 500 epochs, with 132176 positive edges
06:56:26 Optimization finished
[1] "94 0.11"06:56:26 UMAP embedding parameters a = 1.544 b = 0.9058
06:56:26 Read 1203 rows and found 38 numeric columns
06:56:26 Using Annoy for neighbor search, n_neighbors = 94
06:56:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:56:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a82c572
06:56:26 Searching Annoy index using 1 thread, search_k = 9400
06:56:27 Annoy recall = 100%
06:56:33 Commencing smooth kNN distance calibration using 1 thread
06:56:46 Initializing from normalized Laplacian + noise
06:56:47 Commencing optimization for 500 epochs, with 132176 positive edges
06:56:57 Optimization finished
[1] "94 0.12"06:56:57 UMAP embedding parameters a = 1.51 b = 0.9165
06:56:57 Read 1203 rows and found 38 numeric columns
06:56:57 Using Annoy for neighbor search, n_neighbors = 94
06:56:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:56:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87392231c4
06:56:57 Searching Annoy index using 1 thread, search_k = 9400
06:56:58 Annoy recall = 100%
06:57:05 Commencing smooth kNN distance calibration using 1 thread
06:57:18 Initializing from normalized Laplacian + noise
06:57:18 Commencing optimization for 500 epochs, with 132176 positive edges
06:57:28 Optimization finished
[1] "94 0.13"06:57:28 UMAP embedding parameters a = 1.478 b = 0.9272
06:57:28 Read 1203 rows and found 38 numeric columns
06:57:28 Using Annoy for neighbor search, n_neighbors = 94
06:57:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:57:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770d452e5
06:57:29 Searching Annoy index using 1 thread, search_k = 9400
06:57:29 Annoy recall = 100%
06:57:36 Commencing smooth kNN distance calibration using 1 thread
06:57:49 Initializing from normalized Laplacian + noise
06:57:49 Commencing optimization for 500 epochs, with 132176 positive edges
06:57:59 Optimization finished
[1] "94 0.14"06:57:59 UMAP embedding parameters a = 1.446 b = 0.938
06:57:59 Read 1203 rows and found 38 numeric columns
06:57:59 Using Annoy for neighbor search, n_neighbors = 94
06:57:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:58:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e53cc3f
06:58:00 Searching Annoy index using 1 thread, search_k = 9400
06:58:00 Annoy recall = 100%
06:58:07 Commencing smooth kNN distance calibration using 1 thread
06:58:20 Initializing from normalized Laplacian + noise
06:58:20 Commencing optimization for 500 epochs, with 132176 positive edges
06:58:30 Optimization finished
[1] "94 0.15"06:58:30 UMAP embedding parameters a = 1.414 b = 0.9488
06:58:30 Read 1203 rows and found 38 numeric columns
06:58:30 Using Annoy for neighbor search, n_neighbors = 94
06:58:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:58:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ea14d9b
06:58:31 Searching Annoy index using 1 thread, search_k = 9400
06:58:32 Annoy recall = 100%
06:58:38 Commencing smooth kNN distance calibration using 1 thread
06:58:51 Initializing from normalized Laplacian + noise
06:58:51 Commencing optimization for 500 epochs, with 132176 positive edges
06:59:01 Optimization finished
[1] "94 0.16"06:59:01 UMAP embedding parameters a = 1.383 b = 0.9596
06:59:02 Read 1203 rows and found 38 numeric columns
06:59:02 Using Annoy for neighbor search, n_neighbors = 94
06:59:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:59:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771467da
06:59:02 Searching Annoy index using 1 thread, search_k = 9400
06:59:03 Annoy recall = 100%
06:59:09 Commencing smooth kNN distance calibration using 1 thread
06:59:22 Initializing from normalized Laplacian + noise
06:59:22 Commencing optimization for 500 epochs, with 132176 positive edges
06:59:32 Optimization finished
[1] "94 0.17"06:59:33 UMAP embedding parameters a = 1.352 b = 0.9704
06:59:33 Read 1203 rows and found 38 numeric columns
06:59:33 Using Annoy for neighbor search, n_neighbors = 94
06:59:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:59:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757e1ebf5
06:59:33 Searching Annoy index using 1 thread, search_k = 9400
06:59:34 Annoy recall = 100%
06:59:40 Commencing smooth kNN distance calibration using 1 thread
06:59:53 Initializing from normalized Laplacian + noise
06:59:53 Commencing optimization for 500 epochs, with 132176 positive edges
07:00:04 Optimization finished
[1] "94 0.18"07:00:04 UMAP embedding parameters a = 1.321 b = 0.9813
07:00:04 Read 1203 rows and found 38 numeric columns
07:00:04 Using Annoy for neighbor search, n_neighbors = 94
07:00:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:00:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873dacc1b7
07:00:04 Searching Annoy index using 1 thread, search_k = 9400
07:00:05 Annoy recall = 100%
07:00:12 Commencing smooth kNN distance calibration using 1 thread
07:00:25 Initializing from normalized Laplacian + noise
07:00:25 Commencing optimization for 500 epochs, with 132176 positive edges
07:00:35 Optimization finished
[1] "94 0.19"07:00:35 UMAP embedding parameters a = 1.292 b = 0.9921
07:00:35 Read 1203 rows and found 38 numeric columns
07:00:35 Using Annoy for neighbor search, n_neighbors = 94
07:00:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:00:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c44a22d
07:00:36 Searching Annoy index using 1 thread, search_k = 9400
07:00:36 Annoy recall = 100%
07:00:43 Commencing smooth kNN distance calibration using 1 thread
07:00:56 Initializing from normalized Laplacian + noise
07:00:56 Commencing optimization for 500 epochs, with 132176 positive edges
07:01:06 Optimization finished
[1] "94 0.2"07:01:06 UMAP embedding parameters a = 1.262 b = 1.003
07:01:06 Read 1203 rows and found 38 numeric columns
07:01:06 Using Annoy for neighbor search, n_neighbors = 94
07:01:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:01:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e34471
07:01:07 Searching Annoy index using 1 thread, search_k = 9400
07:01:07 Annoy recall = 100%
07:01:14 Commencing smooth kNN distance calibration using 1 thread
07:01:27 Initializing from normalized Laplacian + noise
07:01:27 Commencing optimization for 500 epochs, with 132176 positive edges
07:01:37 Optimization finished
[1] "95 0"07:01:37 UMAP embedding parameters a = 1.933 b = 0.7905
07:01:37 Read 1203 rows and found 38 numeric columns
07:01:37 Using Annoy for neighbor search, n_neighbors = 95
07:01:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:01:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723c07eb4
07:01:38 Searching Annoy index using 1 thread, search_k = 9500
07:01:39 Annoy recall = 100%
07:01:45 Commencing smooth kNN distance calibration using 1 thread
07:01:58 Initializing from normalized Laplacian + noise
07:01:58 Commencing optimization for 500 epochs, with 133492 positive edges
07:02:08 Optimization finished
[1] "95 0.01"07:02:09 UMAP embedding parameters a = 1.896 b = 0.8006
07:02:09 Read 1203 rows and found 38 numeric columns
07:02:09 Using Annoy for neighbor search, n_neighbors = 95
07:02:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:02:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768d70a15
07:02:09 Searching Annoy index using 1 thread, search_k = 9500
07:02:10 Annoy recall = 100%
07:02:16 Commencing smooth kNN distance calibration using 1 thread
07:02:30 Initializing from normalized Laplacian + noise
07:02:30 Commencing optimization for 500 epochs, with 133492 positive edges
07:02:40 Optimization finished
[1] "95 0.02"07:02:40 UMAP embedding parameters a = 1.859 b = 0.8109
07:02:40 Read 1203 rows and found 38 numeric columns
07:02:40 Using Annoy for neighbor search, n_neighbors = 95
07:02:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:02:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d4e55ff
07:02:41 Searching Annoy index using 1 thread, search_k = 9500
07:02:41 Annoy recall = 100%
07:02:48 Commencing smooth kNN distance calibration using 1 thread
07:03:01 Initializing from normalized Laplacian + noise
07:03:01 Commencing optimization for 500 epochs, with 133492 positive edges
07:03:11 Optimization finished
[1] "95 0.03"07:03:11 UMAP embedding parameters a = 1.822 b = 0.8212
07:03:11 Read 1203 rows and found 38 numeric columns
07:03:11 Using Annoy for neighbor search, n_neighbors = 95
07:03:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:03:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e0725b8
07:03:12 Searching Annoy index using 1 thread, search_k = 9500
07:03:13 Annoy recall = 100%
07:03:19 Commencing smooth kNN distance calibration using 1 thread
07:03:32 Initializing from normalized Laplacian + noise
07:03:32 Commencing optimization for 500 epochs, with 133492 positive edges
07:03:42 Optimization finished
[1] "95 0.04"07:03:43 UMAP embedding parameters a = 1.786 b = 0.8316
07:03:43 Read 1203 rows and found 38 numeric columns
07:03:43 Using Annoy for neighbor search, n_neighbors = 95
07:03:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:03:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e8eda2
07:03:43 Searching Annoy index using 1 thread, search_k = 9500
07:03:44 Annoy recall = 100%
07:03:50 Commencing smooth kNN distance calibration using 1 thread
07:04:03 Initializing from normalized Laplacian + noise
07:04:03 Commencing optimization for 500 epochs, with 133492 positive edges
07:04:14 Optimization finished
[1] "95 0.05"07:04:14 UMAP embedding parameters a = 1.75 b = 0.8421
07:04:14 Read 1203 rows and found 38 numeric columns
07:04:14 Using Annoy for neighbor search, n_neighbors = 95
07:04:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:04:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872eba2e48
07:04:14 Searching Annoy index using 1 thread, search_k = 9500
07:04:15 Annoy recall = 100%
07:04:22 Commencing smooth kNN distance calibration using 1 thread
07:04:35 Initializing from normalized Laplacian + noise
07:04:35 Commencing optimization for 500 epochs, with 133492 positive edges
07:04:45 Optimization finished
[1] "95 0.06"07:04:45 UMAP embedding parameters a = 1.715 b = 0.8526
07:04:45 Read 1203 rows and found 38 numeric columns
07:04:45 Using Annoy for neighbor search, n_neighbors = 95
07:04:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:04:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87940080a
07:04:46 Searching Annoy index using 1 thread, search_k = 9500
07:04:46 Annoy recall = 100%
07:04:53 Commencing smooth kNN distance calibration using 1 thread
07:05:06 Initializing from normalized Laplacian + noise
07:05:06 Commencing optimization for 500 epochs, with 133492 positive edges
07:05:16 Optimization finished
[1] "95 0.07"07:05:16 UMAP embedding parameters a = 1.68 b = 0.8631
07:05:16 Read 1203 rows and found 38 numeric columns
07:05:16 Using Annoy for neighbor search, n_neighbors = 95
07:05:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:05:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718d5e1ac
07:05:17 Searching Annoy index using 1 thread, search_k = 9500
07:05:18 Annoy recall = 100%
07:05:24 Commencing smooth kNN distance calibration using 1 thread
07:05:37 Initializing from normalized Laplacian + noise
07:05:37 Commencing optimization for 500 epochs, with 133492 positive edges
07:05:47 Optimization finished
[1] "95 0.08"07:05:48 UMAP embedding parameters a = 1.645 b = 0.8737
07:05:48 Read 1203 rows and found 38 numeric columns
07:05:48 Using Annoy for neighbor search, n_neighbors = 95
07:05:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:05:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f4dfd19
07:05:48 Searching Annoy index using 1 thread, search_k = 9500
07:05:49 Annoy recall = 100%
07:05:55 Commencing smooth kNN distance calibration using 1 thread
07:06:08 Initializing from normalized Laplacian + noise
07:06:08 Commencing optimization for 500 epochs, with 133492 positive edges
07:06:19 Optimization finished
[1] "95 0.09"07:06:19 UMAP embedding parameters a = 1.611 b = 0.8844
07:06:19 Read 1203 rows and found 38 numeric columns
07:06:19 Using Annoy for neighbor search, n_neighbors = 95
07:06:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:06:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876fb2b193
07:06:19 Searching Annoy index using 1 thread, search_k = 9500
07:06:20 Annoy recall = 100%
07:06:27 Commencing smooth kNN distance calibration using 1 thread
07:06:40 Initializing from normalized Laplacian + noise
07:06:40 Commencing optimization for 500 epochs, with 133492 positive edges
07:06:50 Optimization finished
[1] "95 0.1"07:06:50 UMAP embedding parameters a = 1.577 b = 0.8951
07:06:50 Read 1203 rows and found 38 numeric columns
07:06:50 Using Annoy for neighbor search, n_neighbors = 95
07:06:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:06:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872773f416
07:06:51 Searching Annoy index using 1 thread, search_k = 9500
07:06:51 Annoy recall = 100%
07:06:58 Commencing smooth kNN distance calibration using 1 thread
07:07:11 Initializing from normalized Laplacian + noise
07:07:11 Commencing optimization for 500 epochs, with 133492 positive edges
07:07:21 Optimization finished
[1] "95 0.11"07:07:21 UMAP embedding parameters a = 1.544 b = 0.9058
07:07:22 Read 1203 rows and found 38 numeric columns
07:07:22 Using Annoy for neighbor search, n_neighbors = 95
07:07:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:07:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753662f33
07:07:22 Searching Annoy index using 1 thread, search_k = 9500
07:07:23 Annoy recall = 100%
07:07:29 Commencing smooth kNN distance calibration using 1 thread
07:07:42 Initializing from normalized Laplacian + noise
07:07:42 Commencing optimization for 500 epochs, with 133492 positive edges
07:07:53 Optimization finished
[1] "95 0.12"07:07:53 UMAP embedding parameters a = 1.51 b = 0.9165
07:07:53 Read 1203 rows and found 38 numeric columns
07:07:53 Using Annoy for neighbor search, n_neighbors = 95
07:07:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:07:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87376c005c
07:07:53 Searching Annoy index using 1 thread, search_k = 9500
07:07:54 Annoy recall = 100%
07:08:00 Commencing smooth kNN distance calibration using 1 thread
07:08:14 Initializing from normalized Laplacian + noise
07:08:14 Commencing optimization for 500 epochs, with 133492 positive edges
07:08:24 Optimization finished
[1] "95 0.13"07:08:24 UMAP embedding parameters a = 1.478 b = 0.9272
07:08:24 Read 1203 rows and found 38 numeric columns
07:08:24 Using Annoy for neighbor search, n_neighbors = 95
07:08:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:08:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877661cb2
07:08:25 Searching Annoy index using 1 thread, search_k = 9500
07:08:25 Annoy recall = 100%
07:08:32 Commencing smooth kNN distance calibration using 1 thread
07:08:45 Initializing from normalized Laplacian + noise
07:08:45 Commencing optimization for 500 epochs, with 133492 positive edges
07:08:55 Optimization finished
[1] "95 0.14"07:08:56 UMAP embedding parameters a = 1.446 b = 0.938
07:08:56 Read 1203 rows and found 38 numeric columns
07:08:56 Using Annoy for neighbor search, n_neighbors = 95
07:08:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:08:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ceff307
07:08:56 Searching Annoy index using 1 thread, search_k = 9500
07:08:57 Annoy recall = 100%
07:09:03 Commencing smooth kNN distance calibration using 1 thread
07:09:16 Initializing from normalized Laplacian + noise
07:09:16 Commencing optimization for 500 epochs, with 133492 positive edges
07:09:27 Optimization finished
[1] "95 0.15"07:09:27 UMAP embedding parameters a = 1.414 b = 0.9488
07:09:27 Read 1203 rows and found 38 numeric columns
07:09:27 Using Annoy for neighbor search, n_neighbors = 95
07:09:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:09:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874563de1b
07:09:27 Searching Annoy index using 1 thread, search_k = 9500
07:09:28 Annoy recall = 100%
07:09:35 Commencing smooth kNN distance calibration using 1 thread
07:09:48 Initializing from normalized Laplacian + noise
07:09:48 Commencing optimization for 500 epochs, with 133492 positive edges
07:09:58 Optimization finished
[1] "95 0.16"07:09:58 UMAP embedding parameters a = 1.383 b = 0.9596
07:09:58 Read 1203 rows and found 38 numeric columns
07:09:58 Using Annoy for neighbor search, n_neighbors = 95
07:09:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:09:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875dd3b4f4
07:09:59 Searching Annoy index using 1 thread, search_k = 9500
07:09:59 Annoy recall = 100%
07:10:06 Commencing smooth kNN distance calibration using 1 thread
07:10:19 Initializing from normalized Laplacian + noise
07:10:19 Commencing optimization for 500 epochs, with 133492 positive edges
07:10:29 Optimization finished
[1] "95 0.17"07:10:30 UMAP embedding parameters a = 1.352 b = 0.9704
07:10:30 Read 1203 rows and found 38 numeric columns
07:10:30 Using Annoy for neighbor search, n_neighbors = 95
07:10:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:10:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f42d7e9
07:10:30 Searching Annoy index using 1 thread, search_k = 9500
07:10:31 Annoy recall = 100%
07:10:37 Commencing smooth kNN distance calibration using 1 thread
07:10:51 Initializing from normalized Laplacian + noise
07:10:51 Commencing optimization for 500 epochs, with 133492 positive edges
07:11:01 Optimization finished
[1] "95 0.18"07:11:01 UMAP embedding parameters a = 1.321 b = 0.9813
07:11:01 Read 1203 rows and found 38 numeric columns
07:11:01 Using Annoy for neighbor search, n_neighbors = 95
07:11:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:11:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a35be1c
07:11:02 Searching Annoy index using 1 thread, search_k = 9500
07:11:02 Annoy recall = 100%
07:11:09 Commencing smooth kNN distance calibration using 1 thread
07:11:22 Initializing from normalized Laplacian + noise
07:11:22 Commencing optimization for 500 epochs, with 133492 positive edges
07:11:32 Optimization finished
[1] "95 0.19"07:11:32 UMAP embedding parameters a = 1.292 b = 0.9921
07:11:32 Read 1203 rows and found 38 numeric columns
07:11:32 Using Annoy for neighbor search, n_neighbors = 95
07:11:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:11:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e6db6fb
07:11:33 Searching Annoy index using 1 thread, search_k = 9500
07:11:34 Annoy recall = 100%
07:11:40 Commencing smooth kNN distance calibration using 1 thread
07:11:53 Initializing from normalized Laplacian + noise
07:11:54 Commencing optimization for 500 epochs, with 133492 positive edges
07:12:04 Optimization finished
[1] "95 0.2"07:12:04 UMAP embedding parameters a = 1.262 b = 1.003
07:12:04 Read 1203 rows and found 38 numeric columns
07:12:04 Using Annoy for neighbor search, n_neighbors = 95
07:12:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:12:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871eb0fdd3
07:12:04 Searching Annoy index using 1 thread, search_k = 9500
07:12:05 Annoy recall = 100%
07:12:12 Commencing smooth kNN distance calibration using 1 thread
07:12:25 Initializing from normalized Laplacian + noise
07:12:25 Commencing optimization for 500 epochs, with 133492 positive edges
07:12:35 Optimization finished
[1] "96 0"07:12:35 UMAP embedding parameters a = 1.933 b = 0.7905
07:12:35 Read 1203 rows and found 38 numeric columns
07:12:35 Using Annoy for neighbor search, n_neighbors = 96
07:12:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:12:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764b8838e
07:12:36 Searching Annoy index using 1 thread, search_k = 9600
07:12:37 Annoy recall = 100%
07:12:43 Commencing smooth kNN distance calibration using 1 thread
07:12:56 Initializing from normalized Laplacian + noise
07:12:56 Commencing optimization for 500 epochs, with 134770 positive edges
07:13:07 Optimization finished
[1] "96 0.01"07:13:07 UMAP embedding parameters a = 1.896 b = 0.8006
07:13:07 Read 1203 rows and found 38 numeric columns
07:13:07 Using Annoy for neighbor search, n_neighbors = 96
07:13:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:13:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87778fe8c0
07:13:07 Searching Annoy index using 1 thread, search_k = 9600
07:13:08 Annoy recall = 100%
07:13:15 Commencing smooth kNN distance calibration using 1 thread
07:13:28 Initializing from normalized Laplacian + noise
07:13:28 Commencing optimization for 500 epochs, with 134770 positive edges
07:13:38 Optimization finished
[1] "96 0.02"07:13:38 UMAP embedding parameters a = 1.859 b = 0.8109
07:13:38 Read 1203 rows and found 38 numeric columns
07:13:38 Using Annoy for neighbor search, n_neighbors = 96
07:13:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:13:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f8550b9
07:13:39 Searching Annoy index using 1 thread, search_k = 9600
07:13:40 Annoy recall = 100%
07:13:46 Commencing smooth kNN distance calibration using 1 thread
07:13:59 Initializing from normalized Laplacian + noise
07:13:59 Commencing optimization for 500 epochs, with 134770 positive edges
07:14:10 Optimization finished
[1] "96 0.03"07:14:10 UMAP embedding parameters a = 1.822 b = 0.8212
07:14:10 Read 1203 rows and found 38 numeric columns
07:14:10 Using Annoy for neighbor search, n_neighbors = 96
07:14:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:14:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87630c4fcd
07:14:10 Searching Annoy index using 1 thread, search_k = 9600
07:14:11 Annoy recall = 100%
07:14:18 Commencing smooth kNN distance calibration using 1 thread
07:14:31 Initializing from normalized Laplacian + noise
07:14:31 Commencing optimization for 500 epochs, with 134770 positive edges
07:14:41 Optimization finished
[1] "96 0.04"07:14:41 UMAP embedding parameters a = 1.786 b = 0.8316
07:14:41 Read 1203 rows and found 38 numeric columns
07:14:41 Using Annoy for neighbor search, n_neighbors = 96
07:14:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:14:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87631365b
07:14:42 Searching Annoy index using 1 thread, search_k = 9600
07:14:43 Annoy recall = 100%
07:14:49 Commencing smooth kNN distance calibration using 1 thread
07:15:02 Initializing from normalized Laplacian + noise
07:15:03 Commencing optimization for 500 epochs, with 134770 positive edges
07:15:13 Optimization finished
[1] "96 0.05"07:15:13 UMAP embedding parameters a = 1.75 b = 0.8421
07:15:13 Read 1203 rows and found 38 numeric columns
07:15:13 Using Annoy for neighbor search, n_neighbors = 96
07:15:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:15:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871699b893
07:15:13 Searching Annoy index using 1 thread, search_k = 9600
07:15:14 Annoy recall = 100%
07:15:21 Commencing smooth kNN distance calibration using 1 thread
07:15:34 Initializing from normalized Laplacian + noise
07:15:34 Commencing optimization for 500 epochs, with 134770 positive edges
07:15:44 Optimization finished
[1] "96 0.06"07:15:45 UMAP embedding parameters a = 1.715 b = 0.8526
07:15:45 Read 1203 rows and found 38 numeric columns
07:15:45 Using Annoy for neighbor search, n_neighbors = 96
07:15:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:15:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873aee3bc3
07:15:45 Searching Annoy index using 1 thread, search_k = 9600
07:15:46 Annoy recall = 100%
07:15:52 Commencing smooth kNN distance calibration using 1 thread
07:16:06 Initializing from normalized Laplacian + noise
07:16:06 Commencing optimization for 500 epochs, with 134770 positive edges
07:16:16 Optimization finished
[1] "96 0.07"07:16:16 UMAP embedding parameters a = 1.68 b = 0.8631
07:16:16 Read 1203 rows and found 38 numeric columns
07:16:16 Using Annoy for neighbor search, n_neighbors = 96
07:16:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:16:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743ddf812
07:16:17 Searching Annoy index using 1 thread, search_k = 9600
07:16:17 Annoy recall = 100%
07:16:24 Commencing smooth kNN distance calibration using 1 thread
07:16:37 Initializing from normalized Laplacian + noise
07:16:37 Commencing optimization for 500 epochs, with 134770 positive edges
07:16:47 Optimization finished
[1] "96 0.08"07:16:48 UMAP embedding parameters a = 1.645 b = 0.8737
07:16:48 Read 1203 rows and found 38 numeric columns
07:16:48 Using Annoy for neighbor search, n_neighbors = 96
07:16:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:16:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722de5ac0
07:16:48 Searching Annoy index using 1 thread, search_k = 9600
07:16:49 Annoy recall = 100%
07:16:56 Commencing smooth kNN distance calibration using 1 thread
07:17:09 Initializing from normalized Laplacian + noise
07:17:09 Commencing optimization for 500 epochs, with 134770 positive edges
07:17:19 Optimization finished
[1] "96 0.09"07:17:19 UMAP embedding parameters a = 1.611 b = 0.8844
07:17:19 Read 1203 rows and found 38 numeric columns
07:17:19 Using Annoy for neighbor search, n_neighbors = 96
07:17:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:17:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740d18034
07:17:20 Searching Annoy index using 1 thread, search_k = 9600
07:17:20 Annoy recall = 100%
07:17:27 Commencing smooth kNN distance calibration using 1 thread
07:17:40 Initializing from normalized Laplacian + noise
07:17:40 Commencing optimization for 500 epochs, with 134770 positive edges
07:17:51 Optimization finished
[1] "96 0.1"07:17:51 UMAP embedding parameters a = 1.577 b = 0.8951
07:17:51 Read 1203 rows and found 38 numeric columns
07:17:51 Using Annoy for neighbor search, n_neighbors = 96
07:17:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:17:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87679e76c6
07:17:51 Searching Annoy index using 1 thread, search_k = 9600
07:17:52 Annoy recall = 100%
07:17:59 Commencing smooth kNN distance calibration using 1 thread
07:18:12 Initializing from normalized Laplacian + noise
07:18:12 Commencing optimization for 500 epochs, with 134770 positive edges
07:18:22 Optimization finished
[1] "96 0.11"07:18:23 UMAP embedding parameters a = 1.544 b = 0.9058
07:18:23 Read 1203 rows and found 38 numeric columns
07:18:23 Using Annoy for neighbor search, n_neighbors = 96
07:18:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:18:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bb564d5
07:18:23 Searching Annoy index using 1 thread, search_k = 9600
07:18:24 Annoy recall = 100%
07:18:30 Commencing smooth kNN distance calibration using 1 thread
07:18:43 Initializing from normalized Laplacian + noise
07:18:44 Commencing optimization for 500 epochs, with 134770 positive edges
07:18:54 Optimization finished
[1] "96 0.12"07:18:54 UMAP embedding parameters a = 1.51 b = 0.9165
07:18:54 Read 1203 rows and found 38 numeric columns
07:18:54 Using Annoy for neighbor search, n_neighbors = 96
07:18:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:18:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e1fd633
07:18:55 Searching Annoy index using 1 thread, search_k = 9600
07:18:55 Annoy recall = 100%
07:19:02 Commencing smooth kNN distance calibration using 1 thread
07:19:15 Initializing from normalized Laplacian + noise
07:19:15 Commencing optimization for 500 epochs, with 134770 positive edges
07:19:26 Optimization finished
[1] "96 0.13"07:19:26 UMAP embedding parameters a = 1.478 b = 0.9272
07:19:26 Read 1203 rows and found 38 numeric columns
07:19:26 Using Annoy for neighbor search, n_neighbors = 96
07:19:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:19:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765a59c7e
07:19:26 Searching Annoy index using 1 thread, search_k = 9600
07:19:27 Annoy recall = 100%
07:19:34 Commencing smooth kNN distance calibration using 1 thread
07:19:47 Initializing from normalized Laplacian + noise
07:19:47 Commencing optimization for 500 epochs, with 134770 positive edges
07:19:57 Optimization finished
[1] "96 0.14"07:19:57 UMAP embedding parameters a = 1.446 b = 0.938
07:19:57 Read 1203 rows and found 38 numeric columns
07:19:57 Using Annoy for neighbor search, n_neighbors = 96
07:19:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:19:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87119e5278
07:19:58 Searching Annoy index using 1 thread, search_k = 9600
07:19:59 Annoy recall = 100%
07:20:05 Commencing smooth kNN distance calibration using 1 thread
07:20:19 Initializing from normalized Laplacian + noise
07:20:19 Commencing optimization for 500 epochs, with 134770 positive edges
07:20:29 Optimization finished
[1] "96 0.15"07:20:29 UMAP embedding parameters a = 1.414 b = 0.9488
07:20:29 Read 1203 rows and found 38 numeric columns
07:20:29 Using Annoy for neighbor search, n_neighbors = 96
07:20:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:20:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cda047b
07:20:30 Searching Annoy index using 1 thread, search_k = 9600
07:20:30 Annoy recall = 100%
07:20:37 Commencing smooth kNN distance calibration using 1 thread
07:20:50 Initializing from normalized Laplacian + noise
07:20:50 Commencing optimization for 500 epochs, with 134770 positive edges
07:21:01 Optimization finished
[1] "96 0.16"07:21:01 UMAP embedding parameters a = 1.383 b = 0.9596
07:21:01 Read 1203 rows and found 38 numeric columns
07:21:01 Using Annoy for neighbor search, n_neighbors = 96
07:21:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:21:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ee5a488
07:21:01 Searching Annoy index using 1 thread, search_k = 9600
07:21:02 Annoy recall = 100%
07:21:09 Commencing smooth kNN distance calibration using 1 thread
07:21:22 Initializing from normalized Laplacian + noise
07:21:22 Commencing optimization for 500 epochs, with 134770 positive edges
07:21:32 Optimization finished
[1] "96 0.17"07:21:32 UMAP embedding parameters a = 1.352 b = 0.9704
07:21:32 Read 1203 rows and found 38 numeric columns
07:21:32 Using Annoy for neighbor search, n_neighbors = 96
07:21:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:21:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a743424
07:21:33 Searching Annoy index using 1 thread, search_k = 9600
07:21:34 Annoy recall = 100%
07:21:40 Commencing smooth kNN distance calibration using 1 thread
07:21:54 Initializing from normalized Laplacian + noise
07:21:54 Commencing optimization for 500 epochs, with 134770 positive edges
07:22:04 Optimization finished
[1] "96 0.18"07:22:04 UMAP embedding parameters a = 1.321 b = 0.9813
07:22:04 Read 1203 rows and found 38 numeric columns
07:22:04 Using Annoy for neighbor search, n_neighbors = 96
07:22:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:22:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c280195
07:22:05 Searching Annoy index using 1 thread, search_k = 9600
07:22:05 Annoy recall = 100%
07:22:12 Commencing smooth kNN distance calibration using 1 thread
07:22:25 Initializing from normalized Laplacian + noise
07:22:25 Commencing optimization for 500 epochs, with 134770 positive edges
07:22:36 Optimization finished
[1] "96 0.19"07:22:36 UMAP embedding parameters a = 1.292 b = 0.9921
07:22:36 Read 1203 rows and found 38 numeric columns
07:22:36 Using Annoy for neighbor search, n_neighbors = 96
07:22:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:22:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e98561b
07:22:36 Searching Annoy index using 1 thread, search_k = 9600
07:22:37 Annoy recall = 100%
07:22:44 Commencing smooth kNN distance calibration using 1 thread
07:22:57 Initializing from normalized Laplacian + noise
07:22:57 Commencing optimization for 500 epochs, with 134770 positive edges
07:23:07 Optimization finished
[1] "96 0.2"07:23:08 UMAP embedding parameters a = 1.262 b = 1.003
07:23:08 Read 1203 rows and found 38 numeric columns
07:23:08 Using Annoy for neighbor search, n_neighbors = 96
07:23:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:23:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751e8283a
07:23:08 Searching Annoy index using 1 thread, search_k = 9600
07:23:09 Annoy recall = 100%
07:23:16 Commencing smooth kNN distance calibration using 1 thread
07:23:29 Initializing from normalized Laplacian + noise
07:23:29 Commencing optimization for 500 epochs, with 134770 positive edges
07:23:39 Optimization finished
[1] "97 0"07:23:39 UMAP embedding parameters a = 1.933 b = 0.7905
07:23:39 Read 1203 rows and found 38 numeric columns
07:23:39 Using Annoy for neighbor search, n_neighbors = 97
07:23:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:23:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f8e30c8
07:23:40 Searching Annoy index using 1 thread, search_k = 9700
07:23:41 Annoy recall = 100%
07:23:47 Commencing smooth kNN distance calibration using 1 thread
07:24:01 Initializing from normalized Laplacian + noise
07:24:01 Commencing optimization for 500 epochs, with 136076 positive edges
07:24:11 Optimization finished
[1] "97 0.01"07:24:11 UMAP embedding parameters a = 1.896 b = 0.8006
07:24:11 Read 1203 rows and found 38 numeric columns
07:24:11 Using Annoy for neighbor search, n_neighbors = 97
07:24:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:24:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716045678
07:24:12 Searching Annoy index using 1 thread, search_k = 9700
07:24:12 Annoy recall = 100%
07:24:19 Commencing smooth kNN distance calibration using 1 thread
07:24:32 Initializing from normalized Laplacian + noise
07:24:32 Commencing optimization for 500 epochs, with 136076 positive edges
07:24:43 Optimization finished
[1] "97 0.02"07:24:43 UMAP embedding parameters a = 1.859 b = 0.8109
07:24:43 Read 1203 rows and found 38 numeric columns
07:24:43 Using Annoy for neighbor search, n_neighbors = 97
07:24:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:24:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87594e44ed
07:24:43 Searching Annoy index using 1 thread, search_k = 9700
07:24:44 Annoy recall = 100%
07:24:51 Commencing smooth kNN distance calibration using 1 thread
07:25:04 Initializing from normalized Laplacian + noise
07:25:04 Commencing optimization for 500 epochs, with 136076 positive edges
07:25:15 Optimization finished
[1] "97 0.03"07:25:15 UMAP embedding parameters a = 1.822 b = 0.8212
07:25:15 Read 1203 rows and found 38 numeric columns
07:25:15 Using Annoy for neighbor search, n_neighbors = 97
07:25:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:25:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c7e23cf
07:25:15 Searching Annoy index using 1 thread, search_k = 9700
07:25:16 Annoy recall = 100%
07:25:23 Commencing smooth kNN distance calibration using 1 thread
07:25:36 Initializing from normalized Laplacian + noise
07:25:36 Commencing optimization for 500 epochs, with 136076 positive edges
07:25:46 Optimization finished
[1] "97 0.04"07:25:47 UMAP embedding parameters a = 1.786 b = 0.8316
07:25:47 Read 1203 rows and found 38 numeric columns
07:25:47 Using Annoy for neighbor search, n_neighbors = 97
07:25:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:25:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b683493
07:25:47 Searching Annoy index using 1 thread, search_k = 9700
07:25:48 Annoy recall = 100%
07:25:54 Commencing smooth kNN distance calibration using 1 thread
07:26:08 Initializing from normalized Laplacian + noise
07:26:08 Commencing optimization for 500 epochs, with 136076 positive edges
07:26:18 Optimization finished
[1] "97 0.05"07:26:19 UMAP embedding parameters a = 1.75 b = 0.8421
07:26:19 Read 1203 rows and found 38 numeric columns
07:26:19 Using Annoy for neighbor search, n_neighbors = 97
07:26:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:26:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873721f9e1
07:26:19 Searching Annoy index using 1 thread, search_k = 9700
07:26:20 Annoy recall = 100%
07:26:26 Commencing smooth kNN distance calibration using 1 thread
07:26:40 Initializing from normalized Laplacian + noise
07:26:40 Commencing optimization for 500 epochs, with 136076 positive edges
07:26:50 Optimization finished
[1] "97 0.06"07:26:50 UMAP embedding parameters a = 1.715 b = 0.8526
07:26:50 Read 1203 rows and found 38 numeric columns
07:26:50 Using Annoy for neighbor search, n_neighbors = 97
07:26:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:26:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bc0fbb9
07:26:51 Searching Annoy index using 1 thread, search_k = 9700
07:26:52 Annoy recall = 100%
07:26:58 Commencing smooth kNN distance calibration using 1 thread
07:27:12 Initializing from normalized Laplacian + noise
07:27:12 Commencing optimization for 500 epochs, with 136076 positive edges
07:27:22 Optimization finished
[1] "97 0.07"07:27:22 UMAP embedding parameters a = 1.68 b = 0.8631
07:27:22 Read 1203 rows and found 38 numeric columns
07:27:22 Using Annoy for neighbor search, n_neighbors = 97
07:27:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:27:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87259df2af
07:27:23 Searching Annoy index using 1 thread, search_k = 9700
07:27:23 Annoy recall = 100%
07:27:30 Commencing smooth kNN distance calibration using 1 thread
07:27:43 Initializing from normalized Laplacian + noise
07:27:43 Commencing optimization for 500 epochs, with 136076 positive edges
07:27:54 Optimization finished
[1] "97 0.08"07:27:54 UMAP embedding parameters a = 1.645 b = 0.8737
07:27:54 Read 1203 rows and found 38 numeric columns
07:27:54 Using Annoy for neighbor search, n_neighbors = 97
07:27:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:27:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87758fb0dd
07:27:55 Searching Annoy index using 1 thread, search_k = 9700
07:27:55 Annoy recall = 100%
07:28:02 Commencing smooth kNN distance calibration using 1 thread
07:28:15 Initializing from normalized Laplacian + noise
07:28:15 Commencing optimization for 500 epochs, with 136076 positive edges
07:28:26 Optimization finished
[1] "97 0.09"07:28:26 UMAP embedding parameters a = 1.611 b = 0.8844
07:28:26 Read 1203 rows and found 38 numeric columns
07:28:26 Using Annoy for neighbor search, n_neighbors = 97
07:28:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:28:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a71f98c
07:28:27 Searching Annoy index using 1 thread, search_k = 9700
07:28:27 Annoy recall = 100%
07:28:34 Commencing smooth kNN distance calibration using 1 thread
07:28:47 Initializing from normalized Laplacian + noise
07:28:47 Commencing optimization for 500 epochs, with 136076 positive edges
07:28:58 Optimization finished
[1] "97 0.1"07:28:58 UMAP embedding parameters a = 1.577 b = 0.8951
07:28:58 Read 1203 rows and found 38 numeric columns
07:28:58 Using Annoy for neighbor search, n_neighbors = 97
07:28:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:28:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a56763e
07:28:58 Searching Annoy index using 1 thread, search_k = 9700
07:28:59 Annoy recall = 100%
07:29:06 Commencing smooth kNN distance calibration using 1 thread
07:29:19 Initializing from normalized Laplacian + noise
07:29:19 Commencing optimization for 500 epochs, with 136076 positive edges
07:29:30 Optimization finished
[1] "97 0.11"07:29:30 UMAP embedding parameters a = 1.544 b = 0.9058
07:29:30 Read 1203 rows and found 38 numeric columns
07:29:30 Using Annoy for neighbor search, n_neighbors = 97
07:29:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:29:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d1f999d
07:29:30 Searching Annoy index using 1 thread, search_k = 9700
07:29:31 Annoy recall = 100%
07:29:38 Commencing smooth kNN distance calibration using 1 thread
07:29:51 Initializing from normalized Laplacian + noise
07:29:51 Commencing optimization for 500 epochs, with 136076 positive edges
07:30:01 Optimization finished
[1] "97 0.12"07:30:02 UMAP embedding parameters a = 1.51 b = 0.9165
07:30:02 Read 1203 rows and found 38 numeric columns
07:30:02 Using Annoy for neighbor search, n_neighbors = 97
07:30:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:30:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729f74a45
07:30:02 Searching Annoy index using 1 thread, search_k = 9700
07:30:03 Annoy recall = 100%
07:30:09 Commencing smooth kNN distance calibration using 1 thread
07:30:23 Initializing from normalized Laplacian + noise
07:30:23 Commencing optimization for 500 epochs, with 136076 positive edges
07:30:33 Optimization finished
[1] "97 0.13"07:30:34 UMAP embedding parameters a = 1.478 b = 0.9272
07:30:34 Read 1203 rows and found 38 numeric columns
07:30:34 Using Annoy for neighbor search, n_neighbors = 97
07:30:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:30:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d62c60b
07:30:34 Searching Annoy index using 1 thread, search_k = 9700
07:30:35 Annoy recall = 100%
07:30:42 Commencing smooth kNN distance calibration using 1 thread
07:30:55 Initializing from normalized Laplacian + noise
07:30:55 Commencing optimization for 500 epochs, with 136076 positive edges
07:31:05 Optimization finished
[1] "97 0.14"07:31:05 UMAP embedding parameters a = 1.446 b = 0.938
07:31:06 Read 1203 rows and found 38 numeric columns
07:31:06 Using Annoy for neighbor search, n_neighbors = 97
07:31:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:31:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877350cff8
07:31:06 Searching Annoy index using 1 thread, search_k = 9700
07:31:07 Annoy recall = 100%
07:31:13 Commencing smooth kNN distance calibration using 1 thread
07:31:27 Initializing from normalized Laplacian + noise
07:31:27 Commencing optimization for 500 epochs, with 136076 positive edges
07:31:37 Optimization finished
[1] "97 0.15"07:31:38 UMAP embedding parameters a = 1.414 b = 0.9488
07:31:38 Read 1203 rows and found 38 numeric columns
07:31:38 Using Annoy for neighbor search, n_neighbors = 97
07:31:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:31:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87409102d8
07:31:38 Searching Annoy index using 1 thread, search_k = 9700
07:31:39 Annoy recall = 100%
07:31:45 Commencing smooth kNN distance calibration using 1 thread
07:31:59 Initializing from normalized Laplacian + noise
07:31:59 Commencing optimization for 500 epochs, with 136076 positive edges
07:32:09 Optimization finished
[1] "97 0.16"07:32:09 UMAP embedding parameters a = 1.383 b = 0.9596
07:32:09 Read 1203 rows and found 38 numeric columns
07:32:09 Using Annoy for neighbor search, n_neighbors = 97
07:32:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:32:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87285101ce
07:32:10 Searching Annoy index using 1 thread, search_k = 9700
07:32:11 Annoy recall = 100%
07:32:17 Commencing smooth kNN distance calibration using 1 thread
07:32:31 Initializing from normalized Laplacian + noise
07:32:31 Commencing optimization for 500 epochs, with 136076 positive edges
07:32:41 Optimization finished
[1] "97 0.17"07:32:42 UMAP embedding parameters a = 1.352 b = 0.9704
07:32:42 Read 1203 rows and found 38 numeric columns
07:32:42 Using Annoy for neighbor search, n_neighbors = 97
07:32:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:32:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87372ec80a
07:32:42 Searching Annoy index using 1 thread, search_k = 9700
07:32:43 Annoy recall = 100%
07:32:49 Commencing smooth kNN distance calibration using 1 thread
07:33:03 Initializing from normalized Laplacian + noise
07:33:03 Commencing optimization for 500 epochs, with 136076 positive edges
07:33:13 Optimization finished
[1] "97 0.18"07:33:13 UMAP embedding parameters a = 1.321 b = 0.9813
07:33:13 Read 1203 rows and found 38 numeric columns
07:33:13 Using Annoy for neighbor search, n_neighbors = 97
07:33:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:33:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87636f5d99
07:33:14 Searching Annoy index using 1 thread, search_k = 9700
07:33:15 Annoy recall = 100%
07:33:21 Commencing smooth kNN distance calibration using 1 thread
07:33:35 Initializing from normalized Laplacian + noise
07:33:35 Commencing optimization for 500 epochs, with 136076 positive edges
07:33:45 Optimization finished
[1] "97 0.19"07:33:46 UMAP embedding parameters a = 1.292 b = 0.9921
07:33:46 Read 1203 rows and found 38 numeric columns
07:33:46 Using Annoy for neighbor search, n_neighbors = 97
07:33:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:33:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769228202
07:33:46 Searching Annoy index using 1 thread, search_k = 9700
07:33:47 Annoy recall = 100%
07:33:53 Commencing smooth kNN distance calibration using 1 thread
07:34:07 Initializing from normalized Laplacian + noise
07:34:07 Commencing optimization for 500 epochs, with 136076 positive edges
07:34:17 Optimization finished
[1] "97 0.2"07:34:17 UMAP embedding parameters a = 1.262 b = 1.003
07:34:17 Read 1203 rows and found 38 numeric columns
07:34:17 Using Annoy for neighbor search, n_neighbors = 97
07:34:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:34:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ecd3ed1
07:34:18 Searching Annoy index using 1 thread, search_k = 9700
07:34:19 Annoy recall = 100%
07:34:25 Commencing smooth kNN distance calibration using 1 thread
07:34:39 Initializing from normalized Laplacian + noise
07:34:39 Commencing optimization for 500 epochs, with 136076 positive edges
07:34:49 Optimization finished
[1] "98 0"07:34:50 UMAP embedding parameters a = 1.933 b = 0.7905
07:34:50 Read 1203 rows and found 38 numeric columns
07:34:50 Using Annoy for neighbor search, n_neighbors = 98
07:34:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:34:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f24c26e
07:34:50 Searching Annoy index using 1 thread, search_k = 9800
07:34:51 Annoy recall = 100%
07:34:59 Commencing smooth kNN distance calibration using 1 thread
07:35:17 Initializing from normalized Laplacian + noise
07:35:17 Commencing optimization for 500 epochs, with 137364 positive edges
07:35:29 Optimization finished
[1] "98 0.01"07:35:29 UMAP embedding parameters a = 1.896 b = 0.8006
07:35:29 Read 1203 rows and found 38 numeric columns
07:35:29 Using Annoy for neighbor search, n_neighbors = 98
07:35:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:35:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717425836
07:35:31 Searching Annoy index using 1 thread, search_k = 9800
07:35:32 Annoy recall = 100%
07:35:39 Commencing smooth kNN distance calibration using 1 thread
07:35:55 Initializing from normalized Laplacian + noise
07:35:55 Commencing optimization for 500 epochs, with 137364 positive edges
07:36:06 Optimization finished
[1] "98 0.02"07:36:07 UMAP embedding parameters a = 1.859 b = 0.8109
07:36:07 Read 1203 rows and found 38 numeric columns
07:36:07 Using Annoy for neighbor search, n_neighbors = 98
07:36:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:36:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87472db4f
07:36:07 Searching Annoy index using 1 thread, search_k = 9800
07:36:08 Annoy recall = 100%
07:36:14 Commencing smooth kNN distance calibration using 1 thread
07:36:28 Initializing from normalized Laplacian + noise
07:36:28 Commencing optimization for 500 epochs, with 137364 positive edges
07:36:39 Optimization finished
[1] "98 0.03"07:36:39 UMAP embedding parameters a = 1.822 b = 0.8212
07:36:39 Read 1203 rows and found 38 numeric columns
07:36:39 Using Annoy for neighbor search, n_neighbors = 98
07:36:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:36:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c314e6
07:36:40 Searching Annoy index using 1 thread, search_k = 9800
07:36:40 Annoy recall = 100%
07:36:47 Commencing smooth kNN distance calibration using 1 thread
07:37:00 Initializing from normalized Laplacian + noise
07:37:00 Commencing optimization for 500 epochs, with 137364 positive edges
07:37:11 Optimization finished
[1] "98 0.04"07:37:11 UMAP embedding parameters a = 1.786 b = 0.8316
07:37:11 Read 1203 rows and found 38 numeric columns
07:37:11 Using Annoy for neighbor search, n_neighbors = 98
07:37:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:37:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87741c5cb1
07:37:12 Searching Annoy index using 1 thread, search_k = 9800
07:37:12 Annoy recall = 100%
07:37:19 Commencing smooth kNN distance calibration using 1 thread
07:37:32 Initializing from normalized Laplacian + noise
07:37:33 Commencing optimization for 500 epochs, with 137364 positive edges
07:37:43 Optimization finished
[1] "98 0.05"07:37:43 UMAP embedding parameters a = 1.75 b = 0.8421
07:37:43 Read 1203 rows and found 38 numeric columns
07:37:44 Using Annoy for neighbor search, n_neighbors = 98
07:37:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:37:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773587fd8
07:37:44 Searching Annoy index using 1 thread, search_k = 9800
07:37:45 Annoy recall = 100%
07:37:51 Commencing smooth kNN distance calibration using 1 thread
07:38:05 Initializing from normalized Laplacian + noise
07:38:05 Commencing optimization for 500 epochs, with 137364 positive edges
07:38:15 Optimization finished
[1] "98 0.06"07:38:16 UMAP embedding parameters a = 1.715 b = 0.8526
07:38:16 Read 1203 rows and found 38 numeric columns
07:38:16 Using Annoy for neighbor search, n_neighbors = 98
07:38:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:38:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b37490b
07:38:16 Searching Annoy index using 1 thread, search_k = 9800
07:38:17 Annoy recall = 100%
07:38:23 Commencing smooth kNN distance calibration using 1 thread
07:38:37 Initializing from normalized Laplacian + noise
07:38:37 Commencing optimization for 500 epochs, with 137364 positive edges
07:38:47 Optimization finished
[1] "98 0.07"07:38:48 UMAP embedding parameters a = 1.68 b = 0.8631
07:38:48 Read 1203 rows and found 38 numeric columns
07:38:48 Using Annoy for neighbor search, n_neighbors = 98
07:38:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:38:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87445e46
07:38:48 Searching Annoy index using 1 thread, search_k = 9800
07:38:49 Annoy recall = 100%
07:38:55 Commencing smooth kNN distance calibration using 1 thread
07:39:09 Initializing from normalized Laplacian + noise
07:39:09 Commencing optimization for 500 epochs, with 137364 positive edges
07:39:19 Optimization finished
[1] "98 0.08"07:39:20 UMAP embedding parameters a = 1.645 b = 0.8737
07:39:20 Read 1203 rows and found 38 numeric columns
07:39:20 Using Annoy for neighbor search, n_neighbors = 98
07:39:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:39:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751f0d5f3
07:39:20 Searching Annoy index using 1 thread, search_k = 9800
07:39:21 Annoy recall = 100%
07:39:28 Commencing smooth kNN distance calibration using 1 thread
07:39:41 Initializing from normalized Laplacian + noise
07:39:41 Commencing optimization for 500 epochs, with 137364 positive edges
07:39:51 Optimization finished
[1] "98 0.09"07:39:52 UMAP embedding parameters a = 1.611 b = 0.8844
07:39:52 Read 1203 rows and found 38 numeric columns
07:39:52 Using Annoy for neighbor search, n_neighbors = 98
07:39:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:39:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d1f7145
07:39:52 Searching Annoy index using 1 thread, search_k = 9800
07:39:53 Annoy recall = 100%
07:40:00 Commencing smooth kNN distance calibration using 1 thread
07:40:13 Initializing from normalized Laplacian + noise
07:40:13 Commencing optimization for 500 epochs, with 137364 positive edges
07:40:24 Optimization finished
[1] "98 0.1"07:40:24 UMAP embedding parameters a = 1.577 b = 0.8951
07:40:24 Read 1203 rows and found 38 numeric columns
07:40:24 Using Annoy for neighbor search, n_neighbors = 98
07:40:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:40:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fd28f0e
07:40:24 Searching Annoy index using 1 thread, search_k = 9800
07:40:25 Annoy recall = 100%
07:40:32 Commencing smooth kNN distance calibration using 1 thread
07:40:45 Initializing from normalized Laplacian + noise
07:40:45 Commencing optimization for 500 epochs, with 137364 positive edges
07:40:56 Optimization finished
[1] "98 0.11"07:40:56 UMAP embedding parameters a = 1.544 b = 0.9058
07:40:56 Read 1203 rows and found 38 numeric columns
07:40:56 Using Annoy for neighbor search, n_neighbors = 98
07:40:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:40:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767f52c6b
07:40:56 Searching Annoy index using 1 thread, search_k = 9800
07:40:57 Annoy recall = 100%
07:41:04 Commencing smooth kNN distance calibration using 1 thread
07:41:17 Initializing from normalized Laplacian + noise
07:41:17 Commencing optimization for 500 epochs, with 137364 positive edges
07:41:28 Optimization finished
[1] "98 0.12"07:41:28 UMAP embedding parameters a = 1.51 b = 0.9165
07:41:28 Read 1203 rows and found 38 numeric columns
07:41:28 Using Annoy for neighbor search, n_neighbors = 98
07:41:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:41:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87566db632
07:41:28 Searching Annoy index using 1 thread, search_k = 9800
07:41:29 Annoy recall = 100%
07:41:36 Commencing smooth kNN distance calibration using 1 thread
07:41:49 Initializing from normalized Laplacian + noise
07:41:49 Commencing optimization for 500 epochs, with 137364 positive edges
07:42:00 Optimization finished
[1] "98 0.13"07:42:00 UMAP embedding parameters a = 1.478 b = 0.9272
07:42:00 Read 1203 rows and found 38 numeric columns
07:42:00 Using Annoy for neighbor search, n_neighbors = 98
07:42:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:42:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c50b2de
07:42:00 Searching Annoy index using 1 thread, search_k = 9800
07:42:01 Annoy recall = 100%
07:42:08 Commencing smooth kNN distance calibration using 1 thread
07:42:21 Initializing from normalized Laplacian + noise
07:42:21 Commencing optimization for 500 epochs, with 137364 positive edges
07:42:32 Optimization finished
[1] "98 0.14"07:42:32 UMAP embedding parameters a = 1.446 b = 0.938
07:42:32 Read 1203 rows and found 38 numeric columns
07:42:32 Using Annoy for neighbor search, n_neighbors = 98
07:42:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:42:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87435d60ff
07:42:32 Searching Annoy index using 1 thread, search_k = 9800
07:42:33 Annoy recall = 100%
07:42:40 Commencing smooth kNN distance calibration using 1 thread
07:42:53 Initializing from normalized Laplacian + noise
07:42:53 Commencing optimization for 500 epochs, with 137364 positive edges
07:43:04 Optimization finished
[1] "98 0.15"07:43:04 UMAP embedding parameters a = 1.414 b = 0.9488
07:43:04 Read 1203 rows and found 38 numeric columns
07:43:04 Using Annoy for neighbor search, n_neighbors = 98
07:43:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:43:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d8fb014
07:43:04 Searching Annoy index using 1 thread, search_k = 9800
07:43:05 Annoy recall = 100%
07:43:12 Commencing smooth kNN distance calibration using 1 thread
07:43:25 Initializing from normalized Laplacian + noise
07:43:25 Commencing optimization for 500 epochs, with 137364 positive edges
07:43:36 Optimization finished
[1] "98 0.16"07:43:36 UMAP embedding parameters a = 1.383 b = 0.9596
07:43:36 Read 1203 rows and found 38 numeric columns
07:43:36 Using Annoy for neighbor search, n_neighbors = 98
07:43:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:43:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875811ae97
07:43:37 Searching Annoy index using 1 thread, search_k = 9800
07:43:37 Annoy recall = 100%
07:43:44 Commencing smooth kNN distance calibration using 1 thread
07:43:57 Initializing from normalized Laplacian + noise
07:43:57 Commencing optimization for 500 epochs, with 137364 positive edges
07:44:08 Optimization finished
[1] "98 0.17"07:44:08 UMAP embedding parameters a = 1.352 b = 0.9704
07:44:08 Read 1203 rows and found 38 numeric columns
07:44:08 Using Annoy for neighbor search, n_neighbors = 98
07:44:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:44:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768fb53ae
07:44:09 Searching Annoy index using 1 thread, search_k = 9800
07:44:09 Annoy recall = 100%
07:44:16 Commencing smooth kNN distance calibration using 1 thread
07:44:29 Initializing from normalized Laplacian + noise
07:44:29 Commencing optimization for 500 epochs, with 137364 positive edges
07:44:40 Optimization finished
[1] "98 0.18"07:44:40 UMAP embedding parameters a = 1.321 b = 0.9813
07:44:40 Read 1203 rows and found 38 numeric columns
07:44:40 Using Annoy for neighbor search, n_neighbors = 98
07:44:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:44:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731f60f1
07:44:40 Searching Annoy index using 1 thread, search_k = 9800
07:44:41 Annoy recall = 100%
07:44:48 Commencing smooth kNN distance calibration using 1 thread
07:45:02 Initializing from normalized Laplacian + noise
07:45:02 Commencing optimization for 500 epochs, with 137364 positive edges
07:45:13 Optimization finished
[1] "98 0.19"07:45:13 UMAP embedding parameters a = 1.292 b = 0.9921
07:45:13 Read 1203 rows and found 38 numeric columns
07:45:13 Using Annoy for neighbor search, n_neighbors = 98
07:45:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:45:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877283a823
07:45:15 Searching Annoy index using 1 thread, search_k = 9800
07:45:16 Annoy recall = 100%
07:45:23 Commencing smooth kNN distance calibration using 1 thread
07:45:37 Initializing from normalized Laplacian + noise
07:45:37 Commencing optimization for 500 epochs, with 137364 positive edges
07:45:48 Optimization finished
[1] "98 0.2"07:45:48 UMAP embedding parameters a = 1.262 b = 1.003
07:45:48 Read 1203 rows and found 38 numeric columns
07:45:48 Using Annoy for neighbor search, n_neighbors = 98
07:45:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:45:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877351c9ec
07:45:49 Searching Annoy index using 1 thread, search_k = 9800
07:45:50 Annoy recall = 100%
07:45:56 Commencing smooth kNN distance calibration using 1 thread
07:46:09 Initializing from normalized Laplacian + noise
07:46:09 Commencing optimization for 500 epochs, with 137364 positive edges
07:46:20 Optimization finished
[1] "99 0"07:46:20 UMAP embedding parameters a = 1.933 b = 0.7905
07:46:20 Read 1203 rows and found 38 numeric columns
07:46:20 Using Annoy for neighbor search, n_neighbors = 99
07:46:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:46:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87703efa8e
07:46:21 Searching Annoy index using 1 thread, search_k = 9900
07:46:21 Annoy recall = 100%
07:46:28 Commencing smooth kNN distance calibration using 1 thread
07:46:41 Initializing from normalized Laplacian + noise
07:46:41 Commencing optimization for 500 epochs, with 138630 positive edges
07:46:52 Optimization finished
[1] "99 0.01"07:46:52 UMAP embedding parameters a = 1.896 b = 0.8006
07:46:52 Read 1203 rows and found 38 numeric columns
07:46:52 Using Annoy for neighbor search, n_neighbors = 99
07:46:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:46:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c7af269
07:46:53 Searching Annoy index using 1 thread, search_k = 9900
07:46:53 Annoy recall = 100%
07:47:00 Commencing smooth kNN distance calibration using 1 thread
07:47:13 Initializing from normalized Laplacian + noise
07:47:13 Commencing optimization for 500 epochs, with 138630 positive edges
07:47:24 Optimization finished
[1] "99 0.02"07:47:24 UMAP embedding parameters a = 1.859 b = 0.8109
07:47:24 Read 1203 rows and found 38 numeric columns
07:47:24 Using Annoy for neighbor search, n_neighbors = 99
07:47:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:47:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760b48ff8
07:47:24 Searching Annoy index using 1 thread, search_k = 9900
07:47:25 Annoy recall = 100%
07:47:32 Commencing smooth kNN distance calibration using 1 thread
07:47:45 Initializing from normalized Laplacian + noise
07:47:45 Commencing optimization for 500 epochs, with 138630 positive edges
07:47:56 Optimization finished
[1] "99 0.03"07:47:56 UMAP embedding parameters a = 1.822 b = 0.8212
07:47:56 Read 1203 rows and found 38 numeric columns
07:47:56 Using Annoy for neighbor search, n_neighbors = 99
07:47:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:47:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87638fca86
07:47:56 Searching Annoy index using 1 thread, search_k = 9900
07:47:57 Annoy recall = 100%
07:48:04 Commencing smooth kNN distance calibration using 1 thread
07:48:17 Initializing from normalized Laplacian + noise
07:48:17 Commencing optimization for 500 epochs, with 138630 positive edges
07:48:27 Optimization finished
[1] "99 0.04"07:48:27 UMAP embedding parameters a = 1.786 b = 0.8316
07:48:28 Read 1203 rows and found 38 numeric columns
07:48:28 Using Annoy for neighbor search, n_neighbors = 99
07:48:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:48:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d0bf541
07:48:28 Searching Annoy index using 1 thread, search_k = 9900
07:48:29 Annoy recall = 100%
07:48:35 Commencing smooth kNN distance calibration using 1 thread
07:48:49 Initializing from normalized Laplacian + noise
07:48:49 Commencing optimization for 500 epochs, with 138630 positive edges
07:48:59 Optimization finished
[1] "99 0.05"07:49:00 UMAP embedding parameters a = 1.75 b = 0.8421
07:49:00 Read 1203 rows and found 38 numeric columns
07:49:00 Using Annoy for neighbor search, n_neighbors = 99
07:49:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:49:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8790591c6
07:49:00 Searching Annoy index using 1 thread, search_k = 9900
07:49:01 Annoy recall = 100%
07:49:07 Commencing smooth kNN distance calibration using 1 thread
07:49:21 Initializing from normalized Laplacian + noise
07:49:21 Commencing optimization for 500 epochs, with 138630 positive edges
07:49:31 Optimization finished
[1] "99 0.06"07:49:31 UMAP embedding parameters a = 1.715 b = 0.8526
07:49:31 Read 1203 rows and found 38 numeric columns
07:49:31 Using Annoy for neighbor search, n_neighbors = 99
07:49:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:49:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871abe9290
07:49:32 Searching Annoy index using 1 thread, search_k = 9900
07:49:33 Annoy recall = 100%
07:49:39 Commencing smooth kNN distance calibration using 1 thread
07:49:53 Initializing from normalized Laplacian + noise
07:49:53 Commencing optimization for 500 epochs, with 138630 positive edges
07:50:03 Optimization finished
[1] "99 0.07"07:50:04 UMAP embedding parameters a = 1.68 b = 0.8631
07:50:04 Read 1203 rows and found 38 numeric columns
07:50:04 Using Annoy for neighbor search, n_neighbors = 99
07:50:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:50:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87407b52da
07:50:04 Searching Annoy index using 1 thread, search_k = 9900
07:50:05 Annoy recall = 100%
07:50:11 Commencing smooth kNN distance calibration using 1 thread
07:50:25 Initializing from normalized Laplacian + noise
07:50:25 Commencing optimization for 500 epochs, with 138630 positive edges
07:50:35 Optimization finished
[1] "99 0.08"07:50:36 UMAP embedding parameters a = 1.645 b = 0.8737
07:50:36 Read 1203 rows and found 38 numeric columns
07:50:36 Using Annoy for neighbor search, n_neighbors = 99
07:50:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:50:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87722813c9
07:50:36 Searching Annoy index using 1 thread, search_k = 9900
07:50:37 Annoy recall = 100%
07:50:44 Commencing smooth kNN distance calibration using 1 thread
07:50:57 Initializing from normalized Laplacian + noise
07:50:57 Commencing optimization for 500 epochs, with 138630 positive edges
07:51:08 Optimization finished
[1] "99 0.09"07:51:08 UMAP embedding parameters a = 1.611 b = 0.8844
07:51:08 Read 1203 rows and found 38 numeric columns
07:51:08 Using Annoy for neighbor search, n_neighbors = 99
07:51:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:51:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87398bd161
07:51:08 Searching Annoy index using 1 thread, search_k = 9900
07:51:09 Annoy recall = 100%
07:51:16 Commencing smooth kNN distance calibration using 1 thread
07:51:29 Initializing from normalized Laplacian + noise
07:51:29 Commencing optimization for 500 epochs, with 138630 positive edges
07:51:40 Optimization finished
[1] "99 0.1"07:51:40 UMAP embedding parameters a = 1.577 b = 0.8951
07:51:40 Read 1203 rows and found 38 numeric columns
07:51:40 Using Annoy for neighbor search, n_neighbors = 99
07:51:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:51:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fa01549
07:51:40 Searching Annoy index using 1 thread, search_k = 9900
07:51:41 Annoy recall = 100%
07:51:48 Commencing smooth kNN distance calibration using 1 thread
07:52:01 Initializing from normalized Laplacian + noise
07:52:01 Commencing optimization for 500 epochs, with 138630 positive edges
07:52:12 Optimization finished
[1] "99 0.11"07:52:12 UMAP embedding parameters a = 1.544 b = 0.9058
07:52:12 Read 1203 rows and found 38 numeric columns
07:52:12 Using Annoy for neighbor search, n_neighbors = 99
07:52:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:52:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8796a6bff
07:52:13 Searching Annoy index using 1 thread, search_k = 9900
07:52:13 Annoy recall = 100%
07:52:20 Commencing smooth kNN distance calibration using 1 thread
07:52:33 Initializing from normalized Laplacian + noise
07:52:33 Commencing optimization for 500 epochs, with 138630 positive edges
07:52:44 Optimization finished
[1] "99 0.12"07:52:44 UMAP embedding parameters a = 1.51 b = 0.9165
07:52:44 Read 1203 rows and found 38 numeric columns
07:52:44 Using Annoy for neighbor search, n_neighbors = 99
07:52:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:52:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873dfeacb1
07:52:45 Searching Annoy index using 1 thread, search_k = 9900
07:52:45 Annoy recall = 100%
07:52:52 Commencing smooth kNN distance calibration using 1 thread
07:53:06 Initializing from normalized Laplacian + noise
07:53:06 Commencing optimization for 500 epochs, with 138630 positive edges
07:53:16 Optimization finished
[1] "99 0.13"07:53:16 UMAP embedding parameters a = 1.478 b = 0.9272
07:53:16 Read 1203 rows and found 38 numeric columns
07:53:16 Using Annoy for neighbor search, n_neighbors = 99
07:53:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:53:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730632a2f
07:53:17 Searching Annoy index using 1 thread, search_k = 9900
07:53:18 Annoy recall = 100%
07:53:24 Commencing smooth kNN distance calibration using 1 thread
07:53:38 Initializing from normalized Laplacian + noise
07:53:38 Commencing optimization for 500 epochs, with 138630 positive edges
07:53:48 Optimization finished
[1] "99 0.14"07:53:49 UMAP embedding parameters a = 1.446 b = 0.938
07:53:49 Read 1203 rows and found 38 numeric columns
07:53:49 Using Annoy for neighbor search, n_neighbors = 99
07:53:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:53:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d86c8b0
07:53:49 Searching Annoy index using 1 thread, search_k = 9900
07:53:50 Annoy recall = 100%
07:53:56 Commencing smooth kNN distance calibration using 1 thread
07:54:10 Initializing from normalized Laplacian + noise
07:54:10 Commencing optimization for 500 epochs, with 138630 positive edges
07:54:21 Optimization finished
[1] "99 0.15"07:54:21 UMAP embedding parameters a = 1.414 b = 0.9488
07:54:21 Read 1203 rows and found 38 numeric columns
07:54:21 Using Annoy for neighbor search, n_neighbors = 99
07:54:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:54:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731572c89
07:54:21 Searching Annoy index using 1 thread, search_k = 9900
07:54:22 Annoy recall = 100%
07:54:29 Commencing smooth kNN distance calibration using 1 thread
07:54:42 Initializing from normalized Laplacian + noise
07:54:42 Commencing optimization for 500 epochs, with 138630 positive edges
07:54:53 Optimization finished
[1] "99 0.16"07:54:53 UMAP embedding parameters a = 1.383 b = 0.9596
07:54:53 Read 1203 rows and found 38 numeric columns
07:54:53 Using Annoy for neighbor search, n_neighbors = 99
07:54:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:54:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b9a733a
07:54:53 Searching Annoy index using 1 thread, search_k = 9900
07:54:54 Annoy recall = 100%
07:55:01 Commencing smooth kNN distance calibration using 1 thread
07:55:14 Initializing from normalized Laplacian + noise
07:55:14 Commencing optimization for 500 epochs, with 138630 positive edges
07:55:25 Optimization finished
[1] "99 0.17"07:55:25 UMAP embedding parameters a = 1.352 b = 0.9704
07:55:25 Read 1203 rows and found 38 numeric columns
07:55:25 Using Annoy for neighbor search, n_neighbors = 99
07:55:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:55:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877dcb26f7
07:55:26 Searching Annoy index using 1 thread, search_k = 9900
07:55:26 Annoy recall = 100%
07:55:33 Commencing smooth kNN distance calibration using 1 thread
07:55:46 Initializing from normalized Laplacian + noise
07:55:47 Commencing optimization for 500 epochs, with 138630 positive edges
07:55:57 Optimization finished
[1] "99 0.18"07:55:57 UMAP embedding parameters a = 1.321 b = 0.9813
07:55:57 Read 1203 rows and found 38 numeric columns
07:55:57 Using Annoy for neighbor search, n_neighbors = 99
07:55:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:55:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87348027c
07:55:58 Searching Annoy index using 1 thread, search_k = 9900
07:55:59 Annoy recall = 100%
07:56:05 Commencing smooth kNN distance calibration using 1 thread
07:56:19 Initializing from normalized Laplacian + noise
07:56:19 Commencing optimization for 500 epochs, with 138630 positive edges
07:56:29 Optimization finished
[1] "99 0.19"07:56:29 UMAP embedding parameters a = 1.292 b = 0.9921
07:56:30 Read 1203 rows and found 38 numeric columns
07:56:30 Using Annoy for neighbor search, n_neighbors = 99
07:56:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:56:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758b9e480
07:56:30 Searching Annoy index using 1 thread, search_k = 9900
07:56:31 Annoy recall = 100%
07:56:37 Commencing smooth kNN distance calibration using 1 thread
07:56:51 Initializing from normalized Laplacian + noise
07:56:51 Commencing optimization for 500 epochs, with 138630 positive edges
07:57:01 Optimization finished
[1] "99 0.2"07:57:02 UMAP embedding parameters a = 1.262 b = 1.003
07:57:02 Read 1203 rows and found 38 numeric columns
07:57:02 Using Annoy for neighbor search, n_neighbors = 99
07:57:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:57:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d9db605
07:57:02 Searching Annoy index using 1 thread, search_k = 9900
07:57:03 Annoy recall = 100%
07:57:10 Commencing smooth kNN distance calibration using 1 thread
07:57:23 Initializing from normalized Laplacian + noise
07:57:23 Commencing optimization for 500 epochs, with 138630 positive edges
07:57:34 Optimization finished
[1] "100 0"07:57:34 UMAP embedding parameters a = 1.933 b = 0.7905
07:57:34 Read 1203 rows and found 38 numeric columns
07:57:34 Using Annoy for neighbor search, n_neighbors = 100
07:57:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:57:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b3d2ee8
07:57:34 Searching Annoy index using 1 thread, search_k = 10000
07:57:35 Annoy recall = 100%
07:57:42 Commencing smooth kNN distance calibration using 1 thread
07:57:55 Initializing from normalized Laplacian + noise
07:57:56 Commencing optimization for 500 epochs, with 139892 positive edges
07:58:06 Optimization finished
[1] "100 0.01"07:58:06 UMAP embedding parameters a = 1.896 b = 0.8006
07:58:06 Read 1203 rows and found 38 numeric columns
07:58:06 Using Annoy for neighbor search, n_neighbors = 100
07:58:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:58:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f279ab2
07:58:07 Searching Annoy index using 1 thread, search_k = 10000
07:58:08 Annoy recall = 100%
07:58:14 Commencing smooth kNN distance calibration using 1 thread
07:58:28 Initializing from normalized Laplacian + noise
07:58:28 Commencing optimization for 500 epochs, with 139892 positive edges
07:58:38 Optimization finished
[1] "100 0.02"07:58:39 UMAP embedding parameters a = 1.859 b = 0.8109
07:58:39 Read 1203 rows and found 38 numeric columns
07:58:39 Using Annoy for neighbor search, n_neighbors = 100
07:58:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:58:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739ee68e3
07:58:39 Searching Annoy index using 1 thread, search_k = 10000
07:58:40 Annoy recall = 100%
07:58:47 Commencing smooth kNN distance calibration using 1 thread
07:59:00 Initializing from normalized Laplacian + noise
07:59:00 Commencing optimization for 500 epochs, with 139892 positive edges
07:59:11 Optimization finished
[1] "100 0.03"07:59:11 UMAP embedding parameters a = 1.822 b = 0.8212
07:59:11 Read 1203 rows and found 38 numeric columns
07:59:11 Using Annoy for neighbor search, n_neighbors = 100
07:59:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:59:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e9a8fe7
07:59:11 Searching Annoy index using 1 thread, search_k = 10000
07:59:12 Annoy recall = 100%
07:59:19 Commencing smooth kNN distance calibration using 1 thread
07:59:32 Initializing from normalized Laplacian + noise
07:59:33 Commencing optimization for 500 epochs, with 139892 positive edges
07:59:43 Optimization finished
[1] "100 0.04"07:59:43 UMAP embedding parameters a = 1.786 b = 0.8316
07:59:43 Read 1203 rows and found 38 numeric columns
07:59:43 Using Annoy for neighbor search, n_neighbors = 100
07:59:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:59:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cb74ac6
07:59:44 Searching Annoy index using 1 thread, search_k = 10000
07:59:44 Annoy recall = 100%
07:59:51 Commencing smooth kNN distance calibration using 1 thread
08:00:05 Initializing from normalized Laplacian + noise
08:00:05 Commencing optimization for 500 epochs, with 139892 positive edges
08:00:15 Optimization finished
[1] "100 0.05"08:00:16 UMAP embedding parameters a = 1.75 b = 0.8421
08:00:16 Read 1203 rows and found 38 numeric columns
08:00:16 Using Annoy for neighbor search, n_neighbors = 100
08:00:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:00:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871200177a
08:00:16 Searching Annoy index using 1 thread, search_k = 10000
08:00:17 Annoy recall = 100%
08:00:24 Commencing smooth kNN distance calibration using 1 thread
08:00:37 Initializing from normalized Laplacian + noise
08:00:37 Commencing optimization for 500 epochs, with 139892 positive edges
08:00:48 Optimization finished
[1] "100 0.06"08:00:48 UMAP embedding parameters a = 1.715 b = 0.8526
08:00:48 Read 1203 rows and found 38 numeric columns
08:00:48 Using Annoy for neighbor search, n_neighbors = 100
08:00:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:00:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871795e395
08:00:48 Searching Annoy index using 1 thread, search_k = 10000
08:00:49 Annoy recall = 100%
08:00:56 Commencing smooth kNN distance calibration using 1 thread
08:01:09 Initializing from normalized Laplacian + noise
08:01:10 Commencing optimization for 500 epochs, with 139892 positive edges
08:01:20 Optimization finished
[1] "100 0.07"08:01:20 UMAP embedding parameters a = 1.68 b = 0.8631
08:01:20 Read 1203 rows and found 38 numeric columns
08:01:20 Using Annoy for neighbor search, n_neighbors = 100
08:01:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:01:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fd6abb7
08:01:21 Searching Annoy index using 1 thread, search_k = 10000
08:01:22 Annoy recall = 100%
08:01:28 Commencing smooth kNN distance calibration using 1 thread
08:01:42 Initializing from normalized Laplacian + noise
08:01:42 Commencing optimization for 500 epochs, with 139892 positive edges
08:01:52 Optimization finished
[1] "100 0.08"08:01:53 UMAP embedding parameters a = 1.645 b = 0.8737
08:01:53 Read 1203 rows and found 38 numeric columns
08:01:53 Using Annoy for neighbor search, n_neighbors = 100
08:01:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:01:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87483bf9e
08:01:53 Searching Annoy index using 1 thread, search_k = 10000
08:01:54 Annoy recall = 100%
08:02:01 Commencing smooth kNN distance calibration using 1 thread
08:02:14 Initializing from normalized Laplacian + noise
08:02:14 Commencing optimization for 500 epochs, with 139892 positive edges
08:02:25 Optimization finished
[1] "100 0.09"08:02:25 UMAP embedding parameters a = 1.611 b = 0.8844
08:02:25 Read 1203 rows and found 38 numeric columns
08:02:25 Using Annoy for neighbor search, n_neighbors = 100
08:02:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:02:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ae7ad82
08:02:26 Searching Annoy index using 1 thread, search_k = 10000
08:02:26 Annoy recall = 100%
08:02:33 Commencing smooth kNN distance calibration using 1 thread
08:02:47 Initializing from normalized Laplacian + noise
08:02:47 Commencing optimization for 500 epochs, with 139892 positive edges
08:02:57 Optimization finished
[1] "100 0.1"08:02:57 UMAP embedding parameters a = 1.577 b = 0.8951
08:02:57 Read 1203 rows and found 38 numeric columns
08:02:57 Using Annoy for neighbor search, n_neighbors = 100
08:02:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:02:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873015a645
08:02:58 Searching Annoy index using 1 thread, search_k = 10000
08:02:59 Annoy recall = 100%
08:03:05 Commencing smooth kNN distance calibration using 1 thread
08:03:19 Initializing from normalized Laplacian + noise
08:03:19 Commencing optimization for 500 epochs, with 139892 positive edges
08:03:30 Optimization finished
[1] "100 0.11"08:03:30 UMAP embedding parameters a = 1.544 b = 0.9058
08:03:30 Read 1203 rows and found 38 numeric columns
08:03:30 Using Annoy for neighbor search, n_neighbors = 100
08:03:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:03:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720feb207
08:03:30 Searching Annoy index using 1 thread, search_k = 10000
08:03:31 Annoy recall = 100%
08:03:38 Commencing smooth kNN distance calibration using 1 thread
08:03:51 Initializing from normalized Laplacian + noise
08:03:52 Commencing optimization for 500 epochs, with 139892 positive edges
08:04:02 Optimization finished
[1] "100 0.12"08:04:02 UMAP embedding parameters a = 1.51 b = 0.9165
08:04:02 Read 1203 rows and found 38 numeric columns
08:04:02 Using Annoy for neighbor search, n_neighbors = 100
08:04:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:04:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b9c3d7a
08:04:03 Searching Annoy index using 1 thread, search_k = 10000
08:04:03 Annoy recall = 100%
08:04:10 Commencing smooth kNN distance calibration using 1 thread
08:04:24 Initializing from normalized Laplacian + noise
08:04:24 Commencing optimization for 500 epochs, with 139892 positive edges
08:04:35 Optimization finished
[1] "100 0.13"08:04:35 UMAP embedding parameters a = 1.478 b = 0.9272
08:04:35 Read 1203 rows and found 38 numeric columns
08:04:35 Using Annoy for neighbor search, n_neighbors = 100
08:04:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:04:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713a570cb
08:04:35 Searching Annoy index using 1 thread, search_k = 10000
08:04:36 Annoy recall = 100%
08:04:43 Commencing smooth kNN distance calibration using 1 thread
08:04:56 Initializing from normalized Laplacian + noise
08:04:56 Commencing optimization for 500 epochs, with 139892 positive edges
08:05:07 Optimization finished
[1] "100 0.14"08:05:07 UMAP embedding parameters a = 1.446 b = 0.938
08:05:07 Read 1203 rows and found 38 numeric columns
08:05:07 Using Annoy for neighbor search, n_neighbors = 100
08:05:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:05:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e0aa748
08:05:08 Searching Annoy index using 1 thread, search_k = 10000
08:05:08 Annoy recall = 100%
08:05:15 Commencing smooth kNN distance calibration using 1 thread
08:05:29 Initializing from normalized Laplacian + noise
08:05:29 Commencing optimization for 500 epochs, with 139892 positive edges
08:05:39 Optimization finished
[1] "100 0.15"08:05:40 UMAP embedding parameters a = 1.414 b = 0.9488
08:05:40 Read 1203 rows and found 38 numeric columns
08:05:40 Using Annoy for neighbor search, n_neighbors = 100
08:05:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:05:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774a1cf40
08:05:40 Searching Annoy index using 1 thread, search_k = 10000
08:05:41 Annoy recall = 100%
08:05:48 Commencing smooth kNN distance calibration using 1 thread
08:06:01 Initializing from normalized Laplacian + noise
08:06:01 Commencing optimization for 500 epochs, with 139892 positive edges
08:06:12 Optimization finished
[1] "100 0.16"08:06:12 UMAP embedding parameters a = 1.383 b = 0.9596
08:06:12 Read 1203 rows and found 38 numeric columns
08:06:12 Using Annoy for neighbor search, n_neighbors = 100
08:06:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:06:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e64035c
08:06:12 Searching Annoy index using 1 thread, search_k = 10000
08:06:13 Annoy recall = 100%
08:06:20 Commencing smooth kNN distance calibration using 1 thread
08:06:33 Initializing from normalized Laplacian + noise
08:06:34 Commencing optimization for 500 epochs, with 139892 positive edges
08:06:44 Optimization finished
[1] "100 0.17"08:06:44 UMAP embedding parameters a = 1.352 b = 0.9704
08:06:44 Read 1203 rows and found 38 numeric columns
08:06:44 Using Annoy for neighbor search, n_neighbors = 100
08:06:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:06:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e85fa23
08:06:45 Searching Annoy index using 1 thread, search_k = 10000
08:06:46 Annoy recall = 100%
08:06:52 Commencing smooth kNN distance calibration using 1 thread
08:07:06 Initializing from normalized Laplacian + noise
08:07:06 Commencing optimization for 500 epochs, with 139892 positive edges
08:07:16 Optimization finished
[1] "100 0.18"08:07:17 UMAP embedding parameters a = 1.321 b = 0.9813
08:07:17 Read 1203 rows and found 38 numeric columns
08:07:17 Using Annoy for neighbor search, n_neighbors = 100
08:07:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:07:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766c9e309
08:07:17 Searching Annoy index using 1 thread, search_k = 10000
08:07:18 Annoy recall = 100%
08:07:25 Commencing smooth kNN distance calibration using 1 thread
08:07:38 Initializing from normalized Laplacian + noise
08:07:38 Commencing optimization for 500 epochs, with 139892 positive edges
08:07:49 Optimization finished
[1] "100 0.19"08:07:49 UMAP embedding parameters a = 1.292 b = 0.9921
08:07:49 Read 1203 rows and found 38 numeric columns
08:07:49 Using Annoy for neighbor search, n_neighbors = 100
08:07:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:07:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767efd4bd
08:07:50 Searching Annoy index using 1 thread, search_k = 10000
08:07:50 Annoy recall = 100%
08:07:57 Commencing smooth kNN distance calibration using 1 thread
08:08:11 Initializing from normalized Laplacian + noise
08:08:11 Commencing optimization for 500 epochs, with 139892 positive edges
08:08:21 Optimization finished
[1] "100 0.2"08:08:21 UMAP embedding parameters a = 1.262 b = 1.003
08:08:21 Read 1203 rows and found 38 numeric columns
08:08:21 Using Annoy for neighbor search, n_neighbors = 100
08:08:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:08:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e260f6c
08:08:22 Searching Annoy index using 1 thread, search_k = 10000
08:08:23 Annoy recall = 100%
08:08:30 Commencing smooth kNN distance calibration using 1 thread
08:08:43 Initializing from normalized Laplacian + noise
08:08:43 Commencing optimization for 500 epochs, with 139892 positive edges
08:08:54 Optimization finished
[1] "101 0"08:08:54 UMAP embedding parameters a = 1.933 b = 0.7905
08:08:54 Read 1203 rows and found 38 numeric columns
08:08:54 Using Annoy for neighbor search, n_neighbors = 101
08:08:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:08:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770344f08
08:08:55 Searching Annoy index using 1 thread, search_k = 10100
08:08:55 Annoy recall = 100%
08:09:02 Commencing smooth kNN distance calibration using 1 thread
08:09:16 Initializing from normalized Laplacian + noise
08:09:16 Commencing optimization for 500 epochs, with 141212 positive edges
08:09:27 Optimization finished
[1] "101 0.01"08:09:27 UMAP embedding parameters a = 1.896 b = 0.8006
08:09:27 Read 1203 rows and found 38 numeric columns
08:09:27 Using Annoy for neighbor search, n_neighbors = 101
08:09:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:09:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725ee816e
08:09:27 Searching Annoy index using 1 thread, search_k = 10100
08:09:28 Annoy recall = 100%
08:09:35 Commencing smooth kNN distance calibration using 1 thread
08:09:49 Initializing from normalized Laplacian + noise
08:09:49 Commencing optimization for 500 epochs, with 141212 positive edges
08:09:59 Optimization finished
[1] "101 0.02"08:09:59 UMAP embedding parameters a = 1.859 b = 0.8109
08:09:59 Read 1203 rows and found 38 numeric columns
08:09:59 Using Annoy for neighbor search, n_neighbors = 101
08:10:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:10:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e89399b
08:10:00 Searching Annoy index using 1 thread, search_k = 10100
08:10:01 Annoy recall = 100%
08:10:08 Commencing smooth kNN distance calibration using 1 thread
08:10:21 Initializing from normalized Laplacian + noise
08:10:21 Commencing optimization for 500 epochs, with 141212 positive edges
08:10:32 Optimization finished
[1] "101 0.03"08:10:32 UMAP embedding parameters a = 1.822 b = 0.8212
08:10:32 Read 1203 rows and found 38 numeric columns
08:10:32 Using Annoy for neighbor search, n_neighbors = 101
08:10:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:10:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876dbb17b9
08:10:33 Searching Annoy index using 1 thread, search_k = 10100
08:10:33 Annoy recall = 100%
08:10:40 Commencing smooth kNN distance calibration using 1 thread
08:10:54 Initializing from normalized Laplacian + noise
08:10:54 Commencing optimization for 500 epochs, with 141212 positive edges
08:11:04 Optimization finished
[1] "101 0.04"08:11:05 UMAP embedding parameters a = 1.786 b = 0.8316
08:11:05 Read 1203 rows and found 38 numeric columns
08:11:05 Using Annoy for neighbor search, n_neighbors = 101
08:11:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:11:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875745adf7
08:11:05 Searching Annoy index using 1 thread, search_k = 10100
08:11:06 Annoy recall = 100%
08:11:13 Commencing smooth kNN distance calibration using 1 thread
08:11:26 Initializing from normalized Laplacian + noise
08:11:26 Commencing optimization for 500 epochs, with 141212 positive edges
08:11:37 Optimization finished
[1] "101 0.05"08:11:37 UMAP embedding parameters a = 1.75 b = 0.8421
08:11:37 Read 1203 rows and found 38 numeric columns
08:11:37 Using Annoy for neighbor search, n_neighbors = 101
08:11:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:11:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a23acd6
08:11:38 Searching Annoy index using 1 thread, search_k = 10100
08:11:38 Annoy recall = 100%
08:11:45 Commencing smooth kNN distance calibration using 1 thread
08:11:59 Initializing from normalized Laplacian + noise
08:11:59 Commencing optimization for 500 epochs, with 141212 positive edges
08:12:10 Optimization finished
[1] "101 0.06"08:12:10 UMAP embedding parameters a = 1.715 b = 0.8526
08:12:10 Read 1203 rows and found 38 numeric columns
08:12:10 Using Annoy for neighbor search, n_neighbors = 101
08:12:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:12:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b863eb0
08:12:10 Searching Annoy index using 1 thread, search_k = 10100
08:12:11 Annoy recall = 100%
08:12:18 Commencing smooth kNN distance calibration using 1 thread
08:12:31 Initializing from normalized Laplacian + noise
08:12:32 Commencing optimization for 500 epochs, with 141212 positive edges
08:12:42 Optimization finished
[1] "101 0.07"08:12:42 UMAP embedding parameters a = 1.68 b = 0.8631
08:12:42 Read 1203 rows and found 38 numeric columns
08:12:42 Using Annoy for neighbor search, n_neighbors = 101
08:12:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:12:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a8db074
08:12:43 Searching Annoy index using 1 thread, search_k = 10100
08:12:44 Annoy recall = 100%
08:12:50 Commencing smooth kNN distance calibration using 1 thread
08:13:04 Initializing from normalized Laplacian + noise
08:13:04 Commencing optimization for 500 epochs, with 141212 positive edges
08:13:15 Optimization finished
[1] "101 0.08"08:13:15 UMAP embedding parameters a = 1.645 b = 0.8737
08:13:15 Read 1203 rows and found 38 numeric columns
08:13:15 Using Annoy for neighbor search, n_neighbors = 101
08:13:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:13:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752dd9156
08:13:15 Searching Annoy index using 1 thread, search_k = 10100
08:13:16 Annoy recall = 100%
08:13:23 Commencing smooth kNN distance calibration using 1 thread
08:13:37 Initializing from normalized Laplacian + noise
08:13:37 Commencing optimization for 500 epochs, with 141212 positive edges
08:13:47 Optimization finished
[1] "101 0.09"08:13:48 UMAP embedding parameters a = 1.611 b = 0.8844
08:13:48 Read 1203 rows and found 38 numeric columns
08:13:48 Using Annoy for neighbor search, n_neighbors = 101
08:13:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:13:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874923f4b5
08:13:48 Searching Annoy index using 1 thread, search_k = 10100
08:13:49 Annoy recall = 100%
08:13:56 Commencing smooth kNN distance calibration using 1 thread
08:14:10 Initializing from normalized Laplacian + noise
08:14:10 Commencing optimization for 500 epochs, with 141212 positive edges
08:14:21 Optimization finished
[1] "101 0.1"08:14:22 UMAP embedding parameters a = 1.577 b = 0.8951
08:14:22 Read 1203 rows and found 38 numeric columns
08:14:22 Using Annoy for neighbor search, n_neighbors = 101
08:14:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:14:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745cadf5c
08:14:23 Searching Annoy index using 1 thread, search_k = 10100
08:14:24 Annoy recall = 100%
08:14:31 Commencing smooth kNN distance calibration using 1 thread
08:14:46 Initializing from normalized Laplacian + noise
08:14:46 Commencing optimization for 500 epochs, with 141212 positive edges
08:14:58 Optimization finished
[1] "101 0.11"08:14:58 UMAP embedding parameters a = 1.544 b = 0.9058
08:14:58 Read 1203 rows and found 38 numeric columns
08:14:58 Using Annoy for neighbor search, n_neighbors = 101
08:14:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:14:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872052c08
08:14:59 Searching Annoy index using 1 thread, search_k = 10100
08:15:00 Annoy recall = 100%
08:15:07 Commencing smooth kNN distance calibration using 1 thread
08:15:22 Initializing from normalized Laplacian + noise
08:15:22 Commencing optimization for 500 epochs, with 141212 positive edges
08:15:32 Optimization finished
[1] "101 0.12"08:15:33 UMAP embedding parameters a = 1.51 b = 0.9165
08:15:33 Read 1203 rows and found 38 numeric columns
08:15:33 Using Annoy for neighbor search, n_neighbors = 101
08:15:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:15:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873125d99
08:15:33 Searching Annoy index using 1 thread, search_k = 10100
08:15:34 Annoy recall = 100%
08:15:41 Commencing smooth kNN distance calibration using 1 thread
08:15:54 Initializing from normalized Laplacian + noise
08:15:54 Commencing optimization for 500 epochs, with 141212 positive edges
08:16:05 Optimization finished
[1] "101 0.13"08:16:05 UMAP embedding parameters a = 1.478 b = 0.9272
08:16:05 Read 1203 rows and found 38 numeric columns
08:16:05 Using Annoy for neighbor search, n_neighbors = 101
08:16:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:16:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774656f43
08:16:06 Searching Annoy index using 1 thread, search_k = 10100
08:16:07 Annoy recall = 100%
08:16:13 Commencing smooth kNN distance calibration using 1 thread
08:16:27 Initializing from normalized Laplacian + noise
08:16:27 Commencing optimization for 500 epochs, with 141212 positive edges
08:16:38 Optimization finished
[1] "101 0.14"08:16:38 UMAP embedding parameters a = 1.446 b = 0.938
08:16:38 Read 1203 rows and found 38 numeric columns
08:16:38 Using Annoy for neighbor search, n_neighbors = 101
08:16:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:16:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ebc76cf
08:16:38 Searching Annoy index using 1 thread, search_k = 10100
08:16:39 Annoy recall = 100%
08:16:46 Commencing smooth kNN distance calibration using 1 thread
08:17:00 Initializing from normalized Laplacian + noise
08:17:00 Commencing optimization for 500 epochs, with 141212 positive edges
08:17:11 Optimization finished
[1] "101 0.15"08:17:11 UMAP embedding parameters a = 1.414 b = 0.9488
08:17:11 Read 1203 rows and found 38 numeric columns
08:17:11 Using Annoy for neighbor search, n_neighbors = 101
08:17:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:17:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715127513
08:17:11 Searching Annoy index using 1 thread, search_k = 10100
08:17:12 Annoy recall = 100%
08:17:19 Commencing smooth kNN distance calibration using 1 thread
08:17:32 Initializing from normalized Laplacian + noise
08:17:33 Commencing optimization for 500 epochs, with 141212 positive edges
08:17:43 Optimization finished
[1] "101 0.16"08:17:43 UMAP embedding parameters a = 1.383 b = 0.9596
08:17:43 Read 1203 rows and found 38 numeric columns
08:17:43 Using Annoy for neighbor search, n_neighbors = 101
08:17:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:17:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bfb52d8
08:17:44 Searching Annoy index using 1 thread, search_k = 10100
08:17:45 Annoy recall = 100%
08:17:51 Commencing smooth kNN distance calibration using 1 thread
08:18:05 Initializing from normalized Laplacian + noise
08:18:05 Commencing optimization for 500 epochs, with 141212 positive edges
08:18:16 Optimization finished
[1] "101 0.17"08:18:16 UMAP embedding parameters a = 1.352 b = 0.9704
08:18:16 Read 1203 rows and found 38 numeric columns
08:18:16 Using Annoy for neighbor search, n_neighbors = 101
08:18:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:18:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e932286
08:18:17 Searching Annoy index using 1 thread, search_k = 10100
08:18:17 Annoy recall = 100%
08:18:24 Commencing smooth kNN distance calibration using 1 thread
08:18:38 Initializing from normalized Laplacian + noise
08:18:38 Commencing optimization for 500 epochs, with 141212 positive edges
08:18:49 Optimization finished
[1] "101 0.18"08:18:49 UMAP embedding parameters a = 1.321 b = 0.9813
08:18:49 Read 1203 rows and found 38 numeric columns
08:18:49 Using Annoy for neighbor search, n_neighbors = 101
08:18:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:18:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87199634b1
08:18:49 Searching Annoy index using 1 thread, search_k = 10100
08:18:50 Annoy recall = 100%
08:18:57 Commencing smooth kNN distance calibration using 1 thread
08:19:11 Initializing from normalized Laplacian + noise
08:19:11 Commencing optimization for 500 epochs, with 141212 positive edges
08:19:21 Optimization finished
[1] "101 0.19"08:19:22 UMAP embedding parameters a = 1.292 b = 0.9921
08:19:22 Read 1203 rows and found 38 numeric columns
08:19:22 Using Annoy for neighbor search, n_neighbors = 101
08:19:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:19:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716e3005a
08:19:22 Searching Annoy index using 1 thread, search_k = 10100
08:19:23 Annoy recall = 100%
08:19:30 Commencing smooth kNN distance calibration using 1 thread
08:19:43 Initializing from normalized Laplacian + noise
08:19:43 Commencing optimization for 500 epochs, with 141212 positive edges
08:19:54 Optimization finished
[1] "101 0.2"08:19:54 UMAP embedding parameters a = 1.262 b = 1.003
08:19:54 Read 1203 rows and found 38 numeric columns
08:19:54 Using Annoy for neighbor search, n_neighbors = 101
08:19:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:19:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ea8c8cc
08:19:55 Searching Annoy index using 1 thread, search_k = 10100
08:19:55 Annoy recall = 100%
08:20:02 Commencing smooth kNN distance calibration using 1 thread
08:20:16 Initializing from normalized Laplacian + noise
08:20:16 Commencing optimization for 500 epochs, with 141212 positive edges
08:20:27 Optimization finished
[1] "102 0"08:20:27 UMAP embedding parameters a = 1.933 b = 0.7905
08:20:27 Read 1203 rows and found 38 numeric columns
08:20:27 Using Annoy for neighbor search, n_neighbors = 102
08:20:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:20:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a94e6b8
08:20:28 Searching Annoy index using 1 thread, search_k = 10200
08:20:28 Annoy recall = 100%
08:20:35 Commencing smooth kNN distance calibration using 1 thread
08:20:49 Initializing from normalized Laplacian + noise
08:20:49 Commencing optimization for 500 epochs, with 142438 positive edges
08:21:00 Optimization finished
[1] "102 0.01"08:21:00 UMAP embedding parameters a = 1.896 b = 0.8006
08:21:00 Read 1203 rows and found 38 numeric columns
08:21:00 Using Annoy for neighbor search, n_neighbors = 102
08:21:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:21:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727f3dd4
08:21:00 Searching Annoy index using 1 thread, search_k = 10200
08:21:01 Annoy recall = 100%
08:21:08 Commencing smooth kNN distance calibration using 1 thread
08:21:22 Initializing from normalized Laplacian + noise
08:21:22 Commencing optimization for 500 epochs, with 142438 positive edges
08:21:32 Optimization finished
[1] "102 0.02"08:21:33 UMAP embedding parameters a = 1.859 b = 0.8109
08:21:33 Read 1203 rows and found 38 numeric columns
08:21:33 Using Annoy for neighbor search, n_neighbors = 102
08:21:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:21:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87424e3997
08:21:33 Searching Annoy index using 1 thread, search_k = 10200
08:21:34 Annoy recall = 100%
08:21:41 Commencing smooth kNN distance calibration using 1 thread
08:21:55 Initializing from normalized Laplacian + noise
08:21:55 Commencing optimization for 500 epochs, with 142438 positive edges
08:22:05 Optimization finished
[1] "102 0.03"08:22:06 UMAP embedding parameters a = 1.822 b = 0.8212
08:22:06 Read 1203 rows and found 38 numeric columns
08:22:06 Using Annoy for neighbor search, n_neighbors = 102
08:22:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:22:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87389f8e01
08:22:06 Searching Annoy index using 1 thread, search_k = 10200
08:22:07 Annoy recall = 100%
08:22:14 Commencing smooth kNN distance calibration using 1 thread
08:22:27 Initializing from normalized Laplacian + noise
08:22:28 Commencing optimization for 500 epochs, with 142438 positive edges
08:22:38 Optimization finished
[1] "102 0.04"08:22:38 UMAP embedding parameters a = 1.786 b = 0.8316
08:22:38 Read 1203 rows and found 38 numeric columns
08:22:38 Using Annoy for neighbor search, n_neighbors = 102
08:22:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:22:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777210d15
08:22:39 Searching Annoy index using 1 thread, search_k = 10200
08:22:40 Annoy recall = 100%
08:22:46 Commencing smooth kNN distance calibration using 1 thread
08:23:00 Initializing from normalized Laplacian + noise
08:23:00 Commencing optimization for 500 epochs, with 142438 positive edges
08:23:11 Optimization finished
[1] "102 0.05"08:23:11 UMAP embedding parameters a = 1.75 b = 0.8421
08:23:11 Read 1203 rows and found 38 numeric columns
08:23:11 Using Annoy for neighbor search, n_neighbors = 102
08:23:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:23:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770b23cf3
08:23:12 Searching Annoy index using 1 thread, search_k = 10200
08:23:12 Annoy recall = 100%
08:23:19 Commencing smooth kNN distance calibration using 1 thread
08:23:33 Initializing from normalized Laplacian + noise
08:23:33 Commencing optimization for 500 epochs, with 142438 positive edges
08:23:44 Optimization finished
[1] "102 0.06"08:23:44 UMAP embedding parameters a = 1.715 b = 0.8526
08:23:44 Read 1203 rows and found 38 numeric columns
08:23:44 Using Annoy for neighbor search, n_neighbors = 102
08:23:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:23:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777258824
08:23:44 Searching Annoy index using 1 thread, search_k = 10200
08:23:45 Annoy recall = 100%
08:23:52 Commencing smooth kNN distance calibration using 1 thread
08:24:06 Initializing from normalized Laplacian + noise
08:24:06 Commencing optimization for 500 epochs, with 142438 positive edges
08:24:17 Optimization finished
[1] "102 0.07"08:24:17 UMAP embedding parameters a = 1.68 b = 0.8631
08:24:17 Read 1203 rows and found 38 numeric columns
08:24:17 Using Annoy for neighbor search, n_neighbors = 102
08:24:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:24:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875deaf01e
08:24:17 Searching Annoy index using 1 thread, search_k = 10200
08:24:18 Annoy recall = 100%
08:24:25 Commencing smooth kNN distance calibration using 1 thread
08:24:39 Initializing from normalized Laplacian + noise
08:24:39 Commencing optimization for 500 epochs, with 142438 positive edges
08:24:49 Optimization finished
[1] "102 0.08"08:24:50 UMAP embedding parameters a = 1.645 b = 0.8737
08:24:50 Read 1203 rows and found 38 numeric columns
08:24:50 Using Annoy for neighbor search, n_neighbors = 102
08:24:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:24:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758a211b1
08:24:50 Searching Annoy index using 1 thread, search_k = 10200
08:24:51 Annoy recall = 100%
08:24:58 Commencing smooth kNN distance calibration using 1 thread
08:25:12 Initializing from normalized Laplacian + noise
08:25:12 Commencing optimization for 500 epochs, with 142438 positive edges
08:25:22 Optimization finished
[1] "102 0.09"08:25:23 UMAP embedding parameters a = 1.611 b = 0.8844
08:25:23 Read 1203 rows and found 38 numeric columns
08:25:23 Using Annoy for neighbor search, n_neighbors = 102
08:25:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:25:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87654b9790
08:25:23 Searching Annoy index using 1 thread, search_k = 10200
08:25:24 Annoy recall = 100%
08:25:31 Commencing smooth kNN distance calibration using 1 thread
08:25:44 Initializing from normalized Laplacian + noise
08:25:45 Commencing optimization for 500 epochs, with 142438 positive edges
08:25:55 Optimization finished
[1] "102 0.1"08:25:55 UMAP embedding parameters a = 1.577 b = 0.8951
08:25:55 Read 1203 rows and found 38 numeric columns
08:25:55 Using Annoy for neighbor search, n_neighbors = 102
08:25:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:25:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e1f3f27
08:25:56 Searching Annoy index using 1 thread, search_k = 10200
08:25:57 Annoy recall = 100%
08:26:04 Commencing smooth kNN distance calibration using 1 thread
08:26:17 Initializing from normalized Laplacian + noise
08:26:17 Commencing optimization for 500 epochs, with 142438 positive edges
08:26:28 Optimization finished
[1] "102 0.11"08:26:28 UMAP embedding parameters a = 1.544 b = 0.9058
08:26:28 Read 1203 rows and found 38 numeric columns
08:26:28 Using Annoy for neighbor search, n_neighbors = 102
08:26:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:26:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e90931f
08:26:29 Searching Annoy index using 1 thread, search_k = 10200
08:26:30 Annoy recall = 100%
08:26:37 Commencing smooth kNN distance calibration using 1 thread
08:26:50 Initializing from normalized Laplacian + noise
08:26:50 Commencing optimization for 500 epochs, with 142438 positive edges
08:27:01 Optimization finished
[1] "102 0.12"08:27:01 UMAP embedding parameters a = 1.51 b = 0.9165
08:27:01 Read 1203 rows and found 38 numeric columns
08:27:01 Using Annoy for neighbor search, n_neighbors = 102
08:27:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:27:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d4d12b
08:27:02 Searching Annoy index using 1 thread, search_k = 10200
08:27:02 Annoy recall = 100%
08:27:09 Commencing smooth kNN distance calibration using 1 thread
08:27:23 Initializing from normalized Laplacian + noise
08:27:23 Commencing optimization for 500 epochs, with 142438 positive edges
08:27:34 Optimization finished
[1] "102 0.13"08:27:34 UMAP embedding parameters a = 1.478 b = 0.9272
08:27:34 Read 1203 rows and found 38 numeric columns
08:27:34 Using Annoy for neighbor search, n_neighbors = 102
08:27:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:27:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bda56e0
08:27:35 Searching Annoy index using 1 thread, search_k = 10200
08:27:36 Annoy recall = 100%
08:27:42 Commencing smooth kNN distance calibration using 1 thread
08:27:56 Initializing from normalized Laplacian + noise
08:27:56 Commencing optimization for 500 epochs, with 142438 positive edges
08:28:07 Optimization finished
[1] "102 0.14"08:28:07 UMAP embedding parameters a = 1.446 b = 0.938
08:28:07 Read 1203 rows and found 38 numeric columns
08:28:07 Using Annoy for neighbor search, n_neighbors = 102
08:28:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:28:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755d64117
08:28:08 Searching Annoy index using 1 thread, search_k = 10200
08:28:08 Annoy recall = 100%
08:28:15 Commencing smooth kNN distance calibration using 1 thread
08:28:29 Initializing from normalized Laplacian + noise
08:28:29 Commencing optimization for 500 epochs, with 142438 positive edges
08:28:40 Optimization finished
[1] "102 0.15"08:28:40 UMAP embedding parameters a = 1.414 b = 0.9488
08:28:40 Read 1203 rows and found 38 numeric columns
08:28:40 Using Annoy for neighbor search, n_neighbors = 102
08:28:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:28:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877df87e01
08:28:41 Searching Annoy index using 1 thread, search_k = 10200
08:28:42 Annoy recall = 100%
08:28:48 Commencing smooth kNN distance calibration using 1 thread
08:29:02 Initializing from normalized Laplacian + noise
08:29:02 Commencing optimization for 500 epochs, with 142438 positive edges
08:29:13 Optimization finished
[1] "102 0.16"08:29:13 UMAP embedding parameters a = 1.383 b = 0.9596
08:29:13 Read 1203 rows and found 38 numeric columns
08:29:13 Using Annoy for neighbor search, n_neighbors = 102
08:29:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:29:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727609590
08:29:14 Searching Annoy index using 1 thread, search_k = 10200
08:29:14 Annoy recall = 100%
08:29:21 Commencing smooth kNN distance calibration using 1 thread
08:29:35 Initializing from normalized Laplacian + noise
08:29:35 Commencing optimization for 500 epochs, with 142438 positive edges
08:29:46 Optimization finished
[1] "102 0.17"08:29:46 UMAP embedding parameters a = 1.352 b = 0.9704
08:29:46 Read 1203 rows and found 38 numeric columns
08:29:46 Using Annoy for neighbor search, n_neighbors = 102
08:29:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:29:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873063f18b
08:29:47 Searching Annoy index using 1 thread, search_k = 10200
08:29:48 Annoy recall = 100%
08:29:54 Commencing smooth kNN distance calibration using 1 thread
08:30:08 Initializing from normalized Laplacian + noise
08:30:08 Commencing optimization for 500 epochs, with 142438 positive edges
08:30:19 Optimization finished
[1] "102 0.18"08:30:19 UMAP embedding parameters a = 1.321 b = 0.9813
08:30:19 Read 1203 rows and found 38 numeric columns
08:30:19 Using Annoy for neighbor search, n_neighbors = 102
08:30:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:30:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750d60f57
08:30:20 Searching Annoy index using 1 thread, search_k = 10200
08:30:21 Annoy recall = 100%
08:30:27 Commencing smooth kNN distance calibration using 1 thread
08:30:41 Initializing from normalized Laplacian + noise
08:30:41 Commencing optimization for 500 epochs, with 142438 positive edges
08:30:52 Optimization finished
[1] "102 0.19"08:30:52 UMAP embedding parameters a = 1.292 b = 0.9921
08:30:52 Read 1203 rows and found 38 numeric columns
08:30:52 Using Annoy for neighbor search, n_neighbors = 102
08:30:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:30:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770848a45
08:30:53 Searching Annoy index using 1 thread, search_k = 10200
08:30:54 Annoy recall = 100%
08:31:00 Commencing smooth kNN distance calibration using 1 thread
08:31:14 Initializing from normalized Laplacian + noise
08:31:14 Commencing optimization for 500 epochs, with 142438 positive edges
08:31:25 Optimization finished
[1] "102 0.2"08:31:25 UMAP embedding parameters a = 1.262 b = 1.003
08:31:25 Read 1203 rows and found 38 numeric columns
08:31:25 Using Annoy for neighbor search, n_neighbors = 102
08:31:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:31:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87762ed0e7
08:31:26 Searching Annoy index using 1 thread, search_k = 10200
08:31:27 Annoy recall = 100%
08:31:34 Commencing smooth kNN distance calibration using 1 thread
08:31:48 Initializing from normalized Laplacian + noise
08:31:48 Commencing optimization for 500 epochs, with 142438 positive edges
08:31:58 Optimization finished
[1] "103 0"08:31:58 UMAP embedding parameters a = 1.933 b = 0.7905
08:31:58 Read 1203 rows and found 38 numeric columns
08:31:58 Using Annoy for neighbor search, n_neighbors = 103
08:31:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:31:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752db3b60
08:31:59 Searching Annoy index using 1 thread, search_k = 10300
08:32:00 Annoy recall = 100%
08:32:07 Commencing smooth kNN distance calibration using 1 thread
08:32:20 Initializing from normalized Laplacian + noise
08:32:21 Commencing optimization for 500 epochs, with 143732 positive edges
08:32:31 Optimization finished
[1] "103 0.01"08:32:31 UMAP embedding parameters a = 1.896 b = 0.8006
08:32:32 Read 1203 rows and found 38 numeric columns
08:32:32 Using Annoy for neighbor search, n_neighbors = 103
08:32:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:32:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877396e7de
08:32:32 Searching Annoy index using 1 thread, search_k = 10300
08:32:33 Annoy recall = 100%
08:32:40 Commencing smooth kNN distance calibration using 1 thread
08:32:54 Initializing from normalized Laplacian + noise
08:32:54 Commencing optimization for 500 epochs, with 143732 positive edges
08:33:04 Optimization finished
[1] "103 0.02"08:33:05 UMAP embedding parameters a = 1.859 b = 0.8109
08:33:05 Read 1203 rows and found 38 numeric columns
08:33:05 Using Annoy for neighbor search, n_neighbors = 103
08:33:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:33:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a94402a
08:33:05 Searching Annoy index using 1 thread, search_k = 10300
08:33:06 Annoy recall = 100%
08:33:13 Commencing smooth kNN distance calibration using 1 thread
08:33:27 Initializing from normalized Laplacian + noise
08:33:27 Commencing optimization for 500 epochs, with 143732 positive edges
08:33:38 Optimization finished
[1] "103 0.03"08:33:38 UMAP embedding parameters a = 1.822 b = 0.8212
08:33:38 Read 1203 rows and found 38 numeric columns
08:33:38 Using Annoy for neighbor search, n_neighbors = 103
08:33:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:33:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871197b22f
08:33:38 Searching Annoy index using 1 thread, search_k = 10300
08:33:39 Annoy recall = 100%
08:33:46 Commencing smooth kNN distance calibration using 1 thread
08:34:00 Initializing from normalized Laplacian + noise
08:34:00 Commencing optimization for 500 epochs, with 143732 positive edges
08:34:11 Optimization finished
[1] "103 0.04"08:34:11 UMAP embedding parameters a = 1.786 b = 0.8316
08:34:11 Read 1203 rows and found 38 numeric columns
08:34:11 Using Annoy for neighbor search, n_neighbors = 103
08:34:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:34:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878a95cf2
08:34:11 Searching Annoy index using 1 thread, search_k = 10300
08:34:12 Annoy recall = 100%
08:34:19 Commencing smooth kNN distance calibration using 1 thread
08:34:33 Initializing from normalized Laplacian + noise
08:34:33 Commencing optimization for 500 epochs, with 143732 positive edges
08:34:44 Optimization finished
[1] "103 0.05"08:34:44 UMAP embedding parameters a = 1.75 b = 0.8421
08:34:44 Read 1203 rows and found 38 numeric columns
08:34:44 Using Annoy for neighbor search, n_neighbors = 103
08:34:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:34:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87768f9302
08:34:45 Searching Annoy index using 1 thread, search_k = 10300
08:34:45 Annoy recall = 100%
08:34:52 Commencing smooth kNN distance calibration using 1 thread
08:35:06 Initializing from normalized Laplacian + noise
08:35:06 Commencing optimization for 500 epochs, with 143732 positive edges
08:35:17 Optimization finished
[1] "103 0.06"08:35:17 UMAP embedding parameters a = 1.715 b = 0.8526
08:35:17 Read 1203 rows and found 38 numeric columns
08:35:17 Using Annoy for neighbor search, n_neighbors = 103
08:35:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:35:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87102ad4b5
08:35:18 Searching Annoy index using 1 thread, search_k = 10300
08:35:18 Annoy recall = 100%
08:35:25 Commencing smooth kNN distance calibration using 1 thread
08:35:39 Initializing from normalized Laplacian + noise
08:35:39 Commencing optimization for 500 epochs, with 143732 positive edges
08:35:50 Optimization finished
[1] "103 0.07"08:35:50 UMAP embedding parameters a = 1.68 b = 0.8631
08:35:50 Read 1203 rows and found 38 numeric columns
08:35:50 Using Annoy for neighbor search, n_neighbors = 103
08:35:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:35:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87223f91a3
08:35:51 Searching Annoy index using 1 thread, search_k = 10300
08:35:52 Annoy recall = 100%
08:35:59 Commencing smooth kNN distance calibration using 1 thread
08:36:12 Initializing from normalized Laplacian + noise
08:36:12 Commencing optimization for 500 epochs, with 143732 positive edges
08:36:23 Optimization finished
[1] "103 0.08"08:36:23 UMAP embedding parameters a = 1.645 b = 0.8737
08:36:23 Read 1203 rows and found 38 numeric columns
08:36:23 Using Annoy for neighbor search, n_neighbors = 103
08:36:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:36:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d72935d
08:36:24 Searching Annoy index using 1 thread, search_k = 10300
08:36:25 Annoy recall = 100%
08:36:32 Commencing smooth kNN distance calibration using 1 thread
08:36:46 Initializing from normalized Laplacian + noise
08:36:46 Commencing optimization for 500 epochs, with 143732 positive edges
08:36:56 Optimization finished
[1] "103 0.09"08:36:57 UMAP embedding parameters a = 1.611 b = 0.8844
08:36:57 Read 1203 rows and found 38 numeric columns
08:36:57 Using Annoy for neighbor search, n_neighbors = 103
08:36:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:36:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ed39d81
08:36:57 Searching Annoy index using 1 thread, search_k = 10300
08:36:58 Annoy recall = 100%
08:37:05 Commencing smooth kNN distance calibration using 1 thread
08:37:19 Initializing from normalized Laplacian + noise
08:37:19 Commencing optimization for 500 epochs, with 143732 positive edges
08:37:30 Optimization finished
[1] "103 0.1"08:37:30 UMAP embedding parameters a = 1.577 b = 0.8951
08:37:30 Read 1203 rows and found 38 numeric columns
08:37:30 Using Annoy for neighbor search, n_neighbors = 103
08:37:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:37:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cd4785c
08:37:30 Searching Annoy index using 1 thread, search_k = 10300
08:37:31 Annoy recall = 100%
08:37:38 Commencing smooth kNN distance calibration using 1 thread
08:37:52 Initializing from normalized Laplacian + noise
08:37:52 Commencing optimization for 500 epochs, with 143732 positive edges
08:38:03 Optimization finished
[1] "103 0.11"08:38:03 UMAP embedding parameters a = 1.544 b = 0.9058
08:38:03 Read 1203 rows and found 38 numeric columns
08:38:03 Using Annoy for neighbor search, n_neighbors = 103
08:38:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:38:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ff1d131
08:38:04 Searching Annoy index using 1 thread, search_k = 10300
08:38:04 Annoy recall = 100%
08:38:11 Commencing smooth kNN distance calibration using 1 thread
08:38:25 Initializing from normalized Laplacian + noise
08:38:25 Commencing optimization for 500 epochs, with 143732 positive edges
08:38:36 Optimization finished
[1] "103 0.12"08:38:36 UMAP embedding parameters a = 1.51 b = 0.9165
08:38:36 Read 1203 rows and found 38 numeric columns
08:38:36 Using Annoy for neighbor search, n_neighbors = 103
08:38:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:38:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87121d719
08:38:37 Searching Annoy index using 1 thread, search_k = 10300
08:38:37 Annoy recall = 100%
08:38:44 Commencing smooth kNN distance calibration using 1 thread
08:38:58 Initializing from normalized Laplacian + noise
08:38:59 Commencing optimization for 500 epochs, with 143732 positive edges
08:39:09 Optimization finished
[1] "103 0.13"08:39:10 UMAP embedding parameters a = 1.478 b = 0.9272
08:39:10 Read 1203 rows and found 38 numeric columns
08:39:10 Using Annoy for neighbor search, n_neighbors = 103
08:39:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:39:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871574065d
08:39:10 Searching Annoy index using 1 thread, search_k = 10300
08:39:11 Annoy recall = 100%
08:39:18 Commencing smooth kNN distance calibration using 1 thread
08:39:32 Initializing from normalized Laplacian + noise
08:39:32 Commencing optimization for 500 epochs, with 143732 positive edges
08:39:42 Optimization finished
[1] "103 0.14"08:39:43 UMAP embedding parameters a = 1.446 b = 0.938
08:39:43 Read 1203 rows and found 38 numeric columns
08:39:43 Using Annoy for neighbor search, n_neighbors = 103
08:39:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:39:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87712de46
08:39:43 Searching Annoy index using 1 thread, search_k = 10300
08:39:44 Annoy recall = 100%
08:39:51 Commencing smooth kNN distance calibration using 1 thread
08:40:05 Initializing from normalized Laplacian + noise
08:40:05 Commencing optimization for 500 epochs, with 143732 positive edges
08:40:16 Optimization finished
[1] "103 0.15"08:40:16 UMAP embedding parameters a = 1.414 b = 0.9488
08:40:16 Read 1203 rows and found 38 numeric columns
08:40:16 Using Annoy for neighbor search, n_neighbors = 103
08:40:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:40:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771d4140c
08:40:16 Searching Annoy index using 1 thread, search_k = 10300
08:40:17 Annoy recall = 100%
08:40:24 Commencing smooth kNN distance calibration using 1 thread
08:40:38 Initializing from normalized Laplacian + noise
08:40:38 Commencing optimization for 500 epochs, with 143732 positive edges
08:40:49 Optimization finished
[1] "103 0.16"08:40:49 UMAP embedding parameters a = 1.383 b = 0.9596
08:40:49 Read 1203 rows and found 38 numeric columns
08:40:49 Using Annoy for neighbor search, n_neighbors = 103
08:40:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:40:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c998e81
08:40:50 Searching Annoy index using 1 thread, search_k = 10300
08:40:50 Annoy recall = 100%
08:40:57 Commencing smooth kNN distance calibration using 1 thread
08:41:11 Initializing from normalized Laplacian + noise
08:41:11 Commencing optimization for 500 epochs, with 143732 positive edges
08:41:22 Optimization finished
[1] "103 0.17"08:41:22 UMAP embedding parameters a = 1.352 b = 0.9704
08:41:22 Read 1203 rows and found 38 numeric columns
08:41:22 Using Annoy for neighbor search, n_neighbors = 103
08:41:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:41:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764fdce65
08:41:23 Searching Annoy index using 1 thread, search_k = 10300
08:41:24 Annoy recall = 100%
08:41:30 Commencing smooth kNN distance calibration using 1 thread
08:41:44 Initializing from normalized Laplacian + noise
08:41:45 Commencing optimization for 500 epochs, with 143732 positive edges
08:41:55 Optimization finished
[1] "103 0.18"08:41:56 UMAP embedding parameters a = 1.321 b = 0.9813
08:41:56 Read 1203 rows and found 38 numeric columns
08:41:56 Using Annoy for neighbor search, n_neighbors = 103
08:41:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:41:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a7625bd
08:41:56 Searching Annoy index using 1 thread, search_k = 10300
08:41:57 Annoy recall = 100%
08:42:04 Commencing smooth kNN distance calibration using 1 thread
08:42:18 Initializing from normalized Laplacian + noise
08:42:18 Commencing optimization for 500 epochs, with 143732 positive edges
08:42:29 Optimization finished
[1] "103 0.19"08:42:29 UMAP embedding parameters a = 1.292 b = 0.9921
08:42:29 Read 1203 rows and found 38 numeric columns
08:42:29 Using Annoy for neighbor search, n_neighbors = 103
08:42:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:42:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771e52611
08:42:29 Searching Annoy index using 1 thread, search_k = 10300
08:42:30 Annoy recall = 100%
08:42:37 Commencing smooth kNN distance calibration using 1 thread
08:42:51 Initializing from normalized Laplacian + noise
08:42:51 Commencing optimization for 500 epochs, with 143732 positive edges
08:43:02 Optimization finished
[1] "103 0.2"08:43:02 UMAP embedding parameters a = 1.262 b = 1.003
08:43:02 Read 1203 rows and found 38 numeric columns
08:43:02 Using Annoy for neighbor search, n_neighbors = 103
08:43:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:43:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87331d0d8c
08:43:03 Searching Annoy index using 1 thread, search_k = 10300
08:43:03 Annoy recall = 100%
08:43:10 Commencing smooth kNN distance calibration using 1 thread
08:43:24 Initializing from normalized Laplacian + noise
08:43:24 Commencing optimization for 500 epochs, with 143732 positive edges
08:43:35 Optimization finished
[1] "104 0"08:43:35 UMAP embedding parameters a = 1.933 b = 0.7905
08:43:35 Read 1203 rows and found 38 numeric columns
08:43:35 Using Annoy for neighbor search, n_neighbors = 104
08:43:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:43:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874906b8dd
08:43:36 Searching Annoy index using 1 thread, search_k = 10400
08:43:37 Annoy recall = 100%
08:43:44 Commencing smooth kNN distance calibration using 1 thread
08:43:58 Initializing from normalized Laplacian + noise
08:43:58 Commencing optimization for 500 epochs, with 144980 positive edges
08:44:09 Optimization finished
[1] "104 0.01"08:44:09 UMAP embedding parameters a = 1.896 b = 0.8006
08:44:09 Read 1203 rows and found 38 numeric columns
08:44:09 Using Annoy for neighbor search, n_neighbors = 104
08:44:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:44:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775b9f73c
08:44:09 Searching Annoy index using 1 thread, search_k = 10400
08:44:10 Annoy recall = 100%
08:44:17 Commencing smooth kNN distance calibration using 1 thread
08:44:31 Initializing from normalized Laplacian + noise
08:44:31 Commencing optimization for 500 epochs, with 144980 positive edges
08:44:42 Optimization finished
[1] "104 0.02"08:44:42 UMAP embedding parameters a = 1.859 b = 0.8109
08:44:42 Read 1203 rows and found 38 numeric columns
08:44:42 Using Annoy for neighbor search, n_neighbors = 104
08:44:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:44:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ef7646c
08:44:43 Searching Annoy index using 1 thread, search_k = 10400
08:44:43 Annoy recall = 100%
08:44:50 Commencing smooth kNN distance calibration using 1 thread
08:45:05 Initializing from normalized Laplacian + noise
08:45:05 Commencing optimization for 500 epochs, with 144980 positive edges
08:45:16 Optimization finished
[1] "104 0.03"08:45:16 UMAP embedding parameters a = 1.822 b = 0.8212
08:45:16 Read 1203 rows and found 38 numeric columns
08:45:16 Using Annoy for neighbor search, n_neighbors = 104
08:45:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:45:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871edcf9f4
08:45:16 Searching Annoy index using 1 thread, search_k = 10400
08:45:17 Annoy recall = 100%
08:45:24 Commencing smooth kNN distance calibration using 1 thread
08:45:38 Initializing from normalized Laplacian + noise
08:45:38 Commencing optimization for 500 epochs, with 144980 positive edges
08:45:49 Optimization finished
[1] "104 0.04"08:45:49 UMAP embedding parameters a = 1.786 b = 0.8316
08:45:49 Read 1203 rows and found 38 numeric columns
08:45:49 Using Annoy for neighbor search, n_neighbors = 104
08:45:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:45:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773b2753e
08:45:50 Searching Annoy index using 1 thread, search_k = 10400
08:45:51 Annoy recall = 100%
08:45:58 Commencing smooth kNN distance calibration using 1 thread
08:46:12 Initializing from normalized Laplacian + noise
08:46:12 Commencing optimization for 500 epochs, with 144980 positive edges
08:46:23 Optimization finished
[1] "104 0.05"08:46:23 UMAP embedding parameters a = 1.75 b = 0.8421
08:46:23 Read 1203 rows and found 38 numeric columns
08:46:23 Using Annoy for neighbor search, n_neighbors = 104
08:46:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:46:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871657f9fc
08:46:24 Searching Annoy index using 1 thread, search_k = 10400
08:46:24 Annoy recall = 100%
08:46:31 Commencing smooth kNN distance calibration using 1 thread
08:46:45 Initializing from normalized Laplacian + noise
08:46:45 Commencing optimization for 500 epochs, with 144980 positive edges
08:46:56 Optimization finished
[1] "104 0.06"08:46:57 UMAP embedding parameters a = 1.715 b = 0.8526
08:46:57 Read 1203 rows and found 38 numeric columns
08:46:57 Using Annoy for neighbor search, n_neighbors = 104
08:46:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:46:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f40eb7f
08:46:57 Searching Annoy index using 1 thread, search_k = 10400
08:46:58 Annoy recall = 100%
08:47:05 Commencing smooth kNN distance calibration using 1 thread
08:47:19 Initializing from normalized Laplacian + noise
08:47:19 Commencing optimization for 500 epochs, with 144980 positive edges
08:47:30 Optimization finished
[1] "104 0.07"08:47:30 UMAP embedding parameters a = 1.68 b = 0.8631
08:47:30 Read 1203 rows and found 38 numeric columns
08:47:30 Using Annoy for neighbor search, n_neighbors = 104
08:47:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:47:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744888495
08:47:31 Searching Annoy index using 1 thread, search_k = 10400
08:47:32 Annoy recall = 100%
08:47:39 Commencing smooth kNN distance calibration using 1 thread
08:47:53 Initializing from normalized Laplacian + noise
08:47:53 Commencing optimization for 500 epochs, with 144980 positive edges
08:48:04 Optimization finished
[1] "104 0.08"08:48:04 UMAP embedding parameters a = 1.645 b = 0.8737
08:48:04 Read 1203 rows and found 38 numeric columns
08:48:04 Using Annoy for neighbor search, n_neighbors = 104
08:48:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:48:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876dc8441
08:48:04 Searching Annoy index using 1 thread, search_k = 10400
08:48:05 Annoy recall = 100%
08:48:12 Commencing smooth kNN distance calibration using 1 thread
08:48:26 Initializing from normalized Laplacian + noise
08:48:27 Commencing optimization for 500 epochs, with 144980 positive edges
08:48:37 Optimization finished
[1] "104 0.09"08:48:38 UMAP embedding parameters a = 1.611 b = 0.8844
08:48:38 Read 1203 rows and found 38 numeric columns
08:48:38 Using Annoy for neighbor search, n_neighbors = 104
08:48:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:48:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87456fbc66
08:48:38 Searching Annoy index using 1 thread, search_k = 10400
08:48:39 Annoy recall = 100%
08:48:46 Commencing smooth kNN distance calibration using 1 thread
08:49:00 Initializing from normalized Laplacian + noise
08:49:00 Commencing optimization for 500 epochs, with 144980 positive edges
08:49:11 Optimization finished
[1] "104 0.1"08:49:11 UMAP embedding parameters a = 1.577 b = 0.8951
08:49:11 Read 1203 rows and found 38 numeric columns
08:49:11 Using Annoy for neighbor search, n_neighbors = 104
08:49:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:49:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871763bff5
08:49:12 Searching Annoy index using 1 thread, search_k = 10400
08:49:13 Annoy recall = 100%
08:49:20 Commencing smooth kNN distance calibration using 1 thread
08:49:34 Initializing from normalized Laplacian + noise
08:49:34 Commencing optimization for 500 epochs, with 144980 positive edges
08:49:45 Optimization finished
[1] "104 0.11"08:49:45 UMAP embedding parameters a = 1.544 b = 0.9058
08:49:45 Read 1203 rows and found 38 numeric columns
08:49:45 Using Annoy for neighbor search, n_neighbors = 104
08:49:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:49:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a736c20
08:49:45 Searching Annoy index using 1 thread, search_k = 10400
08:49:46 Annoy recall = 100%
08:49:53 Commencing smooth kNN distance calibration using 1 thread
08:50:07 Initializing from normalized Laplacian + noise
08:50:07 Commencing optimization for 500 epochs, with 144980 positive edges
08:50:18 Optimization finished
[1] "104 0.12"08:50:19 UMAP embedding parameters a = 1.51 b = 0.9165
08:50:19 Read 1203 rows and found 38 numeric columns
08:50:19 Using Annoy for neighbor search, n_neighbors = 104
08:50:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:50:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873003fc90
08:50:19 Searching Annoy index using 1 thread, search_k = 10400
08:50:20 Annoy recall = 100%
08:50:27 Commencing smooth kNN distance calibration using 1 thread
08:50:41 Initializing from normalized Laplacian + noise
08:50:41 Commencing optimization for 500 epochs, with 144980 positive edges
08:50:52 Optimization finished
[1] "104 0.13"08:50:52 UMAP embedding parameters a = 1.478 b = 0.9272
08:50:52 Read 1203 rows and found 38 numeric columns
08:50:52 Using Annoy for neighbor search, n_neighbors = 104
08:50:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:50:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728fb7224
08:50:53 Searching Annoy index using 1 thread, search_k = 10400
08:50:54 Annoy recall = 100%
08:51:01 Commencing smooth kNN distance calibration using 1 thread
08:51:15 Initializing from normalized Laplacian + noise
08:51:15 Commencing optimization for 500 epochs, with 144980 positive edges
08:51:26 Optimization finished
[1] "104 0.14"08:51:26 UMAP embedding parameters a = 1.446 b = 0.938
08:51:26 Read 1203 rows and found 38 numeric columns
08:51:26 Using Annoy for neighbor search, n_neighbors = 104
08:51:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:51:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731cc912
08:51:27 Searching Annoy index using 1 thread, search_k = 10400
08:51:27 Annoy recall = 100%
08:51:35 Commencing smooth kNN distance calibration using 1 thread
08:51:49 Initializing from normalized Laplacian + noise
08:51:49 Commencing optimization for 500 epochs, with 144980 positive edges
08:52:00 Optimization finished
[1] "104 0.15"08:52:00 UMAP embedding parameters a = 1.414 b = 0.9488
08:52:00 Read 1203 rows and found 38 numeric columns
08:52:00 Using Annoy for neighbor search, n_neighbors = 104
08:52:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:52:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726938f92
08:52:00 Searching Annoy index using 1 thread, search_k = 10400
08:52:01 Annoy recall = 100%
08:52:08 Commencing smooth kNN distance calibration using 1 thread
08:52:22 Initializing from normalized Laplacian + noise
08:52:22 Commencing optimization for 500 epochs, with 144980 positive edges
08:52:33 Optimization finished
[1] "104 0.16"08:52:34 UMAP embedding parameters a = 1.383 b = 0.9596
08:52:34 Read 1203 rows and found 38 numeric columns
08:52:34 Using Annoy for neighbor search, n_neighbors = 104
08:52:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:52:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87392646da
08:52:34 Searching Annoy index using 1 thread, search_k = 10400
08:52:35 Annoy recall = 100%
08:52:42 Commencing smooth kNN distance calibration using 1 thread
08:52:56 Initializing from normalized Laplacian + noise
08:52:56 Commencing optimization for 500 epochs, with 144980 positive edges
08:53:07 Optimization finished
[1] "104 0.17"08:53:07 UMAP embedding parameters a = 1.352 b = 0.9704
08:53:07 Read 1203 rows and found 38 numeric columns
08:53:07 Using Annoy for neighbor search, n_neighbors = 104
08:53:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:53:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87255c5ab5
08:53:08 Searching Annoy index using 1 thread, search_k = 10400
08:53:09 Annoy recall = 100%
08:53:16 Commencing smooth kNN distance calibration using 1 thread
08:53:30 Initializing from normalized Laplacian + noise
08:53:30 Commencing optimization for 500 epochs, with 144980 positive edges
08:53:41 Optimization finished
[1] "104 0.18"08:53:41 UMAP embedding parameters a = 1.321 b = 0.9813
08:53:41 Read 1203 rows and found 38 numeric columns
08:53:41 Using Annoy for neighbor search, n_neighbors = 104
08:53:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:53:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87340622ef
08:53:42 Searching Annoy index using 1 thread, search_k = 10400
08:53:43 Annoy recall = 100%
08:53:50 Commencing smooth kNN distance calibration using 1 thread
08:54:04 Initializing from normalized Laplacian + noise
08:54:04 Commencing optimization for 500 epochs, with 144980 positive edges
08:54:15 Optimization finished
[1] "104 0.19"08:54:15 UMAP embedding parameters a = 1.292 b = 0.9921
08:54:15 Read 1203 rows and found 38 numeric columns
08:54:15 Using Annoy for neighbor search, n_neighbors = 104
08:54:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:54:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777f9e45b
08:54:15 Searching Annoy index using 1 thread, search_k = 10400
08:54:16 Annoy recall = 100%
08:54:23 Commencing smooth kNN distance calibration using 1 thread
08:54:38 Initializing from normalized Laplacian + noise
08:54:38 Commencing optimization for 500 epochs, with 144980 positive edges
08:54:49 Optimization finished
[1] "104 0.2"08:54:49 UMAP embedding parameters a = 1.262 b = 1.003
08:54:49 Read 1203 rows and found 38 numeric columns
08:54:49 Using Annoy for neighbor search, n_neighbors = 104
08:54:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:54:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87230d311
08:54:49 Searching Annoy index using 1 thread, search_k = 10400
08:54:50 Annoy recall = 100%
08:54:57 Commencing smooth kNN distance calibration using 1 thread
08:55:11 Initializing from normalized Laplacian + noise
08:55:11 Commencing optimization for 500 epochs, with 144980 positive edges
08:55:22 Optimization finished
[1] "105 0"08:55:22 UMAP embedding parameters a = 1.933 b = 0.7905
08:55:23 Read 1203 rows and found 38 numeric columns
08:55:23 Using Annoy for neighbor search, n_neighbors = 105
08:55:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:55:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743f7f421
08:55:23 Searching Annoy index using 1 thread, search_k = 10500
08:55:24 Annoy recall = 100%
08:55:31 Commencing smooth kNN distance calibration using 1 thread
08:55:45 Initializing from normalized Laplacian + noise
08:55:45 Commencing optimization for 500 epochs, with 146262 positive edges
08:55:56 Optimization finished
[1] "105 0.01"08:55:56 UMAP embedding parameters a = 1.896 b = 0.8006
08:55:56 Read 1203 rows and found 38 numeric columns
08:55:56 Using Annoy for neighbor search, n_neighbors = 105
08:55:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:55:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87791bbb74
08:55:57 Searching Annoy index using 1 thread, search_k = 10500
08:55:58 Annoy recall = 100%
08:56:05 Commencing smooth kNN distance calibration using 1 thread
08:56:19 Initializing from normalized Laplacian + noise
08:56:19 Commencing optimization for 500 epochs, with 146262 positive edges
08:56:30 Optimization finished
[1] "105 0.02"08:56:30 UMAP embedding parameters a = 1.859 b = 0.8109
08:56:30 Read 1203 rows and found 38 numeric columns
08:56:30 Using Annoy for neighbor search, n_neighbors = 105
08:56:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:56:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717a4d96e
08:56:31 Searching Annoy index using 1 thread, search_k = 10500
08:56:32 Annoy recall = 100%
08:56:39 Commencing smooth kNN distance calibration using 1 thread
08:56:53 Initializing from normalized Laplacian + noise
08:56:53 Commencing optimization for 500 epochs, with 146262 positive edges
08:57:04 Optimization finished
[1] "105 0.03"08:57:04 UMAP embedding parameters a = 1.822 b = 0.8212
08:57:04 Read 1203 rows and found 38 numeric columns
08:57:04 Using Annoy for neighbor search, n_neighbors = 105
08:57:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:57:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b0ad267
08:57:05 Searching Annoy index using 1 thread, search_k = 10500
08:57:05 Annoy recall = 100%
08:57:13 Commencing smooth kNN distance calibration using 1 thread
08:57:27 Initializing from normalized Laplacian + noise
08:57:27 Commencing optimization for 500 epochs, with 146262 positive edges
08:57:38 Optimization finished
[1] "105 0.04"08:57:38 UMAP embedding parameters a = 1.786 b = 0.8316
08:57:38 Read 1203 rows and found 38 numeric columns
08:57:38 Using Annoy for neighbor search, n_neighbors = 105
08:57:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:57:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aefcf81
08:57:39 Searching Annoy index using 1 thread, search_k = 10500
08:57:39 Annoy recall = 100%
08:57:47 Commencing smooth kNN distance calibration using 1 thread
08:58:01 Initializing from normalized Laplacian + noise
08:58:01 Commencing optimization for 500 epochs, with 146262 positive edges
08:58:12 Optimization finished
[1] "105 0.05"08:58:12 UMAP embedding parameters a = 1.75 b = 0.8421
08:58:12 Read 1203 rows and found 38 numeric columns
08:58:12 Using Annoy for neighbor search, n_neighbors = 105
08:58:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:58:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87243e67ef
08:58:12 Searching Annoy index using 1 thread, search_k = 10500
08:58:13 Annoy recall = 100%
08:58:20 Commencing smooth kNN distance calibration using 1 thread
08:58:35 Initializing from normalized Laplacian + noise
08:58:35 Commencing optimization for 500 epochs, with 146262 positive edges
08:58:46 Optimization finished
[1] "105 0.06"08:58:46 UMAP embedding parameters a = 1.715 b = 0.8526
08:58:46 Read 1203 rows and found 38 numeric columns
08:58:46 Using Annoy for neighbor search, n_neighbors = 105
08:58:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:58:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873008a0cc
08:58:46 Searching Annoy index using 1 thread, search_k = 10500
08:58:47 Annoy recall = 100%
08:58:54 Commencing smooth kNN distance calibration using 1 thread
08:59:09 Initializing from normalized Laplacian + noise
08:59:09 Commencing optimization for 500 epochs, with 146262 positive edges
08:59:20 Optimization finished
[1] "105 0.07"08:59:20 UMAP embedding parameters a = 1.68 b = 0.8631
08:59:20 Read 1203 rows and found 38 numeric columns
08:59:20 Using Annoy for neighbor search, n_neighbors = 105
08:59:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:59:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873565f53e
08:59:20 Searching Annoy index using 1 thread, search_k = 10500
08:59:21 Annoy recall = 100%
08:59:28 Commencing smooth kNN distance calibration using 1 thread
08:59:43 Initializing from normalized Laplacian + noise
08:59:43 Commencing optimization for 500 epochs, with 146262 positive edges
08:59:54 Optimization finished
[1] "105 0.08"08:59:54 UMAP embedding parameters a = 1.645 b = 0.8737
08:59:54 Read 1203 rows and found 38 numeric columns
08:59:54 Using Annoy for neighbor search, n_neighbors = 105
08:59:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:59:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716238e00
08:59:54 Searching Annoy index using 1 thread, search_k = 10500
08:59:55 Annoy recall = 100%
09:00:02 Commencing smooth kNN distance calibration using 1 thread
09:00:17 Initializing from normalized Laplacian + noise
09:00:17 Commencing optimization for 500 epochs, with 146262 positive edges
09:00:28 Optimization finished
[1] "105 0.09"09:00:28 UMAP embedding parameters a = 1.611 b = 0.8844
09:00:28 Read 1203 rows and found 38 numeric columns
09:00:28 Using Annoy for neighbor search, n_neighbors = 105
09:00:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:00:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876325ae58
09:00:28 Searching Annoy index using 1 thread, search_k = 10500
09:00:29 Annoy recall = 100%
09:00:36 Commencing smooth kNN distance calibration using 1 thread
09:00:51 Initializing from normalized Laplacian + noise
09:00:51 Commencing optimization for 500 epochs, with 146262 positive edges
09:01:02 Optimization finished
[1] "105 0.1"09:01:02 UMAP embedding parameters a = 1.577 b = 0.8951
09:01:02 Read 1203 rows and found 38 numeric columns
09:01:02 Using Annoy for neighbor search, n_neighbors = 105
09:01:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:01:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e6cae1b
09:01:03 Searching Annoy index using 1 thread, search_k = 10500
09:01:03 Annoy recall = 100%
09:01:10 Commencing smooth kNN distance calibration using 1 thread
09:01:25 Initializing from normalized Laplacian + noise
09:01:25 Commencing optimization for 500 epochs, with 146262 positive edges
09:01:36 Optimization finished
[1] "105 0.11"09:01:36 UMAP embedding parameters a = 1.544 b = 0.9058
09:01:36 Read 1203 rows and found 38 numeric columns
09:01:36 Using Annoy for neighbor search, n_neighbors = 105
09:01:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:01:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bdd853d
09:01:36 Searching Annoy index using 1 thread, search_k = 10500
09:01:37 Annoy recall = 100%
09:01:44 Commencing smooth kNN distance calibration using 1 thread
09:01:59 Initializing from normalized Laplacian + noise
09:01:59 Commencing optimization for 500 epochs, with 146262 positive edges
09:02:10 Optimization finished
[1] "105 0.12"09:02:10 UMAP embedding parameters a = 1.51 b = 0.9165
09:02:10 Read 1203 rows and found 38 numeric columns
09:02:10 Using Annoy for neighbor search, n_neighbors = 105
09:02:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:02:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87521d12c4
09:02:11 Searching Annoy index using 1 thread, search_k = 10500
09:02:11 Annoy recall = 100%
09:02:18 Commencing smooth kNN distance calibration using 1 thread
09:02:33 Initializing from normalized Laplacian + noise
09:02:33 Commencing optimization for 500 epochs, with 146262 positive edges
09:02:44 Optimization finished
[1] "105 0.13"09:02:44 UMAP embedding parameters a = 1.478 b = 0.9272
09:02:44 Read 1203 rows and found 38 numeric columns
09:02:44 Using Annoy for neighbor search, n_neighbors = 105
09:02:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:02:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d49a80f
09:02:45 Searching Annoy index using 1 thread, search_k = 10500
09:02:45 Annoy recall = 100%
09:02:52 Commencing smooth kNN distance calibration using 1 thread
09:03:07 Initializing from normalized Laplacian + noise
09:03:07 Commencing optimization for 500 epochs, with 146262 positive edges
09:03:18 Optimization finished
[1] "105 0.14"09:03:18 UMAP embedding parameters a = 1.446 b = 0.938
09:03:18 Read 1203 rows and found 38 numeric columns
09:03:18 Using Annoy for neighbor search, n_neighbors = 105
09:03:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:03:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f8ffa7b
09:03:19 Searching Annoy index using 1 thread, search_k = 10500
09:03:19 Annoy recall = 100%
09:03:26 Commencing smooth kNN distance calibration using 1 thread
09:03:41 Initializing from normalized Laplacian + noise
09:03:41 Commencing optimization for 500 epochs, with 146262 positive edges
09:03:52 Optimization finished
[1] "105 0.15"09:03:52 UMAP embedding parameters a = 1.414 b = 0.9488
09:03:52 Read 1203 rows and found 38 numeric columns
09:03:52 Using Annoy for neighbor search, n_neighbors = 105
09:03:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:03:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768750cc0
09:03:52 Searching Annoy index using 1 thread, search_k = 10500
09:03:53 Annoy recall = 100%
09:04:00 Commencing smooth kNN distance calibration using 1 thread
09:04:15 Initializing from normalized Laplacian + noise
09:04:15 Commencing optimization for 500 epochs, with 146262 positive edges
09:04:26 Optimization finished
[1] "105 0.16"09:04:26 UMAP embedding parameters a = 1.383 b = 0.9596
09:04:26 Read 1203 rows and found 38 numeric columns
09:04:26 Using Annoy for neighbor search, n_neighbors = 105
09:04:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:04:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c8a938e
09:04:26 Searching Annoy index using 1 thread, search_k = 10500
09:04:27 Annoy recall = 100%
09:04:34 Commencing smooth kNN distance calibration using 1 thread
09:04:49 Initializing from normalized Laplacian + noise
09:04:49 Commencing optimization for 500 epochs, with 146262 positive edges
09:05:00 Optimization finished
[1] "105 0.17"09:05:00 UMAP embedding parameters a = 1.352 b = 0.9704
09:05:00 Read 1203 rows and found 38 numeric columns
09:05:00 Using Annoy for neighbor search, n_neighbors = 105
09:05:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:05:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744187f10
09:05:00 Searching Annoy index using 1 thread, search_k = 10500
09:05:01 Annoy recall = 100%
09:05:08 Commencing smooth kNN distance calibration using 1 thread
09:05:23 Initializing from normalized Laplacian + noise
09:05:23 Commencing optimization for 500 epochs, with 146262 positive edges
09:05:34 Optimization finished
[1] "105 0.18"09:05:34 UMAP embedding parameters a = 1.321 b = 0.9813
09:05:34 Read 1203 rows and found 38 numeric columns
09:05:34 Using Annoy for neighbor search, n_neighbors = 105
09:05:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:05:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f519102
09:05:35 Searching Annoy index using 1 thread, search_k = 10500
09:05:35 Annoy recall = 100%
09:05:42 Commencing smooth kNN distance calibration using 1 thread
09:05:57 Initializing from normalized Laplacian + noise
09:05:57 Commencing optimization for 500 epochs, with 146262 positive edges
09:06:08 Optimization finished
[1] "105 0.19"09:06:08 UMAP embedding parameters a = 1.292 b = 0.9921
09:06:08 Read 1203 rows and found 38 numeric columns
09:06:08 Using Annoy for neighbor search, n_neighbors = 105
09:06:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:06:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731fa4ff4
09:06:09 Searching Annoy index using 1 thread, search_k = 10500
09:06:10 Annoy recall = 100%
09:06:17 Commencing smooth kNN distance calibration using 1 thread
09:06:31 Initializing from normalized Laplacian + noise
09:06:31 Commencing optimization for 500 epochs, with 146262 positive edges
09:06:42 Optimization finished
[1] "105 0.2"09:06:42 UMAP embedding parameters a = 1.262 b = 1.003
09:06:42 Read 1203 rows and found 38 numeric columns
09:06:42 Using Annoy for neighbor search, n_neighbors = 105
09:06:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:06:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b7c3f06
09:06:43 Searching Annoy index using 1 thread, search_k = 10500
09:06:44 Annoy recall = 100%
09:06:51 Commencing smooth kNN distance calibration using 1 thread
09:07:06 Initializing from normalized Laplacian + noise
09:07:06 Commencing optimization for 500 epochs, with 146262 positive edges
09:07:17 Optimization finished
[1] "106 0"09:07:17 UMAP embedding parameters a = 1.933 b = 0.7905
09:07:17 Read 1203 rows and found 38 numeric columns
09:07:17 Using Annoy for neighbor search, n_neighbors = 106
09:07:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:07:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769c4fd22
09:07:18 Searching Annoy index using 1 thread, search_k = 10600
09:07:18 Annoy recall = 100%
09:07:25 Commencing smooth kNN distance calibration using 1 thread
09:07:40 Initializing from normalized Laplacian + noise
09:07:40 Commencing optimization for 500 epochs, with 147534 positive edges
09:07:51 Optimization finished
[1] "106 0.01"09:07:51 UMAP embedding parameters a = 1.896 b = 0.8006
09:07:51 Read 1203 rows and found 38 numeric columns
09:07:51 Using Annoy for neighbor search, n_neighbors = 106
09:07:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:07:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761fe4c84
09:07:52 Searching Annoy index using 1 thread, search_k = 10600
09:07:53 Annoy recall = 100%
09:08:00 Commencing smooth kNN distance calibration using 1 thread
09:08:14 Initializing from normalized Laplacian + noise
09:08:14 Commencing optimization for 500 epochs, with 147534 positive edges
09:08:26 Optimization finished
[1] "106 0.02"09:08:26 UMAP embedding parameters a = 1.859 b = 0.8109
09:08:26 Read 1203 rows and found 38 numeric columns
09:08:26 Using Annoy for neighbor search, n_neighbors = 106
09:08:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:08:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87477b12a
09:08:26 Searching Annoy index using 1 thread, search_k = 10600
09:08:27 Annoy recall = 100%
09:08:34 Commencing smooth kNN distance calibration using 1 thread
09:08:49 Initializing from normalized Laplacian + noise
09:08:49 Commencing optimization for 500 epochs, with 147534 positive edges
09:09:00 Optimization finished
[1] "106 0.03"09:09:00 UMAP embedding parameters a = 1.822 b = 0.8212
09:09:00 Read 1203 rows and found 38 numeric columns
09:09:00 Using Annoy for neighbor search, n_neighbors = 106
09:09:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:09:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ce1c634
09:09:01 Searching Annoy index using 1 thread, search_k = 10600
09:09:02 Annoy recall = 100%
09:09:10 Commencing smooth kNN distance calibration using 1 thread
09:09:26 Initializing from normalized Laplacian + noise
09:09:26 Commencing optimization for 500 epochs, with 147534 positive edges
09:09:39 Optimization finished
[1] "106 0.04"09:09:39 UMAP embedding parameters a = 1.786 b = 0.8316
09:09:39 Read 1203 rows and found 38 numeric columns
09:09:39 Using Annoy for neighbor search, n_neighbors = 106
09:09:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:09:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87891dc17
09:09:40 Searching Annoy index using 1 thread, search_k = 10600
09:09:41 Annoy recall = 100%
09:09:49 Commencing smooth kNN distance calibration using 1 thread
09:10:05 Initializing from normalized Laplacian + noise
09:10:06 Commencing optimization for 500 epochs, with 147534 positive edges
09:10:17 Optimization finished
[1] "106 0.05"09:10:18 UMAP embedding parameters a = 1.75 b = 0.8421
09:10:18 Read 1203 rows and found 38 numeric columns
09:10:18 Using Annoy for neighbor search, n_neighbors = 106
09:10:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:10:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d9df804
09:10:18 Searching Annoy index using 1 thread, search_k = 10600
09:10:19 Annoy recall = 100%
09:10:27 Commencing smooth kNN distance calibration using 1 thread
09:10:44 Initializing from normalized Laplacian + noise
09:10:44 Commencing optimization for 500 epochs, with 147534 positive edges
09:10:57 Optimization finished
[1] "106 0.06"09:10:57 UMAP embedding parameters a = 1.715 b = 0.8526
09:10:57 Read 1203 rows and found 38 numeric columns
09:10:57 Using Annoy for neighbor search, n_neighbors = 106
09:10:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:10:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87123e20e9
09:10:58 Searching Annoy index using 1 thread, search_k = 10600
09:10:59 Annoy recall = 100%
09:11:08 Commencing smooth kNN distance calibration using 1 thread
09:11:26 Initializing from normalized Laplacian + noise
09:11:26 Commencing optimization for 500 epochs, with 147534 positive edges
09:11:39 Optimization finished
[1] "106 0.07"09:11:39 UMAP embedding parameters a = 1.68 b = 0.8631
09:11:39 Read 1203 rows and found 38 numeric columns
09:11:39 Using Annoy for neighbor search, n_neighbors = 106
09:11:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:11:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c97ff06
09:11:40 Searching Annoy index using 1 thread, search_k = 10600
09:11:41 Annoy recall = 100%
09:11:49 Commencing smooth kNN distance calibration using 1 thread
09:12:07 Initializing from normalized Laplacian + noise
09:12:07 Commencing optimization for 500 epochs, with 147534 positive edges
09:12:21 Optimization finished
[1] "106 0.08"09:12:21 UMAP embedding parameters a = 1.645 b = 0.8737
09:12:21 Read 1203 rows and found 38 numeric columns
09:12:21 Using Annoy for neighbor search, n_neighbors = 106
09:12:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:12:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873597dc60
09:12:22 Searching Annoy index using 1 thread, search_k = 10600
09:12:23 Annoy recall = 100%
09:12:31 Commencing smooth kNN distance calibration using 1 thread
09:12:47 Initializing from normalized Laplacian + noise
09:12:47 Commencing optimization for 500 epochs, with 147534 positive edges
09:13:00 Optimization finished
[1] "106 0.09"09:13:00 UMAP embedding parameters a = 1.611 b = 0.8844
09:13:00 Read 1203 rows and found 38 numeric columns
09:13:00 Using Annoy for neighbor search, n_neighbors = 106
09:13:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:13:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87146ef3fb
09:13:01 Searching Annoy index using 1 thread, search_k = 10600
09:13:01 Annoy recall = 100%
09:13:09 Commencing smooth kNN distance calibration using 1 thread
09:13:25 Initializing from normalized Laplacian + noise
09:13:25 Commencing optimization for 500 epochs, with 147534 positive edges
09:13:38 Optimization finished
[1] "106 0.1"09:13:38 UMAP embedding parameters a = 1.577 b = 0.8951
09:13:38 Read 1203 rows and found 38 numeric columns
09:13:38 Using Annoy for neighbor search, n_neighbors = 106
09:13:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:13:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878ff327
09:13:39 Searching Annoy index using 1 thread, search_k = 10600
09:13:40 Annoy recall = 100%
09:13:48 Commencing smooth kNN distance calibration using 1 thread
09:14:05 Initializing from normalized Laplacian + noise
09:14:05 Commencing optimization for 500 epochs, with 147534 positive edges
09:14:16 Optimization finished
[1] "106 0.11"09:14:17 UMAP embedding parameters a = 1.544 b = 0.9058
09:14:17 Read 1203 rows and found 38 numeric columns
09:14:17 Using Annoy for neighbor search, n_neighbors = 106
09:14:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:14:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872eb397d4
09:14:17 Searching Annoy index using 1 thread, search_k = 10600
09:14:18 Annoy recall = 100%
09:14:26 Commencing smooth kNN distance calibration using 1 thread
09:14:41 Initializing from normalized Laplacian + noise
09:14:41 Commencing optimization for 500 epochs, with 147534 positive edges
09:14:52 Optimization finished
[1] "106 0.12"09:14:53 UMAP embedding parameters a = 1.51 b = 0.9165
09:14:53 Read 1203 rows and found 38 numeric columns
09:14:53 Using Annoy for neighbor search, n_neighbors = 106
09:14:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:14:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c13cd69
09:14:53 Searching Annoy index using 1 thread, search_k = 10600
09:14:54 Annoy recall = 100%
09:15:02 Commencing smooth kNN distance calibration using 1 thread
09:15:16 Initializing from normalized Laplacian + noise
09:15:17 Commencing optimization for 500 epochs, with 147534 positive edges
09:15:28 Optimization finished
[1] "106 0.13"09:15:29 UMAP embedding parameters a = 1.478 b = 0.9272
09:15:29 Read 1203 rows and found 38 numeric columns
09:15:29 Using Annoy for neighbor search, n_neighbors = 106
09:15:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:15:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b9ac58f
09:15:29 Searching Annoy index using 1 thread, search_k = 10600
09:15:30 Annoy recall = 100%
09:15:40 Commencing smooth kNN distance calibration using 1 thread
09:15:57 Initializing from normalized Laplacian + noise
09:15:57 Commencing optimization for 500 epochs, with 147534 positive edges
09:16:11 Optimization finished
[1] "106 0.14"09:16:11 UMAP embedding parameters a = 1.446 b = 0.938
09:16:11 Read 1203 rows and found 38 numeric columns
09:16:11 Using Annoy for neighbor search, n_neighbors = 106
09:16:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:16:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719a36755
09:16:11 Searching Annoy index using 1 thread, search_k = 10600
09:16:12 Annoy recall = 100%
09:16:21 Commencing smooth kNN distance calibration using 1 thread
09:16:38 Initializing from normalized Laplacian + noise
09:16:38 Commencing optimization for 500 epochs, with 147534 positive edges
09:16:51 Optimization finished
[1] "106 0.15"09:16:51 UMAP embedding parameters a = 1.414 b = 0.9488
09:16:51 Read 1203 rows and found 38 numeric columns
09:16:51 Using Annoy for neighbor search, n_neighbors = 106
09:16:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:16:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750523559
09:16:51 Searching Annoy index using 1 thread, search_k = 10600
09:16:52 Annoy recall = 100%
09:17:00 Commencing smooth kNN distance calibration using 1 thread
09:17:16 Initializing from normalized Laplacian + noise
09:17:16 Commencing optimization for 500 epochs, with 147534 positive edges
09:17:29 Optimization finished
[1] "106 0.16"09:17:30 UMAP embedding parameters a = 1.383 b = 0.9596
09:17:30 Read 1203 rows and found 38 numeric columns
09:17:30 Using Annoy for neighbor search, n_neighbors = 106
09:17:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:17:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ba3665b
09:17:30 Searching Annoy index using 1 thread, search_k = 10600
09:17:31 Annoy recall = 100%
09:17:39 Commencing smooth kNN distance calibration using 1 thread
09:17:55 Initializing from normalized Laplacian + noise
09:17:55 Commencing optimization for 500 epochs, with 147534 positive edges
09:18:08 Optimization finished
[1] "106 0.17"09:18:08 UMAP embedding parameters a = 1.352 b = 0.9704
09:18:08 Read 1203 rows and found 38 numeric columns
09:18:08 Using Annoy for neighbor search, n_neighbors = 106
09:18:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:18:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f095c94
09:18:09 Searching Annoy index using 1 thread, search_k = 10600
09:18:09 Annoy recall = 100%
09:18:17 Commencing smooth kNN distance calibration using 1 thread
09:18:32 Initializing from normalized Laplacian + noise
09:18:32 Commencing optimization for 500 epochs, with 147534 positive edges
09:18:44 Optimization finished
[1] "106 0.18"09:18:44 UMAP embedding parameters a = 1.321 b = 0.9813
09:18:44 Read 1203 rows and found 38 numeric columns
09:18:44 Using Annoy for neighbor search, n_neighbors = 106
09:18:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:18:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876675c359
09:18:45 Searching Annoy index using 1 thread, search_k = 10600
09:18:45 Annoy recall = 100%
09:18:53 Commencing smooth kNN distance calibration using 1 thread
09:19:08 Initializing from normalized Laplacian + noise
09:19:08 Commencing optimization for 500 epochs, with 147534 positive edges
09:19:20 Optimization finished
[1] "106 0.19"09:19:20 UMAP embedding parameters a = 1.292 b = 0.9921
09:19:20 Read 1203 rows and found 38 numeric columns
09:19:20 Using Annoy for neighbor search, n_neighbors = 106
09:19:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:19:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ec914b4
09:19:20 Searching Annoy index using 1 thread, search_k = 10600
09:19:21 Annoy recall = 100%
09:19:29 Commencing smooth kNN distance calibration using 1 thread
09:19:44 Initializing from normalized Laplacian + noise
09:19:44 Commencing optimization for 500 epochs, with 147534 positive edges
09:19:57 Optimization finished
[1] "106 0.2"09:19:57 UMAP embedding parameters a = 1.262 b = 1.003
09:19:57 Read 1203 rows and found 38 numeric columns
09:19:57 Using Annoy for neighbor search, n_neighbors = 106
09:19:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:19:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d760aaf
09:19:58 Searching Annoy index using 1 thread, search_k = 10600
09:19:58 Annoy recall = 100%
09:20:08 Commencing smooth kNN distance calibration using 1 thread
09:20:27 Initializing from normalized Laplacian + noise
09:20:27 Commencing optimization for 500 epochs, with 147534 positive edges
09:20:40 Optimization finished
[1] "107 0"09:20:40 UMAP embedding parameters a = 1.933 b = 0.7905
09:20:40 Read 1203 rows and found 38 numeric columns
09:20:40 Using Annoy for neighbor search, n_neighbors = 107
09:20:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:20:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772534896
09:20:41 Searching Annoy index using 1 thread, search_k = 10700
09:20:42 Annoy recall = 100%
09:20:50 Commencing smooth kNN distance calibration using 1 thread
09:21:07 Initializing from normalized Laplacian + noise
09:21:07 Commencing optimization for 500 epochs, with 148760 positive edges
09:21:21 Optimization finished
[1] "107 0.01"09:21:21 UMAP embedding parameters a = 1.896 b = 0.8006
09:21:21 Read 1203 rows and found 38 numeric columns
09:21:21 Using Annoy for neighbor search, n_neighbors = 107
09:21:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:21:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730e62778
09:21:22 Searching Annoy index using 1 thread, search_k = 10700
09:21:22 Annoy recall = 100%
09:21:30 Commencing smooth kNN distance calibration using 1 thread
09:21:46 Initializing from normalized Laplacian + noise
09:21:46 Commencing optimization for 500 epochs, with 148760 positive edges
09:21:58 Optimization finished
[1] "107 0.02"09:21:58 UMAP embedding parameters a = 1.859 b = 0.8109
09:21:58 Read 1203 rows and found 38 numeric columns
09:21:58 Using Annoy for neighbor search, n_neighbors = 107
09:21:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:21:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876abfb2bf
09:21:59 Searching Annoy index using 1 thread, search_k = 10700
09:22:00 Annoy recall = 100%
09:22:07 Commencing smooth kNN distance calibration using 1 thread
09:22:22 Initializing from normalized Laplacian + noise
09:22:22 Commencing optimization for 500 epochs, with 148760 positive edges
09:22:34 Optimization finished
[1] "107 0.03"09:22:34 UMAP embedding parameters a = 1.822 b = 0.8212
09:22:34 Read 1203 rows and found 38 numeric columns
09:22:34 Using Annoy for neighbor search, n_neighbors = 107
09:22:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:22:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771e34311
09:22:35 Searching Annoy index using 1 thread, search_k = 10700
09:22:36 Annoy recall = 100%
09:22:43 Commencing smooth kNN distance calibration using 1 thread
09:23:00 Initializing from normalized Laplacian + noise
09:23:00 Commencing optimization for 500 epochs, with 148760 positive edges
09:23:13 Optimization finished
[1] "107 0.04"09:23:13 UMAP embedding parameters a = 1.786 b = 0.8316
09:23:13 Read 1203 rows and found 38 numeric columns
09:23:13 Using Annoy for neighbor search, n_neighbors = 107
09:23:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:23:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87195b3439
09:23:14 Searching Annoy index using 1 thread, search_k = 10700
09:23:15 Annoy recall = 100%
09:23:24 Commencing smooth kNN distance calibration using 1 thread
09:23:40 Initializing from normalized Laplacian + noise
09:23:40 Commencing optimization for 500 epochs, with 148760 positive edges
09:23:52 Optimization finished
[1] "107 0.05"09:23:52 UMAP embedding parameters a = 1.75 b = 0.8421
09:23:53 Read 1203 rows and found 38 numeric columns
09:23:53 Using Annoy for neighbor search, n_neighbors = 107
09:23:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:23:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87574a464d
09:23:53 Searching Annoy index using 1 thread, search_k = 10700
09:23:54 Annoy recall = 100%
09:24:04 Commencing smooth kNN distance calibration using 1 thread
09:24:22 Initializing from normalized Laplacian + noise
09:24:22 Commencing optimization for 500 epochs, with 148760 positive edges
09:24:35 Optimization finished
[1] "107 0.06"09:24:36 UMAP embedding parameters a = 1.715 b = 0.8526
09:24:36 Read 1203 rows and found 38 numeric columns
09:24:36 Using Annoy for neighbor search, n_neighbors = 107
09:24:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:24:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735fbc222
09:24:36 Searching Annoy index using 1 thread, search_k = 10700
09:24:37 Annoy recall = 100%
09:24:46 Commencing smooth kNN distance calibration using 1 thread
09:25:02 Initializing from normalized Laplacian + noise
09:25:02 Commencing optimization for 500 epochs, with 148760 positive edges
09:25:14 Optimization finished
[1] "107 0.07"09:25:14 UMAP embedding parameters a = 1.68 b = 0.8631
09:25:14 Read 1203 rows and found 38 numeric columns
09:25:15 Using Annoy for neighbor search, n_neighbors = 107
09:25:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:25:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878acc53b
09:25:15 Searching Annoy index using 1 thread, search_k = 10700
09:25:16 Annoy recall = 100%
09:25:24 Commencing smooth kNN distance calibration using 1 thread
09:25:39 Initializing from normalized Laplacian + noise
09:25:39 Commencing optimization for 500 epochs, with 148760 positive edges
09:25:50 Optimization finished
[1] "107 0.08"09:25:51 UMAP embedding parameters a = 1.645 b = 0.8737
09:25:51 Read 1203 rows and found 38 numeric columns
09:25:51 Using Annoy for neighbor search, n_neighbors = 107
09:25:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:25:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879449642
09:25:51 Searching Annoy index using 1 thread, search_k = 10700
09:25:52 Annoy recall = 100%
09:25:59 Commencing smooth kNN distance calibration using 1 thread
09:26:14 Initializing from normalized Laplacian + noise
09:26:15 Commencing optimization for 500 epochs, with 148760 positive edges
09:26:26 Optimization finished
[1] "107 0.09"09:26:26 UMAP embedding parameters a = 1.611 b = 0.8844
09:26:26 Read 1203 rows and found 38 numeric columns
09:26:26 Using Annoy for neighbor search, n_neighbors = 107
09:26:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:26:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711780128
09:26:27 Searching Annoy index using 1 thread, search_k = 10700
09:26:27 Annoy recall = 100%
09:26:35 Commencing smooth kNN distance calibration using 1 thread
09:26:51 Initializing from normalized Laplacian + noise
09:26:51 Commencing optimization for 500 epochs, with 148760 positive edges
09:27:04 Optimization finished
[1] "107 0.1"09:27:04 UMAP embedding parameters a = 1.577 b = 0.8951
09:27:04 Read 1203 rows and found 38 numeric columns
09:27:04 Using Annoy for neighbor search, n_neighbors = 107
09:27:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:27:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877271c25d
09:27:05 Searching Annoy index using 1 thread, search_k = 10700
09:27:06 Annoy recall = 100%
09:27:13 Commencing smooth kNN distance calibration using 1 thread
09:27:30 Initializing from normalized Laplacian + noise
09:27:31 Commencing optimization for 500 epochs, with 148760 positive edges
09:27:43 Optimization finished
[1] "107 0.11"09:27:43 UMAP embedding parameters a = 1.544 b = 0.9058
09:27:43 Read 1203 rows and found 38 numeric columns
09:27:43 Using Annoy for neighbor search, n_neighbors = 107
09:27:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:27:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b42e2c6
09:27:44 Searching Annoy index using 1 thread, search_k = 10700
09:27:45 Annoy recall = 100%
09:27:54 Commencing smooth kNN distance calibration using 1 thread
09:28:12 Initializing from normalized Laplacian + noise
09:28:12 Commencing optimization for 500 epochs, with 148760 positive edges
09:28:26 Optimization finished
[1] "107 0.12"09:28:26 UMAP embedding parameters a = 1.51 b = 0.9165
09:28:26 Read 1203 rows and found 38 numeric columns
09:28:26 Using Annoy for neighbor search, n_neighbors = 107
09:28:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:28:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715efb252
09:28:27 Searching Annoy index using 1 thread, search_k = 10700
09:28:28 Annoy recall = 100%
09:28:37 Commencing smooth kNN distance calibration using 1 thread
09:28:53 Initializing from normalized Laplacian + noise
09:28:53 Commencing optimization for 500 epochs, with 148760 positive edges
09:29:05 Optimization finished
[1] "107 0.13"09:29:05 UMAP embedding parameters a = 1.478 b = 0.9272
09:29:05 Read 1203 rows and found 38 numeric columns
09:29:05 Using Annoy for neighbor search, n_neighbors = 107
09:29:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:29:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f538891
09:29:06 Searching Annoy index using 1 thread, search_k = 10700
09:29:07 Annoy recall = 100%
09:29:14 Commencing smooth kNN distance calibration using 1 thread
09:29:29 Initializing from normalized Laplacian + noise
09:29:29 Commencing optimization for 500 epochs, with 148760 positive edges
09:29:40 Optimization finished
[1] "107 0.14"09:29:40 UMAP embedding parameters a = 1.446 b = 0.938
09:29:40 Read 1203 rows and found 38 numeric columns
09:29:40 Using Annoy for neighbor search, n_neighbors = 107
09:29:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:29:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773d4bedd
09:29:41 Searching Annoy index using 1 thread, search_k = 10700
09:29:42 Annoy recall = 100%
09:29:49 Commencing smooth kNN distance calibration using 1 thread
09:30:04 Initializing from normalized Laplacian + noise
09:30:04 Commencing optimization for 500 epochs, with 148760 positive edges
09:30:15 Optimization finished
[1] "107 0.15"09:30:15 UMAP embedding parameters a = 1.414 b = 0.9488
09:30:15 Read 1203 rows and found 38 numeric columns
09:30:15 Using Annoy for neighbor search, n_neighbors = 107
09:30:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:30:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87538daa57
09:30:16 Searching Annoy index using 1 thread, search_k = 10700
09:30:17 Annoy recall = 100%
09:30:24 Commencing smooth kNN distance calibration using 1 thread
09:30:38 Initializing from normalized Laplacian + noise
09:30:39 Commencing optimization for 500 epochs, with 148760 positive edges
09:30:50 Optimization finished
[1] "107 0.16"09:30:50 UMAP embedding parameters a = 1.383 b = 0.9596
09:30:50 Read 1203 rows and found 38 numeric columns
09:30:50 Using Annoy for neighbor search, n_neighbors = 107
09:30:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:30:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877191a97a
09:30:50 Searching Annoy index using 1 thread, search_k = 10700
09:30:51 Annoy recall = 100%
09:30:59 Commencing smooth kNN distance calibration using 1 thread
09:31:13 Initializing from normalized Laplacian + noise
09:31:13 Commencing optimization for 500 epochs, with 148760 positive edges
09:31:24 Optimization finished
[1] "107 0.17"09:31:24 UMAP embedding parameters a = 1.352 b = 0.9704
09:31:24 Read 1203 rows and found 38 numeric columns
09:31:24 Using Annoy for neighbor search, n_neighbors = 107
09:31:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:31:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87306cbde4
09:31:25 Searching Annoy index using 1 thread, search_k = 10700
09:31:26 Annoy recall = 100%
09:31:33 Commencing smooth kNN distance calibration using 1 thread
09:31:48 Initializing from normalized Laplacian + noise
09:31:48 Commencing optimization for 500 epochs, with 148760 positive edges
09:31:59 Optimization finished
[1] "107 0.18"09:31:59 UMAP embedding parameters a = 1.321 b = 0.9813
09:31:59 Read 1203 rows and found 38 numeric columns
09:31:59 Using Annoy for neighbor search, n_neighbors = 107
09:31:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:32:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8792586b7
09:32:00 Searching Annoy index using 1 thread, search_k = 10700
09:32:00 Annoy recall = 100%
09:32:08 Commencing smooth kNN distance calibration using 1 thread
09:32:22 Initializing from normalized Laplacian + noise
09:32:22 Commencing optimization for 500 epochs, with 148760 positive edges
09:32:33 Optimization finished
[1] "107 0.19"09:32:33 UMAP embedding parameters a = 1.292 b = 0.9921
09:32:33 Read 1203 rows and found 38 numeric columns
09:32:33 Using Annoy for neighbor search, n_neighbors = 107
09:32:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:32:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876009d75
09:32:34 Searching Annoy index using 1 thread, search_k = 10700
09:32:35 Annoy recall = 100%
09:32:42 Commencing smooth kNN distance calibration using 1 thread
09:32:57 Initializing from normalized Laplacian + noise
09:32:57 Commencing optimization for 500 epochs, with 148760 positive edges
09:33:08 Optimization finished
[1] "107 0.2"09:33:08 UMAP embedding parameters a = 1.262 b = 1.003
09:33:08 Read 1203 rows and found 38 numeric columns
09:33:08 Using Annoy for neighbor search, n_neighbors = 107
09:33:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:33:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730fcb10b
09:33:09 Searching Annoy index using 1 thread, search_k = 10700
09:33:10 Annoy recall = 100%
09:33:17 Commencing smooth kNN distance calibration using 1 thread
09:33:31 Initializing from normalized Laplacian + noise
09:33:31 Commencing optimization for 500 epochs, with 148760 positive edges
09:33:42 Optimization finished
[1] "108 0"09:33:43 UMAP embedding parameters a = 1.933 b = 0.7905
09:33:43 Read 1203 rows and found 38 numeric columns
09:33:43 Using Annoy for neighbor search, n_neighbors = 108
09:33:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:33:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737d91e8b
09:33:43 Searching Annoy index using 1 thread, search_k = 10800
09:33:44 Annoy recall = 100%
09:33:51 Commencing smooth kNN distance calibration using 1 thread
09:34:06 Initializing from normalized Laplacian + noise
09:34:06 Commencing optimization for 500 epochs, with 150060 positive edges
09:34:17 Optimization finished
[1] "108 0.01"09:34:17 UMAP embedding parameters a = 1.896 b = 0.8006
09:34:17 Read 1203 rows and found 38 numeric columns
09:34:17 Using Annoy for neighbor search, n_neighbors = 108
09:34:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:34:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732146adf
09:34:18 Searching Annoy index using 1 thread, search_k = 10800
09:34:19 Annoy recall = 100%
09:34:26 Commencing smooth kNN distance calibration using 1 thread
09:34:40 Initializing from normalized Laplacian + noise
09:34:40 Commencing optimization for 500 epochs, with 150060 positive edges
09:34:52 Optimization finished
[1] "108 0.02"09:34:52 UMAP embedding parameters a = 1.859 b = 0.8109
09:34:52 Read 1203 rows and found 38 numeric columns
09:34:52 Using Annoy for neighbor search, n_neighbors = 108
09:34:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:34:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c97769a
09:34:52 Searching Annoy index using 1 thread, search_k = 10800
09:34:53 Annoy recall = 100%
09:35:01 Commencing smooth kNN distance calibration using 1 thread
09:35:15 Initializing from normalized Laplacian + noise
09:35:15 Commencing optimization for 500 epochs, with 150060 positive edges
09:35:26 Optimization finished
[1] "108 0.03"09:35:27 UMAP embedding parameters a = 1.822 b = 0.8212
09:35:27 Read 1203 rows and found 38 numeric columns
09:35:27 Using Annoy for neighbor search, n_neighbors = 108
09:35:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:35:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87517c85e1
09:35:27 Searching Annoy index using 1 thread, search_k = 10800
09:35:28 Annoy recall = 100%
09:35:35 Commencing smooth kNN distance calibration using 1 thread
09:35:50 Initializing from normalized Laplacian + noise
09:35:50 Commencing optimization for 500 epochs, with 150060 positive edges
09:36:01 Optimization finished
[1] "108 0.04"09:36:01 UMAP embedding parameters a = 1.786 b = 0.8316
09:36:01 Read 1203 rows and found 38 numeric columns
09:36:01 Using Annoy for neighbor search, n_neighbors = 108
09:36:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:36:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87266a038
09:36:02 Searching Annoy index using 1 thread, search_k = 10800
09:36:03 Annoy recall = 100%
09:36:10 Commencing smooth kNN distance calibration using 1 thread
09:36:24 Initializing from normalized Laplacian + noise
09:36:25 Commencing optimization for 500 epochs, with 150060 positive edges
09:36:36 Optimization finished
[1] "108 0.05"09:36:36 UMAP embedding parameters a = 1.75 b = 0.8421
09:36:36 Read 1203 rows and found 38 numeric columns
09:36:36 Using Annoy for neighbor search, n_neighbors = 108
09:36:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:36:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87783adcf6
09:36:36 Searching Annoy index using 1 thread, search_k = 10800
09:36:37 Annoy recall = 100%
09:36:44 Commencing smooth kNN distance calibration using 1 thread
09:36:59 Initializing from normalized Laplacian + noise
09:36:59 Commencing optimization for 500 epochs, with 150060 positive edges
09:37:10 Optimization finished
[1] "108 0.06"09:37:11 UMAP embedding parameters a = 1.715 b = 0.8526
09:37:11 Read 1203 rows and found 38 numeric columns
09:37:11 Using Annoy for neighbor search, n_neighbors = 108
09:37:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:37:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872085e275
09:37:11 Searching Annoy index using 1 thread, search_k = 10800
09:37:12 Annoy recall = 100%
09:37:19 Commencing smooth kNN distance calibration using 1 thread
09:37:34 Initializing from normalized Laplacian + noise
09:37:34 Commencing optimization for 500 epochs, with 150060 positive edges
09:37:45 Optimization finished
[1] "108 0.07"09:37:45 UMAP embedding parameters a = 1.68 b = 0.8631
09:37:45 Read 1203 rows and found 38 numeric columns
09:37:45 Using Annoy for neighbor search, n_neighbors = 108
09:37:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:37:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768dc6391
09:37:46 Searching Annoy index using 1 thread, search_k = 10800
09:37:46 Annoy recall = 100%
09:37:54 Commencing smooth kNN distance calibration using 1 thread
09:38:08 Initializing from normalized Laplacian + noise
09:38:09 Commencing optimization for 500 epochs, with 150060 positive edges
09:38:20 Optimization finished
[1] "108 0.08"09:38:20 UMAP embedding parameters a = 1.645 b = 0.8737
09:38:20 Read 1203 rows and found 38 numeric columns
09:38:20 Using Annoy for neighbor search, n_neighbors = 108
09:38:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:38:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875703f1aa
09:38:21 Searching Annoy index using 1 thread, search_k = 10800
09:38:21 Annoy recall = 100%
09:38:29 Commencing smooth kNN distance calibration using 1 thread
09:38:44 Initializing from normalized Laplacian + noise
09:38:44 Commencing optimization for 500 epochs, with 150060 positive edges
09:38:55 Optimization finished
[1] "108 0.09"09:38:55 UMAP embedding parameters a = 1.611 b = 0.8844
09:38:55 Read 1203 rows and found 38 numeric columns
09:38:55 Using Annoy for neighbor search, n_neighbors = 108
09:38:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:38:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876dfbed24
09:38:56 Searching Annoy index using 1 thread, search_k = 10800
09:38:57 Annoy recall = 100%
09:39:04 Commencing smooth kNN distance calibration using 1 thread
09:39:19 Initializing from normalized Laplacian + noise
09:39:19 Commencing optimization for 500 epochs, with 150060 positive edges
09:39:31 Optimization finished
[1] "108 0.1"09:39:31 UMAP embedding parameters a = 1.577 b = 0.8951
09:39:31 Read 1203 rows and found 38 numeric columns
09:39:31 Using Annoy for neighbor search, n_neighbors = 108
09:39:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:39:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b2fac28
09:39:31 Searching Annoy index using 1 thread, search_k = 10800
09:39:32 Annoy recall = 100%
09:39:40 Commencing smooth kNN distance calibration using 1 thread
09:39:55 Initializing from normalized Laplacian + noise
09:39:55 Commencing optimization for 500 epochs, with 150060 positive edges
09:40:06 Optimization finished
[1] "108 0.11"09:40:06 UMAP embedding parameters a = 1.544 b = 0.9058
09:40:06 Read 1203 rows and found 38 numeric columns
09:40:06 Using Annoy for neighbor search, n_neighbors = 108
09:40:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:40:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ea1922
09:40:07 Searching Annoy index using 1 thread, search_k = 10800
09:40:08 Annoy recall = 100%
09:40:15 Commencing smooth kNN distance calibration using 1 thread
09:40:30 Initializing from normalized Laplacian + noise
09:40:30 Commencing optimization for 500 epochs, with 150060 positive edges
09:40:42 Optimization finished
[1] "108 0.12"09:40:42 UMAP embedding parameters a = 1.51 b = 0.9165
09:40:42 Read 1203 rows and found 38 numeric columns
09:40:42 Using Annoy for neighbor search, n_neighbors = 108
09:40:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:40:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758bb9fe3
09:40:42 Searching Annoy index using 1 thread, search_k = 10800
09:40:43 Annoy recall = 100%
09:40:51 Commencing smooth kNN distance calibration using 1 thread
09:41:06 Initializing from normalized Laplacian + noise
09:41:06 Commencing optimization for 500 epochs, with 150060 positive edges
09:41:17 Optimization finished
[1] "108 0.13"09:41:17 UMAP embedding parameters a = 1.478 b = 0.9272
09:41:17 Read 1203 rows and found 38 numeric columns
09:41:17 Using Annoy for neighbor search, n_neighbors = 108
09:41:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:41:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d12ef39
09:41:18 Searching Annoy index using 1 thread, search_k = 10800
09:41:19 Annoy recall = 100%
09:41:26 Commencing smooth kNN distance calibration using 1 thread
09:41:41 Initializing from normalized Laplacian + noise
09:41:41 Commencing optimization for 500 epochs, with 150060 positive edges
09:41:53 Optimization finished
[1] "108 0.14"09:41:53 UMAP embedding parameters a = 1.446 b = 0.938
09:41:53 Read 1203 rows and found 38 numeric columns
09:41:53 Using Annoy for neighbor search, n_neighbors = 108
09:41:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:41:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721454d5b
09:41:53 Searching Annoy index using 1 thread, search_k = 10800
09:41:54 Annoy recall = 100%
09:42:02 Commencing smooth kNN distance calibration using 1 thread
09:42:17 Initializing from normalized Laplacian + noise
09:42:17 Commencing optimization for 500 epochs, with 150060 positive edges
09:42:28 Optimization finished
[1] "108 0.15"09:42:29 UMAP embedding parameters a = 1.414 b = 0.9488
09:42:29 Read 1203 rows and found 38 numeric columns
09:42:29 Using Annoy for neighbor search, n_neighbors = 108
09:42:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:42:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873005e631
09:42:29 Searching Annoy index using 1 thread, search_k = 10800
09:42:30 Annoy recall = 100%
09:42:37 Commencing smooth kNN distance calibration using 1 thread
09:42:53 Initializing from normalized Laplacian + noise
09:42:53 Commencing optimization for 500 epochs, with 150060 positive edges
09:43:04 Optimization finished
[1] "108 0.16"09:43:04 UMAP embedding parameters a = 1.383 b = 0.9596
09:43:04 Read 1203 rows and found 38 numeric columns
09:43:04 Using Annoy for neighbor search, n_neighbors = 108
09:43:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:43:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730eb15b
09:43:05 Searching Annoy index using 1 thread, search_k = 10800
09:43:06 Annoy recall = 100%
09:43:13 Commencing smooth kNN distance calibration using 1 thread
09:43:28 Initializing from normalized Laplacian + noise
09:43:28 Commencing optimization for 500 epochs, with 150060 positive edges
09:43:40 Optimization finished
[1] "108 0.17"09:43:40 UMAP embedding parameters a = 1.352 b = 0.9704
09:43:40 Read 1203 rows and found 38 numeric columns
09:43:40 Using Annoy for neighbor search, n_neighbors = 108
09:43:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:43:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729f21296
09:43:41 Searching Annoy index using 1 thread, search_k = 10800
09:43:41 Annoy recall = 100%
09:43:49 Commencing smooth kNN distance calibration using 1 thread
09:44:04 Initializing from normalized Laplacian + noise
09:44:04 Commencing optimization for 500 epochs, with 150060 positive edges
09:44:15 Optimization finished
[1] "108 0.18"09:44:16 UMAP embedding parameters a = 1.321 b = 0.9813
09:44:16 Read 1203 rows and found 38 numeric columns
09:44:16 Using Annoy for neighbor search, n_neighbors = 108
09:44:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:44:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87394a7c73
09:44:16 Searching Annoy index using 1 thread, search_k = 10800
09:44:17 Annoy recall = 100%
09:44:24 Commencing smooth kNN distance calibration using 1 thread
09:44:40 Initializing from normalized Laplacian + noise
09:44:40 Commencing optimization for 500 epochs, with 150060 positive edges
09:44:51 Optimization finished
[1] "108 0.19"09:44:51 UMAP embedding parameters a = 1.292 b = 0.9921
09:44:51 Read 1203 rows and found 38 numeric columns
09:44:51 Using Annoy for neighbor search, n_neighbors = 108
09:44:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:44:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871486b283
09:44:52 Searching Annoy index using 1 thread, search_k = 10800
09:44:53 Annoy recall = 100%
09:45:00 Commencing smooth kNN distance calibration using 1 thread
09:45:15 Initializing from normalized Laplacian + noise
09:45:15 Commencing optimization for 500 epochs, with 150060 positive edges
09:45:27 Optimization finished
[1] "108 0.2"09:45:27 UMAP embedding parameters a = 1.262 b = 1.003
09:45:27 Read 1203 rows and found 38 numeric columns
09:45:27 Using Annoy for neighbor search, n_neighbors = 108
09:45:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:45:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c63d4f3
09:45:28 Searching Annoy index using 1 thread, search_k = 10800
09:45:28 Annoy recall = 100%
09:45:36 Commencing smooth kNN distance calibration using 1 thread
09:45:51 Initializing from normalized Laplacian + noise
09:45:51 Commencing optimization for 500 epochs, with 150060 positive edges
09:46:02 Optimization finished
[1] "109 0"09:46:03 UMAP embedding parameters a = 1.933 b = 0.7905
09:46:03 Read 1203 rows and found 38 numeric columns
09:46:03 Using Annoy for neighbor search, n_neighbors = 109
09:46:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:46:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87248d5f39
09:46:03 Searching Annoy index using 1 thread, search_k = 10900
09:46:04 Annoy recall = 100%
09:46:12 Commencing smooth kNN distance calibration using 1 thread
09:46:27 Initializing from normalized Laplacian + noise
09:46:27 Commencing optimization for 500 epochs, with 151330 positive edges
09:46:38 Optimization finished
[1] "109 0.01"09:46:38 UMAP embedding parameters a = 1.896 b = 0.8006
09:46:38 Read 1203 rows and found 38 numeric columns
09:46:38 Using Annoy for neighbor search, n_neighbors = 109
09:46:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:46:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a7664d6
09:46:39 Searching Annoy index using 1 thread, search_k = 10900
09:46:40 Annoy recall = 100%
09:46:47 Commencing smooth kNN distance calibration using 1 thread
09:47:02 Initializing from normalized Laplacian + noise
09:47:03 Commencing optimization for 500 epochs, with 151330 positive edges
09:47:14 Optimization finished
[1] "109 0.02"09:47:14 UMAP embedding parameters a = 1.859 b = 0.8109
09:47:14 Read 1203 rows and found 38 numeric columns
09:47:14 Using Annoy for neighbor search, n_neighbors = 109
09:47:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:47:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bb75d84
09:47:15 Searching Annoy index using 1 thread, search_k = 10900
09:47:16 Annoy recall = 100%
09:47:23 Commencing smooth kNN distance calibration using 1 thread
09:47:38 Initializing from normalized Laplacian + noise
09:47:38 Commencing optimization for 500 epochs, with 151330 positive edges
09:47:50 Optimization finished
[1] "109 0.03"09:47:50 UMAP embedding parameters a = 1.822 b = 0.8212
09:47:50 Read 1203 rows and found 38 numeric columns
09:47:50 Using Annoy for neighbor search, n_neighbors = 109
09:47:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:47:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718621e17
09:47:51 Searching Annoy index using 1 thread, search_k = 10900
09:47:52 Annoy recall = 100%
09:47:59 Commencing smooth kNN distance calibration using 1 thread
09:48:14 Initializing from normalized Laplacian + noise
09:48:14 Commencing optimization for 500 epochs, with 151330 positive edges
09:48:26 Optimization finished
[1] "109 0.04"09:48:26 UMAP embedding parameters a = 1.786 b = 0.8316
09:48:26 Read 1203 rows and found 38 numeric columns
09:48:26 Using Annoy for neighbor search, n_neighbors = 109
09:48:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:48:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e040f2d
09:48:26 Searching Annoy index using 1 thread, search_k = 10900
09:48:27 Annoy recall = 100%
09:48:35 Commencing smooth kNN distance calibration using 1 thread
09:48:50 Initializing from normalized Laplacian + noise
09:48:50 Commencing optimization for 500 epochs, with 151330 positive edges
09:49:02 Optimization finished
[1] "109 0.05"09:49:02 UMAP embedding parameters a = 1.75 b = 0.8421
09:49:02 Read 1203 rows and found 38 numeric columns
09:49:02 Using Annoy for neighbor search, n_neighbors = 109
09:49:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:49:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d4906ff
09:49:02 Searching Annoy index using 1 thread, search_k = 10900
09:49:03 Annoy recall = 100%
09:49:11 Commencing smooth kNN distance calibration using 1 thread
09:49:26 Initializing from normalized Laplacian + noise
09:49:26 Commencing optimization for 500 epochs, with 151330 positive edges
09:49:37 Optimization finished
[1] "109 0.06"09:49:37 UMAP embedding parameters a = 1.715 b = 0.8526
09:49:37 Read 1203 rows and found 38 numeric columns
09:49:37 Using Annoy for neighbor search, n_neighbors = 109
09:49:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:49:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748cedbfb
09:49:38 Searching Annoy index using 1 thread, search_k = 10900
09:49:39 Annoy recall = 100%
09:49:46 Commencing smooth kNN distance calibration using 1 thread
09:50:01 Initializing from normalized Laplacian + noise
09:50:01 Commencing optimization for 500 epochs, with 151330 positive edges
09:50:13 Optimization finished
[1] "109 0.07"09:50:13 UMAP embedding parameters a = 1.68 b = 0.8631
09:50:13 Read 1203 rows and found 38 numeric columns
09:50:13 Using Annoy for neighbor search, n_neighbors = 109
09:50:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:50:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772995e4
09:50:14 Searching Annoy index using 1 thread, search_k = 10900
09:50:14 Annoy recall = 100%
09:50:22 Commencing smooth kNN distance calibration using 1 thread
09:50:36 Initializing from normalized Laplacian + noise
09:50:36 Commencing optimization for 500 epochs, with 151330 positive edges
09:50:48 Optimization finished
[1] "109 0.08"09:50:48 UMAP embedding parameters a = 1.645 b = 0.8737
09:50:48 Read 1203 rows and found 38 numeric columns
09:50:48 Using Annoy for neighbor search, n_neighbors = 109
09:50:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:50:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877349a474
09:50:48 Searching Annoy index using 1 thread, search_k = 10900
09:50:49 Annoy recall = 100%
09:50:57 Commencing smooth kNN distance calibration using 1 thread
09:51:11 Initializing from normalized Laplacian + noise
09:51:11 Commencing optimization for 500 epochs, with 151330 positive edges
09:51:23 Optimization finished
[1] "109 0.09"09:51:23 UMAP embedding parameters a = 1.611 b = 0.8844
09:51:23 Read 1203 rows and found 38 numeric columns
09:51:23 Using Annoy for neighbor search, n_neighbors = 109
09:51:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:51:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779cb8d06
09:51:23 Searching Annoy index using 1 thread, search_k = 10900
09:51:24 Annoy recall = 100%
09:51:31 Commencing smooth kNN distance calibration using 1 thread
09:51:46 Initializing from normalized Laplacian + noise
09:51:46 Commencing optimization for 500 epochs, with 151330 positive edges
09:51:58 Optimization finished
[1] "109 0.1"09:51:58 UMAP embedding parameters a = 1.577 b = 0.8951
09:51:58 Read 1203 rows and found 38 numeric columns
09:51:58 Using Annoy for neighbor search, n_neighbors = 109
09:51:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:51:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f02b46f
09:51:58 Searching Annoy index using 1 thread, search_k = 10900
09:51:59 Annoy recall = 100%
09:52:07 Commencing smooth kNN distance calibration using 1 thread
09:52:21 Initializing from normalized Laplacian + noise
09:52:21 Commencing optimization for 500 epochs, with 151330 positive edges
09:52:32 Optimization finished
[1] "109 0.11"09:52:33 UMAP embedding parameters a = 1.544 b = 0.9058
09:52:33 Read 1203 rows and found 38 numeric columns
09:52:33 Using Annoy for neighbor search, n_neighbors = 109
09:52:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:52:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87255e0f53
09:52:33 Searching Annoy index using 1 thread, search_k = 10900
09:52:34 Annoy recall = 100%
09:52:41 Commencing smooth kNN distance calibration using 1 thread
09:52:56 Initializing from normalized Laplacian + noise
09:52:56 Commencing optimization for 500 epochs, with 151330 positive edges
09:53:07 Optimization finished
[1] "109 0.12"09:53:08 UMAP embedding parameters a = 1.51 b = 0.9165
09:53:08 Read 1203 rows and found 38 numeric columns
09:53:08 Using Annoy for neighbor search, n_neighbors = 109
09:53:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:53:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87766303a1
09:53:08 Searching Annoy index using 1 thread, search_k = 10900
09:53:09 Annoy recall = 100%
09:53:16 Commencing smooth kNN distance calibration using 1 thread
09:53:31 Initializing from normalized Laplacian + noise
09:53:31 Commencing optimization for 500 epochs, with 151330 positive edges
09:53:42 Optimization finished
[1] "109 0.13"09:53:43 UMAP embedding parameters a = 1.478 b = 0.9272
09:53:43 Read 1203 rows and found 38 numeric columns
09:53:43 Using Annoy for neighbor search, n_neighbors = 109
09:53:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:53:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87107f3a50
09:53:43 Searching Annoy index using 1 thread, search_k = 10900
09:53:44 Annoy recall = 100%
09:53:51 Commencing smooth kNN distance calibration using 1 thread
09:54:06 Initializing from normalized Laplacian + noise
09:54:06 Commencing optimization for 500 epochs, with 151330 positive edges
09:54:17 Optimization finished
[1] "109 0.14"09:54:18 UMAP embedding parameters a = 1.446 b = 0.938
09:54:18 Read 1203 rows and found 38 numeric columns
09:54:18 Using Annoy for neighbor search, n_neighbors = 109
09:54:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:54:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727c4af8b
09:54:18 Searching Annoy index using 1 thread, search_k = 10900
09:54:19 Annoy recall = 100%
09:54:26 Commencing smooth kNN distance calibration using 1 thread
09:54:41 Initializing from normalized Laplacian + noise
09:54:41 Commencing optimization for 500 epochs, with 151330 positive edges
09:54:52 Optimization finished
[1] "109 0.15"09:54:52 UMAP embedding parameters a = 1.414 b = 0.9488
09:54:52 Read 1203 rows and found 38 numeric columns
09:54:52 Using Annoy for neighbor search, n_neighbors = 109
09:54:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:54:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e9de097
09:54:53 Searching Annoy index using 1 thread, search_k = 10900
09:54:54 Annoy recall = 100%
09:55:01 Commencing smooth kNN distance calibration using 1 thread
09:55:16 Initializing from normalized Laplacian + noise
09:55:16 Commencing optimization for 500 epochs, with 151330 positive edges
09:55:27 Optimization finished
[1] "109 0.16"09:55:28 UMAP embedding parameters a = 1.383 b = 0.9596
09:55:28 Read 1203 rows and found 38 numeric columns
09:55:28 Using Annoy for neighbor search, n_neighbors = 109
09:55:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:55:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731051cc5
09:55:28 Searching Annoy index using 1 thread, search_k = 10900
09:55:29 Annoy recall = 100%
09:55:36 Commencing smooth kNN distance calibration using 1 thread
09:55:51 Initializing from normalized Laplacian + noise
09:55:51 Commencing optimization for 500 epochs, with 151330 positive edges
09:56:02 Optimization finished
[1] "109 0.17"09:56:03 UMAP embedding parameters a = 1.352 b = 0.9704
09:56:03 Read 1203 rows and found 38 numeric columns
09:56:03 Using Annoy for neighbor search, n_neighbors = 109
09:56:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:56:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710a1131d
09:56:03 Searching Annoy index using 1 thread, search_k = 10900
09:56:04 Annoy recall = 100%
09:56:11 Commencing smooth kNN distance calibration using 1 thread
09:56:26 Initializing from normalized Laplacian + noise
09:56:26 Commencing optimization for 500 epochs, with 151330 positive edges
09:56:37 Optimization finished
[1] "109 0.18"09:56:38 UMAP embedding parameters a = 1.321 b = 0.9813
09:56:38 Read 1203 rows and found 38 numeric columns
09:56:38 Using Annoy for neighbor search, n_neighbors = 109
09:56:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:56:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745a1d241
09:56:38 Searching Annoy index using 1 thread, search_k = 10900
09:56:39 Annoy recall = 100%
09:56:46 Commencing smooth kNN distance calibration using 1 thread
09:57:01 Initializing from normalized Laplacian + noise
09:57:01 Commencing optimization for 500 epochs, with 151330 positive edges
09:57:12 Optimization finished
[1] "109 0.19"09:57:13 UMAP embedding parameters a = 1.292 b = 0.9921
09:57:13 Read 1203 rows and found 38 numeric columns
09:57:13 Using Annoy for neighbor search, n_neighbors = 109
09:57:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:57:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f0109ea
09:57:13 Searching Annoy index using 1 thread, search_k = 10900
09:57:14 Annoy recall = 100%
09:57:21 Commencing smooth kNN distance calibration using 1 thread
09:57:36 Initializing from normalized Laplacian + noise
09:57:36 Commencing optimization for 500 epochs, with 151330 positive edges
09:57:47 Optimization finished
[1] "109 0.2"09:57:48 UMAP embedding parameters a = 1.262 b = 1.003
09:57:48 Read 1203 rows and found 38 numeric columns
09:57:48 Using Annoy for neighbor search, n_neighbors = 109
09:57:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:57:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bd0bf45
09:57:48 Searching Annoy index using 1 thread, search_k = 10900
09:57:49 Annoy recall = 100%
09:57:56 Commencing smooth kNN distance calibration using 1 thread
09:58:11 Initializing from normalized Laplacian + noise
09:58:11 Commencing optimization for 500 epochs, with 151330 positive edges
09:58:23 Optimization finished
[1] "110 0"09:58:23 UMAP embedding parameters a = 1.933 b = 0.7905
09:58:23 Read 1203 rows and found 38 numeric columns
09:58:23 Using Annoy for neighbor search, n_neighbors = 110
09:58:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:58:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d8beb63
09:58:23 Searching Annoy index using 1 thread, search_k = 11000
09:58:24 Annoy recall = 100%
09:58:32 Commencing smooth kNN distance calibration using 1 thread
09:58:46 Initializing from normalized Laplacian + noise
09:58:46 Commencing optimization for 500 epochs, with 152646 positive edges
09:58:58 Optimization finished
[1] "110 0.01"09:58:58 UMAP embedding parameters a = 1.896 b = 0.8006
09:58:58 Read 1203 rows and found 38 numeric columns
09:58:58 Using Annoy for neighbor search, n_neighbors = 110
09:58:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:58:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777bca9cd
09:58:58 Searching Annoy index using 1 thread, search_k = 11000
09:58:59 Annoy recall = 100%
09:59:06 Commencing smooth kNN distance calibration using 1 thread
09:59:21 Initializing from normalized Laplacian + noise
09:59:22 Commencing optimization for 500 epochs, with 152646 positive edges
09:59:33 Optimization finished
[1] "110 0.02"09:59:33 UMAP embedding parameters a = 1.859 b = 0.8109
09:59:33 Read 1203 rows and found 38 numeric columns
09:59:33 Using Annoy for neighbor search, n_neighbors = 110
09:59:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:59:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738e3ae7e
09:59:34 Searching Annoy index using 1 thread, search_k = 11000
09:59:34 Annoy recall = 100%
09:59:42 Commencing smooth kNN distance calibration using 1 thread
09:59:56 Initializing from normalized Laplacian + noise
09:59:56 Commencing optimization for 500 epochs, with 152646 positive edges
10:00:08 Optimization finished
[1] "110 0.03"10:00:08 UMAP embedding parameters a = 1.822 b = 0.8212
10:00:08 Read 1203 rows and found 38 numeric columns
10:00:08 Using Annoy for neighbor search, n_neighbors = 110
10:00:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:00:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ed138bf
10:00:09 Searching Annoy index using 1 thread, search_k = 11000
10:00:09 Annoy recall = 100%
10:00:17 Commencing smooth kNN distance calibration using 1 thread
10:00:32 Initializing from normalized Laplacian + noise
10:00:32 Commencing optimization for 500 epochs, with 152646 positive edges
10:00:43 Optimization finished
[1] "110 0.04"10:00:43 UMAP embedding parameters a = 1.786 b = 0.8316
10:00:43 Read 1203 rows and found 38 numeric columns
10:00:43 Using Annoy for neighbor search, n_neighbors = 110
10:00:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:00:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727c28ffe
10:00:44 Searching Annoy index using 1 thread, search_k = 11000
10:00:45 Annoy recall = 100%
10:00:52 Commencing smooth kNN distance calibration using 1 thread
10:01:07 Initializing from normalized Laplacian + noise
10:01:07 Commencing optimization for 500 epochs, with 152646 positive edges
10:01:18 Optimization finished
[1] "110 0.05"10:01:18 UMAP embedding parameters a = 1.75 b = 0.8421
10:01:18 Read 1203 rows and found 38 numeric columns
10:01:18 Using Annoy for neighbor search, n_neighbors = 110
10:01:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:01:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bf25fda
10:01:19 Searching Annoy index using 1 thread, search_k = 11000
10:01:20 Annoy recall = 100%
10:01:27 Commencing smooth kNN distance calibration using 1 thread
10:01:42 Initializing from normalized Laplacian + noise
10:01:42 Commencing optimization for 500 epochs, with 152646 positive edges
10:01:53 Optimization finished
[1] "110 0.06"10:01:54 UMAP embedding parameters a = 1.715 b = 0.8526
10:01:54 Read 1203 rows and found 38 numeric columns
10:01:54 Using Annoy for neighbor search, n_neighbors = 110
10:01:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:01:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718c34b55
10:01:54 Searching Annoy index using 1 thread, search_k = 11000
10:01:55 Annoy recall = 100%
10:02:02 Commencing smooth kNN distance calibration using 1 thread
10:02:17 Initializing from normalized Laplacian + noise
10:02:17 Commencing optimization for 500 epochs, with 152646 positive edges
10:02:29 Optimization finished
[1] "110 0.07"10:02:29 UMAP embedding parameters a = 1.68 b = 0.8631
10:02:29 Read 1203 rows and found 38 numeric columns
10:02:29 Using Annoy for neighbor search, n_neighbors = 110
10:02:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:02:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87610d0c71
10:02:29 Searching Annoy index using 1 thread, search_k = 11000
10:02:30 Annoy recall = 100%
10:02:38 Commencing smooth kNN distance calibration using 1 thread
10:02:52 Initializing from normalized Laplacian + noise
10:02:52 Commencing optimization for 500 epochs, with 152646 positive edges
10:03:04 Optimization finished
[1] "110 0.08"10:03:04 UMAP embedding parameters a = 1.645 b = 0.8737
10:03:04 Read 1203 rows and found 38 numeric columns
10:03:04 Using Annoy for neighbor search, n_neighbors = 110
10:03:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:03:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875079125d
10:03:04 Searching Annoy index using 1 thread, search_k = 11000
10:03:05 Annoy recall = 100%
10:03:13 Commencing smooth kNN distance calibration using 1 thread
10:03:28 Initializing from normalized Laplacian + noise
10:03:28 Commencing optimization for 500 epochs, with 152646 positive edges
10:03:39 Optimization finished
[1] "110 0.09"10:03:39 UMAP embedding parameters a = 1.611 b = 0.8844
10:03:39 Read 1203 rows and found 38 numeric columns
10:03:39 Using Annoy for neighbor search, n_neighbors = 110
10:03:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:03:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735272049
10:03:40 Searching Annoy index using 1 thread, search_k = 11000
10:03:41 Annoy recall = 100%
10:03:48 Commencing smooth kNN distance calibration using 1 thread
10:04:03 Initializing from normalized Laplacian + noise
10:04:03 Commencing optimization for 500 epochs, with 152646 positive edges
10:04:14 Optimization finished
[1] "110 0.1"10:04:14 UMAP embedding parameters a = 1.577 b = 0.8951
10:04:14 Read 1203 rows and found 38 numeric columns
10:04:14 Using Annoy for neighbor search, n_neighbors = 110
10:04:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:04:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759a6bab
10:04:15 Searching Annoy index using 1 thread, search_k = 11000
10:04:16 Annoy recall = 100%
10:04:23 Commencing smooth kNN distance calibration using 1 thread
10:04:38 Initializing from normalized Laplacian + noise
10:04:38 Commencing optimization for 500 epochs, with 152646 positive edges
10:04:49 Optimization finished
[1] "110 0.11"10:04:50 UMAP embedding parameters a = 1.544 b = 0.9058
10:04:50 Read 1203 rows and found 38 numeric columns
10:04:50 Using Annoy for neighbor search, n_neighbors = 110
10:04:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:04:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877aef7733
10:04:50 Searching Annoy index using 1 thread, search_k = 11000
10:04:51 Annoy recall = 100%
10:04:58 Commencing smooth kNN distance calibration using 1 thread
10:05:13 Initializing from normalized Laplacian + noise
10:05:13 Commencing optimization for 500 epochs, with 152646 positive edges
10:05:24 Optimization finished
[1] "110 0.12"10:05:25 UMAP embedding parameters a = 1.51 b = 0.9165
10:05:25 Read 1203 rows and found 38 numeric columns
10:05:25 Using Annoy for neighbor search, n_neighbors = 110
10:05:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:05:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730de7dcd
10:05:25 Searching Annoy index using 1 thread, search_k = 11000
10:05:26 Annoy recall = 100%
10:05:33 Commencing smooth kNN distance calibration using 1 thread
10:05:48 Initializing from normalized Laplacian + noise
10:05:49 Commencing optimization for 500 epochs, with 152646 positive edges
10:06:00 Optimization finished
[1] "110 0.13"10:06:00 UMAP embedding parameters a = 1.478 b = 0.9272
10:06:00 Read 1203 rows and found 38 numeric columns
10:06:00 Using Annoy for neighbor search, n_neighbors = 110
10:06:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:06:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871dfc89c2
10:06:01 Searching Annoy index using 1 thread, search_k = 11000
10:06:01 Annoy recall = 100%
10:06:09 Commencing smooth kNN distance calibration using 1 thread
10:06:24 Initializing from normalized Laplacian + noise
10:06:24 Commencing optimization for 500 epochs, with 152646 positive edges
10:06:35 Optimization finished
[1] "110 0.14"10:06:35 UMAP embedding parameters a = 1.446 b = 0.938
10:06:35 Read 1203 rows and found 38 numeric columns
10:06:35 Using Annoy for neighbor search, n_neighbors = 110
10:06:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:06:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778f38660
10:06:36 Searching Annoy index using 1 thread, search_k = 11000
10:06:37 Annoy recall = 100%
10:06:44 Commencing smooth kNN distance calibration using 1 thread
10:07:00 Initializing from normalized Laplacian + noise
10:07:00 Commencing optimization for 500 epochs, with 152646 positive edges
10:07:11 Optimization finished
[1] "110 0.15"10:07:11 UMAP embedding parameters a = 1.414 b = 0.9488
10:07:11 Read 1203 rows and found 38 numeric columns
10:07:11 Using Annoy for neighbor search, n_neighbors = 110
10:07:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:07:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e2784cc
10:07:12 Searching Annoy index using 1 thread, search_k = 11000
10:07:13 Annoy recall = 100%
10:07:20 Commencing smooth kNN distance calibration using 1 thread
10:07:35 Initializing from normalized Laplacian + noise
10:07:36 Commencing optimization for 500 epochs, with 152646 positive edges
10:07:47 Optimization finished
[1] "110 0.16"10:07:47 UMAP embedding parameters a = 1.383 b = 0.9596
10:07:47 Read 1203 rows and found 38 numeric columns
10:07:47 Using Annoy for neighbor search, n_neighbors = 110
10:07:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:07:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766cb65bd
10:07:48 Searching Annoy index using 1 thread, search_k = 11000
10:07:49 Annoy recall = 100%
10:07:56 Commencing smooth kNN distance calibration using 1 thread
10:08:11 Initializing from normalized Laplacian + noise
10:08:11 Commencing optimization for 500 epochs, with 152646 positive edges
10:08:22 Optimization finished
[1] "110 0.17"10:08:23 UMAP embedding parameters a = 1.352 b = 0.9704
10:08:23 Read 1203 rows and found 38 numeric columns
10:08:23 Using Annoy for neighbor search, n_neighbors = 110
10:08:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:08:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d1c44
10:08:23 Searching Annoy index using 1 thread, search_k = 11000
10:08:24 Annoy recall = 100%
10:08:31 Commencing smooth kNN distance calibration using 1 thread
10:08:46 Initializing from normalized Laplacian + noise
10:08:47 Commencing optimization for 500 epochs, with 152646 positive edges
10:08:58 Optimization finished
[1] "110 0.18"10:08:58 UMAP embedding parameters a = 1.321 b = 0.9813
10:08:58 Read 1203 rows and found 38 numeric columns
10:08:58 Using Annoy for neighbor search, n_neighbors = 110
10:08:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:08:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711712941
10:08:59 Searching Annoy index using 1 thread, search_k = 11000
10:09:00 Annoy recall = 100%
10:09:07 Commencing smooth kNN distance calibration using 1 thread
10:09:22 Initializing from normalized Laplacian + noise
10:09:22 Commencing optimization for 500 epochs, with 152646 positive edges
10:09:33 Optimization finished
[1] "110 0.19"10:09:33 UMAP embedding parameters a = 1.292 b = 0.9921
10:09:33 Read 1203 rows and found 38 numeric columns
10:09:34 Using Annoy for neighbor search, n_neighbors = 110
10:09:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:09:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876096f2c3
10:09:34 Searching Annoy index using 1 thread, search_k = 11000
10:09:35 Annoy recall = 100%
10:09:42 Commencing smooth kNN distance calibration using 1 thread
10:09:57 Initializing from normalized Laplacian + noise
10:09:58 Commencing optimization for 500 epochs, with 152646 positive edges
10:10:09 Optimization finished
[1] "110 0.2"10:10:09 UMAP embedding parameters a = 1.262 b = 1.003
10:10:09 Read 1203 rows and found 38 numeric columns
10:10:09 Using Annoy for neighbor search, n_neighbors = 110
10:10:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:10:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f1fd0b4
10:10:10 Searching Annoy index using 1 thread, search_k = 11000
10:10:10 Annoy recall = 100%
10:10:18 Commencing smooth kNN distance calibration using 1 thread
10:10:33 Initializing from normalized Laplacian + noise
10:10:33 Commencing optimization for 500 epochs, with 152646 positive edges
10:10:44 Optimization finished
[1] "111 0"10:10:44 UMAP embedding parameters a = 1.933 b = 0.7905
10:10:45 Read 1203 rows and found 38 numeric columns
10:10:45 Using Annoy for neighbor search, n_neighbors = 111
10:10:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:10:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736cf3894
10:10:45 Searching Annoy index using 1 thread, search_k = 11100
10:10:46 Annoy recall = 100%
10:10:53 Commencing smooth kNN distance calibration using 1 thread
10:11:09 Initializing from normalized Laplacian + noise
10:11:09 Commencing optimization for 500 epochs, with 153896 positive edges
10:11:20 Optimization finished
[1] "111 0.01"10:11:20 UMAP embedding parameters a = 1.896 b = 0.8006
10:11:20 Read 1203 rows and found 38 numeric columns
10:11:20 Using Annoy for neighbor search, n_neighbors = 111
10:11:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:11:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756f9f664
10:11:21 Searching Annoy index using 1 thread, search_k = 11100
10:11:21 Annoy recall = 100%
10:11:29 Commencing smooth kNN distance calibration using 1 thread
10:11:44 Initializing from normalized Laplacian + noise
10:11:44 Commencing optimization for 500 epochs, with 153896 positive edges
10:11:55 Optimization finished
[1] "111 0.02"10:11:56 UMAP embedding parameters a = 1.859 b = 0.8109
10:11:56 Read 1203 rows and found 38 numeric columns
10:11:56 Using Annoy for neighbor search, n_neighbors = 111
10:11:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:11:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f9f0b04
10:11:56 Searching Annoy index using 1 thread, search_k = 11100
10:11:57 Annoy recall = 100%
10:12:05 Commencing smooth kNN distance calibration using 1 thread
10:12:20 Initializing from normalized Laplacian + noise
10:12:20 Commencing optimization for 500 epochs, with 153896 positive edges
10:12:31 Optimization finished
[1] "111 0.03"10:12:31 UMAP embedding parameters a = 1.822 b = 0.8212
10:12:31 Read 1203 rows and found 38 numeric columns
10:12:31 Using Annoy for neighbor search, n_neighbors = 111
10:12:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:12:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e93e820
10:12:32 Searching Annoy index using 1 thread, search_k = 11100
10:12:33 Annoy recall = 100%
10:12:40 Commencing smooth kNN distance calibration using 1 thread
10:12:55 Initializing from normalized Laplacian + noise
10:12:55 Commencing optimization for 500 epochs, with 153896 positive edges
10:13:07 Optimization finished
[1] "111 0.04"10:13:07 UMAP embedding parameters a = 1.786 b = 0.8316
10:13:07 Read 1203 rows and found 38 numeric columns
10:13:07 Using Annoy for neighbor search, n_neighbors = 111
10:13:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:13:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874597d6fb
10:13:07 Searching Annoy index using 1 thread, search_k = 11100
10:13:08 Annoy recall = 100%
10:13:16 Commencing smooth kNN distance calibration using 1 thread
10:13:31 Initializing from normalized Laplacian + noise
10:13:31 Commencing optimization for 500 epochs, with 153896 positive edges
10:13:42 Optimization finished
[1] "111 0.05"10:13:42 UMAP embedding parameters a = 1.75 b = 0.8421
10:13:42 Read 1203 rows and found 38 numeric columns
10:13:42 Using Annoy for neighbor search, n_neighbors = 111
10:13:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:13:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a427ca
10:13:43 Searching Annoy index using 1 thread, search_k = 11100
10:13:44 Annoy recall = 100%
10:13:51 Commencing smooth kNN distance calibration using 1 thread
10:14:06 Initializing from normalized Laplacian + noise
10:14:07 Commencing optimization for 500 epochs, with 153896 positive edges
10:14:18 Optimization finished
[1] "111 0.06"10:14:18 UMAP embedding parameters a = 1.715 b = 0.8526
10:14:18 Read 1203 rows and found 38 numeric columns
10:14:18 Using Annoy for neighbor search, n_neighbors = 111
10:14:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:14:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f34fb3d
10:14:19 Searching Annoy index using 1 thread, search_k = 11100
10:14:20 Annoy recall = 100%
10:14:27 Commencing smooth kNN distance calibration using 1 thread
10:14:42 Initializing from normalized Laplacian + noise
10:14:42 Commencing optimization for 500 epochs, with 153896 positive edges
10:14:53 Optimization finished
[1] "111 0.07"10:14:54 UMAP embedding parameters a = 1.68 b = 0.8631
10:14:54 Read 1203 rows and found 38 numeric columns
10:14:54 Using Annoy for neighbor search, n_neighbors = 111
10:14:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:14:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b39a93c
10:14:54 Searching Annoy index using 1 thread, search_k = 11100
10:14:55 Annoy recall = 100%
10:15:02 Commencing smooth kNN distance calibration using 1 thread
10:15:18 Initializing from normalized Laplacian + noise
10:15:18 Commencing optimization for 500 epochs, with 153896 positive edges
10:15:29 Optimization finished
[1] "111 0.08"10:15:29 UMAP embedding parameters a = 1.645 b = 0.8737
10:15:29 Read 1203 rows and found 38 numeric columns
10:15:29 Using Annoy for neighbor search, n_neighbors = 111
10:15:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:15:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871fa531b4
10:15:30 Searching Annoy index using 1 thread, search_k = 11100
10:15:31 Annoy recall = 100%
10:15:38 Commencing smooth kNN distance calibration using 1 thread
10:15:53 Initializing from normalized Laplacian + noise
10:15:53 Commencing optimization for 500 epochs, with 153896 positive edges
10:16:05 Optimization finished
[1] "111 0.09"10:16:05 UMAP embedding parameters a = 1.611 b = 0.8844
10:16:05 Read 1203 rows and found 38 numeric columns
10:16:05 Using Annoy for neighbor search, n_neighbors = 111
10:16:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:16:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b05ba82
10:16:05 Searching Annoy index using 1 thread, search_k = 11100
10:16:06 Annoy recall = 100%
10:16:14 Commencing smooth kNN distance calibration using 1 thread
10:16:29 Initializing from normalized Laplacian + noise
10:16:29 Commencing optimization for 500 epochs, with 153896 positive edges
10:16:40 Optimization finished
[1] "111 0.1"10:16:41 UMAP embedding parameters a = 1.577 b = 0.8951
10:16:41 Read 1203 rows and found 38 numeric columns
10:16:41 Using Annoy for neighbor search, n_neighbors = 111
10:16:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:16:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758c594a0
10:16:41 Searching Annoy index using 1 thread, search_k = 11100
10:16:42 Annoy recall = 100%
10:16:49 Commencing smooth kNN distance calibration using 1 thread
10:17:05 Initializing from normalized Laplacian + noise
10:17:05 Commencing optimization for 500 epochs, with 153896 positive edges
10:17:16 Optimization finished
[1] "111 0.11"10:17:16 UMAP embedding parameters a = 1.544 b = 0.9058
10:17:16 Read 1203 rows and found 38 numeric columns
10:17:16 Using Annoy for neighbor search, n_neighbors = 111
10:17:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:17:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871761db81
10:17:17 Searching Annoy index using 1 thread, search_k = 11100
10:17:18 Annoy recall = 100%
10:17:25 Commencing smooth kNN distance calibration using 1 thread
10:17:40 Initializing from normalized Laplacian + noise
10:17:41 Commencing optimization for 500 epochs, with 153896 positive edges
10:17:52 Optimization finished
[1] "111 0.12"10:17:52 UMAP embedding parameters a = 1.51 b = 0.9165
10:17:52 Read 1203 rows and found 38 numeric columns
10:17:52 Using Annoy for neighbor search, n_neighbors = 111
10:17:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:17:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713e96900
10:17:53 Searching Annoy index using 1 thread, search_k = 11100
10:17:54 Annoy recall = 100%
10:18:01 Commencing smooth kNN distance calibration using 1 thread
10:18:16 Initializing from normalized Laplacian + noise
10:18:16 Commencing optimization for 500 epochs, with 153896 positive edges
10:18:28 Optimization finished
[1] "111 0.13"10:18:28 UMAP embedding parameters a = 1.478 b = 0.9272
10:18:28 Read 1203 rows and found 38 numeric columns
10:18:28 Using Annoy for neighbor search, n_neighbors = 111
10:18:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:18:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874796cd5f
10:18:29 Searching Annoy index using 1 thread, search_k = 11100
10:18:29 Annoy recall = 100%
10:18:37 Commencing smooth kNN distance calibration using 1 thread
10:18:52 Initializing from normalized Laplacian + noise
10:18:52 Commencing optimization for 500 epochs, with 153896 positive edges
10:19:03 Optimization finished
[1] "111 0.14"10:19:04 UMAP embedding parameters a = 1.446 b = 0.938
10:19:04 Read 1203 rows and found 38 numeric columns
10:19:04 Using Annoy for neighbor search, n_neighbors = 111
10:19:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:19:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f246b80
10:19:04 Searching Annoy index using 1 thread, search_k = 11100
10:19:05 Annoy recall = 100%
10:19:13 Commencing smooth kNN distance calibration using 1 thread
10:19:28 Initializing from normalized Laplacian + noise
10:19:28 Commencing optimization for 500 epochs, with 153896 positive edges
10:19:39 Optimization finished
[1] "111 0.15"10:19:40 UMAP embedding parameters a = 1.414 b = 0.9488
10:19:40 Read 1203 rows and found 38 numeric columns
10:19:40 Using Annoy for neighbor search, n_neighbors = 111
10:19:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:19:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fdbc8da
10:19:40 Searching Annoy index using 1 thread, search_k = 11100
10:19:41 Annoy recall = 100%
10:19:48 Commencing smooth kNN distance calibration using 1 thread
10:20:04 Initializing from normalized Laplacian + noise
10:20:04 Commencing optimization for 500 epochs, with 153896 positive edges
10:20:15 Optimization finished
[1] "111 0.16"10:20:15 UMAP embedding parameters a = 1.383 b = 0.9596
10:20:15 Read 1203 rows and found 38 numeric columns
10:20:15 Using Annoy for neighbor search, n_neighbors = 111
10:20:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:20:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87605a18b4
10:20:16 Searching Annoy index using 1 thread, search_k = 11100
10:20:17 Annoy recall = 100%
10:20:24 Commencing smooth kNN distance calibration using 1 thread
10:20:40 Initializing from normalized Laplacian + noise
10:20:40 Commencing optimization for 500 epochs, with 153896 positive edges
10:20:51 Optimization finished
[1] "111 0.17"10:20:51 UMAP embedding parameters a = 1.352 b = 0.9704
10:20:51 Read 1203 rows and found 38 numeric columns
10:20:51 Using Annoy for neighbor search, n_neighbors = 111
10:20:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:20:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87203177f1
10:20:52 Searching Annoy index using 1 thread, search_k = 11100
10:20:53 Annoy recall = 100%
10:21:00 Commencing smooth kNN distance calibration using 1 thread
10:21:15 Initializing from normalized Laplacian + noise
10:21:15 Commencing optimization for 500 epochs, with 153896 positive edges
10:21:27 Optimization finished
[1] "111 0.18"10:21:27 UMAP embedding parameters a = 1.321 b = 0.9813
10:21:27 Read 1203 rows and found 38 numeric columns
10:21:27 Using Annoy for neighbor search, n_neighbors = 111
10:21:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:21:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872054db38
10:21:28 Searching Annoy index using 1 thread, search_k = 11100
10:21:28 Annoy recall = 100%
10:21:36 Commencing smooth kNN distance calibration using 1 thread
10:21:51 Initializing from normalized Laplacian + noise
10:21:51 Commencing optimization for 500 epochs, with 153896 positive edges
10:22:03 Optimization finished
[1] "111 0.19"10:22:03 UMAP embedding parameters a = 1.292 b = 0.9921
10:22:03 Read 1203 rows and found 38 numeric columns
10:22:03 Using Annoy for neighbor search, n_neighbors = 111
10:22:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:22:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87158138fd
10:22:03 Searching Annoy index using 1 thread, search_k = 11100
10:22:04 Annoy recall = 100%
10:22:12 Commencing smooth kNN distance calibration using 1 thread
10:22:27 Initializing from normalized Laplacian + noise
10:22:27 Commencing optimization for 500 epochs, with 153896 positive edges
10:22:39 Optimization finished
[1] "111 0.2"10:22:39 UMAP embedding parameters a = 1.262 b = 1.003
10:22:39 Read 1203 rows and found 38 numeric columns
10:22:39 Using Annoy for neighbor search, n_neighbors = 111
10:22:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:22:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725cbe39c
10:22:39 Searching Annoy index using 1 thread, search_k = 11100
10:22:40 Annoy recall = 100%
10:22:48 Commencing smooth kNN distance calibration using 1 thread
10:23:03 Initializing from normalized Laplacian + noise
10:23:03 Commencing optimization for 500 epochs, with 153896 positive edges
10:23:14 Optimization finished
[1] "112 0"10:23:15 UMAP embedding parameters a = 1.933 b = 0.7905
10:23:15 Read 1203 rows and found 38 numeric columns
10:23:15 Using Annoy for neighbor search, n_neighbors = 112
10:23:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:23:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b44526b
10:23:15 Searching Annoy index using 1 thread, search_k = 11200
10:23:16 Annoy recall = 100%
10:23:24 Commencing smooth kNN distance calibration using 1 thread
10:23:39 Initializing from normalized Laplacian + noise
10:23:39 Commencing optimization for 500 epochs, with 155136 positive edges
10:23:51 Optimization finished
[1] "112 0.01"10:23:51 UMAP embedding parameters a = 1.896 b = 0.8006
10:23:51 Read 1203 rows and found 38 numeric columns
10:23:51 Using Annoy for neighbor search, n_neighbors = 112
10:23:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:23:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87465fb6cb
10:23:51 Searching Annoy index using 1 thread, search_k = 11200
10:23:52 Annoy recall = 100%
10:24:00 Commencing smooth kNN distance calibration using 1 thread
10:24:15 Initializing from normalized Laplacian + noise
10:24:15 Commencing optimization for 500 epochs, with 155136 positive edges
10:24:26 Optimization finished
[1] "112 0.02"10:24:27 UMAP embedding parameters a = 1.859 b = 0.8109
10:24:27 Read 1203 rows and found 38 numeric columns
10:24:27 Using Annoy for neighbor search, n_neighbors = 112
10:24:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:24:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743c86d5e
10:24:27 Searching Annoy index using 1 thread, search_k = 11200
10:24:28 Annoy recall = 100%
10:24:36 Commencing smooth kNN distance calibration using 1 thread
10:24:51 Initializing from normalized Laplacian + noise
10:24:51 Commencing optimization for 500 epochs, with 155136 positive edges
10:25:02 Optimization finished
[1] "112 0.03"10:25:03 UMAP embedding parameters a = 1.822 b = 0.8212
10:25:03 Read 1203 rows and found 38 numeric columns
10:25:03 Using Annoy for neighbor search, n_neighbors = 112
10:25:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:25:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871437d8cc
10:25:03 Searching Annoy index using 1 thread, search_k = 11200
10:25:04 Annoy recall = 100%
10:25:11 Commencing smooth kNN distance calibration using 1 thread
10:25:27 Initializing from normalized Laplacian + noise
10:25:27 Commencing optimization for 500 epochs, with 155136 positive edges
10:25:38 Optimization finished
[1] "112 0.04"10:25:39 UMAP embedding parameters a = 1.786 b = 0.8316
10:25:39 Read 1203 rows and found 38 numeric columns
10:25:39 Using Annoy for neighbor search, n_neighbors = 112
10:25:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:25:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764873b97
10:25:39 Searching Annoy index using 1 thread, search_k = 11200
10:25:40 Annoy recall = 100%
10:25:48 Commencing smooth kNN distance calibration using 1 thread
10:26:03 Initializing from normalized Laplacian + noise
10:26:03 Commencing optimization for 500 epochs, with 155136 positive edges
10:26:14 Optimization finished
[1] "112 0.05"10:26:14 UMAP embedding parameters a = 1.75 b = 0.8421
10:26:15 Read 1203 rows and found 38 numeric columns
10:26:15 Using Annoy for neighbor search, n_neighbors = 112
10:26:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:26:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a93d31b
10:26:15 Searching Annoy index using 1 thread, search_k = 11200
10:26:16 Annoy recall = 100%
10:26:23 Commencing smooth kNN distance calibration using 1 thread
10:26:39 Initializing from normalized Laplacian + noise
10:26:39 Commencing optimization for 500 epochs, with 155136 positive edges
10:26:50 Optimization finished
[1] "112 0.06"10:26:51 UMAP embedding parameters a = 1.715 b = 0.8526
10:26:51 Read 1203 rows and found 38 numeric columns
10:26:51 Using Annoy for neighbor search, n_neighbors = 112
10:26:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:26:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871454f510
10:26:51 Searching Annoy index using 1 thread, search_k = 11200
10:26:52 Annoy recall = 100%
10:27:00 Commencing smooth kNN distance calibration using 1 thread
10:27:15 Initializing from normalized Laplacian + noise
10:27:15 Commencing optimization for 500 epochs, with 155136 positive edges
10:27:26 Optimization finished
[1] "112 0.07"10:27:26 UMAP embedding parameters a = 1.68 b = 0.8631
10:27:26 Read 1203 rows and found 38 numeric columns
10:27:26 Using Annoy for neighbor search, n_neighbors = 112
10:27:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:27:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775f864d8
10:27:27 Searching Annoy index using 1 thread, search_k = 11200
10:27:28 Annoy recall = 100%
10:27:35 Commencing smooth kNN distance calibration using 1 thread
10:27:51 Initializing from normalized Laplacian + noise
10:27:51 Commencing optimization for 500 epochs, with 155136 positive edges
10:28:02 Optimization finished
[1] "112 0.08"10:28:03 UMAP embedding parameters a = 1.645 b = 0.8737
10:28:03 Read 1203 rows and found 38 numeric columns
10:28:03 Using Annoy for neighbor search, n_neighbors = 112
10:28:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:28:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b2ac5df
10:28:03 Searching Annoy index using 1 thread, search_k = 11200
10:28:04 Annoy recall = 100%
10:28:11 Commencing smooth kNN distance calibration using 1 thread
10:28:27 Initializing from normalized Laplacian + noise
10:28:27 Commencing optimization for 500 epochs, with 155136 positive edges
10:28:38 Optimization finished
[1] "112 0.09"10:28:39 UMAP embedding parameters a = 1.611 b = 0.8844
10:28:39 Read 1203 rows and found 38 numeric columns
10:28:39 Using Annoy for neighbor search, n_neighbors = 112
10:28:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:28:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875374c5c4
10:28:39 Searching Annoy index using 1 thread, search_k = 11200
10:28:40 Annoy recall = 100%
10:28:48 Commencing smooth kNN distance calibration using 1 thread
10:29:03 Initializing from normalized Laplacian + noise
10:29:03 Commencing optimization for 500 epochs, with 155136 positive edges
10:29:14 Optimization finished
[1] "112 0.1"10:29:15 UMAP embedding parameters a = 1.577 b = 0.8951
10:29:15 Read 1203 rows and found 38 numeric columns
10:29:15 Using Annoy for neighbor search, n_neighbors = 112
10:29:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:29:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cc79d6d
10:29:15 Searching Annoy index using 1 thread, search_k = 11200
10:29:16 Annoy recall = 100%
10:29:24 Commencing smooth kNN distance calibration using 1 thread
10:29:39 Initializing from normalized Laplacian + noise
10:29:39 Commencing optimization for 500 epochs, with 155136 positive edges
10:29:51 Optimization finished
[1] "112 0.11"10:29:51 UMAP embedding parameters a = 1.544 b = 0.9058
10:29:51 Read 1203 rows and found 38 numeric columns
10:29:51 Using Annoy for neighbor search, n_neighbors = 112
10:29:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:29:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876224bc43
10:29:51 Searching Annoy index using 1 thread, search_k = 11200
10:29:52 Annoy recall = 100%
10:30:00 Commencing smooth kNN distance calibration using 1 thread
10:30:15 Initializing from normalized Laplacian + noise
10:30:15 Commencing optimization for 500 epochs, with 155136 positive edges
10:30:27 Optimization finished
[1] "112 0.12"10:30:27 UMAP embedding parameters a = 1.51 b = 0.9165
10:30:27 Read 1203 rows and found 38 numeric columns
10:30:27 Using Annoy for neighbor search, n_neighbors = 112
10:30:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:30:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872313d0c9
10:30:27 Searching Annoy index using 1 thread, search_k = 11200
10:30:28 Annoy recall = 100%
10:30:36 Commencing smooth kNN distance calibration using 1 thread
10:30:51 Initializing from normalized Laplacian + noise
10:30:51 Commencing optimization for 500 epochs, with 155136 positive edges
10:31:03 Optimization finished
[1] "112 0.13"10:31:03 UMAP embedding parameters a = 1.478 b = 0.9272
10:31:03 Read 1203 rows and found 38 numeric columns
10:31:03 Using Annoy for neighbor search, n_neighbors = 112
10:31:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:31:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b5b858d
10:31:04 Searching Annoy index using 1 thread, search_k = 11200
10:31:04 Annoy recall = 100%
10:31:12 Commencing smooth kNN distance calibration using 1 thread
10:31:27 Initializing from normalized Laplacian + noise
10:31:27 Commencing optimization for 500 epochs, with 155136 positive edges
10:31:39 Optimization finished
[1] "112 0.14"10:31:39 UMAP embedding parameters a = 1.446 b = 0.938
10:31:39 Read 1203 rows and found 38 numeric columns
10:31:39 Using Annoy for neighbor search, n_neighbors = 112
10:31:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:31:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727bc933f
10:31:40 Searching Annoy index using 1 thread, search_k = 11200
10:31:40 Annoy recall = 100%
10:31:48 Commencing smooth kNN distance calibration using 1 thread
10:32:03 Initializing from normalized Laplacian + noise
10:32:04 Commencing optimization for 500 epochs, with 155136 positive edges
10:32:15 Optimization finished
[1] "112 0.15"10:32:15 UMAP embedding parameters a = 1.414 b = 0.9488
10:32:15 Read 1203 rows and found 38 numeric columns
10:32:15 Using Annoy for neighbor search, n_neighbors = 112
10:32:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:32:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723b7f893
10:32:16 Searching Annoy index using 1 thread, search_k = 11200
10:32:17 Annoy recall = 100%
10:32:24 Commencing smooth kNN distance calibration using 1 thread
10:32:39 Initializing from normalized Laplacian + noise
10:32:40 Commencing optimization for 500 epochs, with 155136 positive edges
10:32:51 Optimization finished
[1] "112 0.16"10:32:51 UMAP embedding parameters a = 1.383 b = 0.9596
10:32:51 Read 1203 rows and found 38 numeric columns
10:32:51 Using Annoy for neighbor search, n_neighbors = 112
10:32:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:32:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a9080ca
10:32:52 Searching Annoy index using 1 thread, search_k = 11200
10:32:53 Annoy recall = 100%
10:33:00 Commencing smooth kNN distance calibration using 1 thread
10:33:16 Initializing from normalized Laplacian + noise
10:33:16 Commencing optimization for 500 epochs, with 155136 positive edges
10:33:27 Optimization finished
[1] "112 0.17"10:33:27 UMAP embedding parameters a = 1.352 b = 0.9704
10:33:27 Read 1203 rows and found 38 numeric columns
10:33:27 Using Annoy for neighbor search, n_neighbors = 112
10:33:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:33:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732f63c7b
10:33:28 Searching Annoy index using 1 thread, search_k = 11200
10:33:29 Annoy recall = 100%
10:33:36 Commencing smooth kNN distance calibration using 1 thread
10:33:52 Initializing from normalized Laplacian + noise
10:33:52 Commencing optimization for 500 epochs, with 155136 positive edges
10:34:03 Optimization finished
[1] "112 0.18"10:34:04 UMAP embedding parameters a = 1.321 b = 0.9813
10:34:04 Read 1203 rows and found 38 numeric columns
10:34:04 Using Annoy for neighbor search, n_neighbors = 112
10:34:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:34:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87435d2a47
10:34:04 Searching Annoy index using 1 thread, search_k = 11200
10:34:05 Annoy recall = 100%
10:34:13 Commencing smooth kNN distance calibration using 1 thread
10:34:28 Initializing from normalized Laplacian + noise
10:34:28 Commencing optimization for 500 epochs, with 155136 positive edges
10:34:39 Optimization finished
[1] "112 0.19"10:34:40 UMAP embedding parameters a = 1.292 b = 0.9921
10:34:40 Read 1203 rows and found 38 numeric columns
10:34:40 Using Annoy for neighbor search, n_neighbors = 112
10:34:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:34:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755963b4c
10:34:40 Searching Annoy index using 1 thread, search_k = 11200
10:34:41 Annoy recall = 100%
10:34:49 Commencing smooth kNN distance calibration using 1 thread
10:35:04 Initializing from normalized Laplacian + noise
10:35:04 Commencing optimization for 500 epochs, with 155136 positive edges
10:35:16 Optimization finished
[1] "112 0.2"10:35:16 UMAP embedding parameters a = 1.262 b = 1.003
10:35:16 Read 1203 rows and found 38 numeric columns
10:35:16 Using Annoy for neighbor search, n_neighbors = 112
10:35:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:35:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bbbd11b
10:35:17 Searching Annoy index using 1 thread, search_k = 11200
10:35:17 Annoy recall = 100%
10:35:25 Commencing smooth kNN distance calibration using 1 thread
10:35:40 Initializing from normalized Laplacian + noise
10:35:40 Commencing optimization for 500 epochs, with 155136 positive edges
10:35:52 Optimization finished
[1] "113 0"10:35:52 UMAP embedding parameters a = 1.933 b = 0.7905
10:35:52 Read 1203 rows and found 38 numeric columns
10:35:52 Using Annoy for neighbor search, n_neighbors = 113
10:35:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:35:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875abf05c8
10:35:53 Searching Annoy index using 1 thread, search_k = 11300
10:35:54 Annoy recall = 100%
10:36:01 Commencing smooth kNN distance calibration using 1 thread
10:36:17 Initializing from normalized Laplacian + noise
10:36:17 Commencing optimization for 500 epochs, with 156440 positive edges
10:36:28 Optimization finished
[1] "113 0.01"10:36:28 UMAP embedding parameters a = 1.896 b = 0.8006
10:36:28 Read 1203 rows and found 38 numeric columns
10:36:28 Using Annoy for neighbor search, n_neighbors = 113
10:36:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:36:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87697fa44c
10:36:29 Searching Annoy index using 1 thread, search_k = 11300
10:36:30 Annoy recall = 100%
10:36:37 Commencing smooth kNN distance calibration using 1 thread
10:36:53 Initializing from normalized Laplacian + noise
10:36:53 Commencing optimization for 500 epochs, with 156440 positive edges
10:37:04 Optimization finished
[1] "113 0.02"10:37:05 UMAP embedding parameters a = 1.859 b = 0.8109
10:37:05 Read 1203 rows and found 38 numeric columns
10:37:05 Using Annoy for neighbor search, n_neighbors = 113
10:37:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:37:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753529e7a
10:37:05 Searching Annoy index using 1 thread, search_k = 11300
10:37:06 Annoy recall = 100%
10:37:14 Commencing smooth kNN distance calibration using 1 thread
10:37:29 Initializing from normalized Laplacian + noise
10:37:29 Commencing optimization for 500 epochs, with 156440 positive edges
10:37:41 Optimization finished
[1] "113 0.03"10:37:41 UMAP embedding parameters a = 1.822 b = 0.8212
10:37:41 Read 1203 rows and found 38 numeric columns
10:37:41 Using Annoy for neighbor search, n_neighbors = 113
10:37:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:37:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719e37148
10:37:41 Searching Annoy index using 1 thread, search_k = 11300
10:37:42 Annoy recall = 100%
10:37:50 Commencing smooth kNN distance calibration using 1 thread
10:38:05 Initializing from normalized Laplacian + noise
10:38:05 Commencing optimization for 500 epochs, with 156440 positive edges
10:38:17 Optimization finished
[1] "113 0.04"10:38:17 UMAP embedding parameters a = 1.786 b = 0.8316
10:38:17 Read 1203 rows and found 38 numeric columns
10:38:17 Using Annoy for neighbor search, n_neighbors = 113
10:38:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:38:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87395b6d27
10:38:18 Searching Annoy index using 1 thread, search_k = 11300
10:38:19 Annoy recall = 100%
10:38:26 Commencing smooth kNN distance calibration using 1 thread
10:38:41 Initializing from normalized Laplacian + noise
10:38:42 Commencing optimization for 500 epochs, with 156440 positive edges
10:38:53 Optimization finished
[1] "113 0.05"10:38:53 UMAP embedding parameters a = 1.75 b = 0.8421
10:38:53 Read 1203 rows and found 38 numeric columns
10:38:53 Using Annoy for neighbor search, n_neighbors = 113
10:38:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:38:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733acb72f
10:38:54 Searching Annoy index using 1 thread, search_k = 11300
10:38:55 Annoy recall = 100%
10:39:02 Commencing smooth kNN distance calibration using 1 thread
10:39:18 Initializing from normalized Laplacian + noise
10:39:18 Commencing optimization for 500 epochs, with 156440 positive edges
10:39:29 Optimization finished
[1] "113 0.06"10:39:30 UMAP embedding parameters a = 1.715 b = 0.8526
10:39:30 Read 1203 rows and found 38 numeric columns
10:39:30 Using Annoy for neighbor search, n_neighbors = 113
10:39:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:39:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a14e93a
10:39:30 Searching Annoy index using 1 thread, search_k = 11300
10:39:31 Annoy recall = 100%
10:39:39 Commencing smooth kNN distance calibration using 1 thread
10:39:54 Initializing from normalized Laplacian + noise
10:39:54 Commencing optimization for 500 epochs, with 156440 positive edges
10:40:06 Optimization finished
[1] "113 0.07"10:40:06 UMAP embedding parameters a = 1.68 b = 0.8631
10:40:06 Read 1203 rows and found 38 numeric columns
10:40:06 Using Annoy for neighbor search, n_neighbors = 113
10:40:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:40:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759b0485f
10:40:06 Searching Annoy index using 1 thread, search_k = 11300
10:40:07 Annoy recall = 100%
10:40:15 Commencing smooth kNN distance calibration using 1 thread
10:40:30 Initializing from normalized Laplacian + noise
10:40:31 Commencing optimization for 500 epochs, with 156440 positive edges
10:40:42 Optimization finished
[1] "113 0.08"10:40:42 UMAP embedding parameters a = 1.645 b = 0.8737
10:40:42 Read 1203 rows and found 38 numeric columns
10:40:42 Using Annoy for neighbor search, n_neighbors = 113
10:40:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:40:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87492df02c
10:40:43 Searching Annoy index using 1 thread, search_k = 11300
10:40:44 Annoy recall = 100%
10:40:51 Commencing smooth kNN distance calibration using 1 thread
10:41:07 Initializing from normalized Laplacian + noise
10:41:07 Commencing optimization for 500 epochs, with 156440 positive edges
10:41:18 Optimization finished
[1] "113 0.09"10:41:19 UMAP embedding parameters a = 1.611 b = 0.8844
10:41:19 Read 1203 rows and found 38 numeric columns
10:41:19 Using Annoy for neighbor search, n_neighbors = 113
10:41:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:41:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fe0ccd6
10:41:19 Searching Annoy index using 1 thread, search_k = 11300
10:41:20 Annoy recall = 100%
10:41:28 Commencing smooth kNN distance calibration using 1 thread
10:41:43 Initializing from normalized Laplacian + noise
10:41:43 Commencing optimization for 500 epochs, with 156440 positive edges
10:41:55 Optimization finished
[1] "113 0.1"10:41:55 UMAP embedding parameters a = 1.577 b = 0.8951
10:41:55 Read 1203 rows and found 38 numeric columns
10:41:55 Using Annoy for neighbor search, n_neighbors = 113
10:41:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:41:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774f49aca
10:41:55 Searching Annoy index using 1 thread, search_k = 11300
10:41:56 Annoy recall = 100%
10:42:04 Commencing smooth kNN distance calibration using 1 thread
10:42:19 Initializing from normalized Laplacian + noise
10:42:19 Commencing optimization for 500 epochs, with 156440 positive edges
10:42:31 Optimization finished
[1] "113 0.11"10:42:31 UMAP embedding parameters a = 1.544 b = 0.9058
10:42:31 Read 1203 rows and found 38 numeric columns
10:42:31 Using Annoy for neighbor search, n_neighbors = 113
10:42:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:42:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f8da6f7
10:42:32 Searching Annoy index using 1 thread, search_k = 11300
10:42:33 Annoy recall = 100%
10:42:40 Commencing smooth kNN distance calibration using 1 thread
10:42:56 Initializing from normalized Laplacian + noise
10:42:56 Commencing optimization for 500 epochs, with 156440 positive edges
10:43:07 Optimization finished
[1] "113 0.12"10:43:08 UMAP embedding parameters a = 1.51 b = 0.9165
10:43:08 Read 1203 rows and found 38 numeric columns
10:43:08 Using Annoy for neighbor search, n_neighbors = 113
10:43:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:43:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723a93a35
10:43:08 Searching Annoy index using 1 thread, search_k = 11300
10:43:09 Annoy recall = 100%
10:43:17 Commencing smooth kNN distance calibration using 1 thread
10:43:32 Initializing from normalized Laplacian + noise
10:43:32 Commencing optimization for 500 epochs, with 156440 positive edges
10:43:44 Optimization finished
[1] "113 0.13"10:43:44 UMAP embedding parameters a = 1.478 b = 0.9272
10:43:44 Read 1203 rows and found 38 numeric columns
10:43:44 Using Annoy for neighbor search, n_neighbors = 113
10:43:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:43:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8792c7396
10:43:44 Searching Annoy index using 1 thread, search_k = 11300
10:43:45 Annoy recall = 100%
10:43:53 Commencing smooth kNN distance calibration using 1 thread
10:44:08 Initializing from normalized Laplacian + noise
10:44:08 Commencing optimization for 500 epochs, with 156440 positive edges
10:44:20 Optimization finished
[1] "113 0.14"10:44:20 UMAP embedding parameters a = 1.446 b = 0.938
10:44:20 Read 1203 rows and found 38 numeric columns
10:44:20 Using Annoy for neighbor search, n_neighbors = 113
10:44:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:44:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877414e28f
10:44:21 Searching Annoy index using 1 thread, search_k = 11300
10:44:22 Annoy recall = 100%
10:44:29 Commencing smooth kNN distance calibration using 1 thread
10:44:45 Initializing from normalized Laplacian + noise
10:44:45 Commencing optimization for 500 epochs, with 156440 positive edges
10:44:56 Optimization finished
[1] "113 0.15"10:44:57 UMAP embedding parameters a = 1.414 b = 0.9488
10:44:57 Read 1203 rows and found 38 numeric columns
10:44:57 Using Annoy for neighbor search, n_neighbors = 113
10:44:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:44:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e3d0d50
10:44:57 Searching Annoy index using 1 thread, search_k = 11300
10:44:58 Annoy recall = 100%
10:45:06 Commencing smooth kNN distance calibration using 1 thread
10:45:21 Initializing from normalized Laplacian + noise
10:45:21 Commencing optimization for 500 epochs, with 156440 positive edges
10:45:33 Optimization finished
[1] "113 0.16"10:45:33 UMAP embedding parameters a = 1.383 b = 0.9596
10:45:33 Read 1203 rows and found 38 numeric columns
10:45:33 Using Annoy for neighbor search, n_neighbors = 113
10:45:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:45:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d8168a7
10:45:34 Searching Annoy index using 1 thread, search_k = 11300
10:45:35 Annoy recall = 100%
10:45:42 Commencing smooth kNN distance calibration using 1 thread
10:45:58 Initializing from normalized Laplacian + noise
10:45:58 Commencing optimization for 500 epochs, with 156440 positive edges
10:46:09 Optimization finished
[1] "113 0.17"10:46:09 UMAP embedding parameters a = 1.352 b = 0.9704
10:46:09 Read 1203 rows and found 38 numeric columns
10:46:09 Using Annoy for neighbor search, n_neighbors = 113
10:46:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:46:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a0d4767
10:46:10 Searching Annoy index using 1 thread, search_k = 11300
10:46:11 Annoy recall = 100%
10:46:19 Commencing smooth kNN distance calibration using 1 thread
10:46:34 Initializing from normalized Laplacian + noise
10:46:34 Commencing optimization for 500 epochs, with 156440 positive edges
10:46:46 Optimization finished
[1] "113 0.18"10:46:46 UMAP embedding parameters a = 1.321 b = 0.9813
10:46:46 Read 1203 rows and found 38 numeric columns
10:46:46 Using Annoy for neighbor search, n_neighbors = 113
10:46:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:46:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875967d32f
10:46:47 Searching Annoy index using 1 thread, search_k = 11300
10:46:47 Annoy recall = 100%
10:46:55 Commencing smooth kNN distance calibration using 1 thread
10:47:10 Initializing from normalized Laplacian + noise
10:47:10 Commencing optimization for 500 epochs, with 156440 positive edges
10:47:22 Optimization finished
[1] "113 0.19"10:47:22 UMAP embedding parameters a = 1.292 b = 0.9921
10:47:22 Read 1203 rows and found 38 numeric columns
10:47:22 Using Annoy for neighbor search, n_neighbors = 113
10:47:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:47:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770f62e6b
10:47:23 Searching Annoy index using 1 thread, search_k = 11300
10:47:24 Annoy recall = 100%
10:47:31 Commencing smooth kNN distance calibration using 1 thread
10:47:47 Initializing from normalized Laplacian + noise
10:47:47 Commencing optimization for 500 epochs, with 156440 positive edges
10:47:59 Optimization finished
[1] "113 0.2"10:47:59 UMAP embedding parameters a = 1.262 b = 1.003
10:47:59 Read 1203 rows and found 38 numeric columns
10:47:59 Using Annoy for neighbor search, n_neighbors = 113
10:47:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:47:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716d4e4d4
10:47:59 Searching Annoy index using 1 thread, search_k = 11300
10:48:00 Annoy recall = 100%
10:48:08 Commencing smooth kNN distance calibration using 1 thread
10:48:23 Initializing from normalized Laplacian + noise
10:48:23 Commencing optimization for 500 epochs, with 156440 positive edges
10:48:35 Optimization finished
[1] "114 0"10:48:35 UMAP embedding parameters a = 1.933 b = 0.7905
10:48:35 Read 1203 rows and found 38 numeric columns
10:48:35 Using Annoy for neighbor search, n_neighbors = 114
10:48:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:48:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b8c8f73
10:48:36 Searching Annoy index using 1 thread, search_k = 11400
10:48:37 Annoy recall = 100%
10:48:44 Commencing smooth kNN distance calibration using 1 thread
10:49:00 Initializing from normalized Laplacian + noise
10:49:00 Commencing optimization for 500 epochs, with 157728 positive edges
10:49:11 Optimization finished
[1] "114 0.01"10:49:12 UMAP embedding parameters a = 1.896 b = 0.8006
10:49:12 Read 1203 rows and found 38 numeric columns
10:49:12 Using Annoy for neighbor search, n_neighbors = 114
10:49:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:49:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871409ff34
10:49:12 Searching Annoy index using 1 thread, search_k = 11400
10:49:13 Annoy recall = 100%
10:49:21 Commencing smooth kNN distance calibration using 1 thread
10:49:36 Initializing from normalized Laplacian + noise
10:49:36 Commencing optimization for 500 epochs, with 157728 positive edges
10:49:48 Optimization finished
[1] "114 0.02"10:49:48 UMAP embedding parameters a = 1.859 b = 0.8109
10:49:48 Read 1203 rows and found 38 numeric columns
10:49:48 Using Annoy for neighbor search, n_neighbors = 114
10:49:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:49:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722306a61
10:49:49 Searching Annoy index using 1 thread, search_k = 11400
10:49:50 Annoy recall = 100%
10:49:57 Commencing smooth kNN distance calibration using 1 thread
10:50:13 Initializing from normalized Laplacian + noise
10:50:13 Commencing optimization for 500 epochs, with 157728 positive edges
10:50:25 Optimization finished
[1] "114 0.03"10:50:25 UMAP embedding parameters a = 1.822 b = 0.8212
10:50:25 Read 1203 rows and found 38 numeric columns
10:50:25 Using Annoy for neighbor search, n_neighbors = 114
10:50:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:50:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87634922b2
10:50:25 Searching Annoy index using 1 thread, search_k = 11400
10:50:26 Annoy recall = 100%
10:50:34 Commencing smooth kNN distance calibration using 1 thread
10:50:50 Initializing from normalized Laplacian + noise
10:50:50 Commencing optimization for 500 epochs, with 157728 positive edges
10:51:01 Optimization finished
[1] "114 0.04"10:51:01 UMAP embedding parameters a = 1.786 b = 0.8316
10:51:01 Read 1203 rows and found 38 numeric columns
10:51:01 Using Annoy for neighbor search, n_neighbors = 114
10:51:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:51:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737c1f7c7
10:51:02 Searching Annoy index using 1 thread, search_k = 11400
10:51:03 Annoy recall = 100%
10:51:10 Commencing smooth kNN distance calibration using 1 thread
10:51:26 Initializing from normalized Laplacian + noise
10:51:26 Commencing optimization for 500 epochs, with 157728 positive edges
10:51:38 Optimization finished
[1] "114 0.05"10:51:38 UMAP embedding parameters a = 1.75 b = 0.8421
10:51:38 Read 1203 rows and found 38 numeric columns
10:51:38 Using Annoy for neighbor search, n_neighbors = 114
10:51:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:51:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871cc0eb2b
10:51:39 Searching Annoy index using 1 thread, search_k = 11400
10:51:39 Annoy recall = 100%
10:51:47 Commencing smooth kNN distance calibration using 1 thread
10:52:03 Initializing from normalized Laplacian + noise
10:52:03 Commencing optimization for 500 epochs, with 157728 positive edges
10:52:14 Optimization finished
[1] "114 0.06"10:52:14 UMAP embedding parameters a = 1.715 b = 0.8526
10:52:14 Read 1203 rows and found 38 numeric columns
10:52:14 Using Annoy for neighbor search, n_neighbors = 114
10:52:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:52:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87163f5f2d
10:52:15 Searching Annoy index using 1 thread, search_k = 11400
10:52:16 Annoy recall = 100%
10:52:24 Commencing smooth kNN distance calibration using 1 thread
10:52:39 Initializing from normalized Laplacian + noise
10:52:39 Commencing optimization for 500 epochs, with 157728 positive edges
10:52:51 Optimization finished
[1] "114 0.07"10:52:51 UMAP embedding parameters a = 1.68 b = 0.8631
10:52:51 Read 1203 rows and found 38 numeric columns
10:52:51 Using Annoy for neighbor search, n_neighbors = 114
10:52:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:52:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b1f220e
10:52:52 Searching Annoy index using 1 thread, search_k = 11400
10:52:53 Annoy recall = 100%
10:53:00 Commencing smooth kNN distance calibration using 1 thread
10:53:16 Initializing from normalized Laplacian + noise
10:53:16 Commencing optimization for 500 epochs, with 157728 positive edges
10:53:27 Optimization finished
[1] "114 0.08"10:53:28 UMAP embedding parameters a = 1.645 b = 0.8737
10:53:28 Read 1203 rows and found 38 numeric columns
10:53:28 Using Annoy for neighbor search, n_neighbors = 114
10:53:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:53:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772572677
10:53:28 Searching Annoy index using 1 thread, search_k = 11400
10:53:29 Annoy recall = 100%
10:53:37 Commencing smooth kNN distance calibration using 1 thread
10:53:52 Initializing from normalized Laplacian + noise
10:53:53 Commencing optimization for 500 epochs, with 157728 positive edges
10:54:04 Optimization finished
[1] "114 0.09"10:54:04 UMAP embedding parameters a = 1.611 b = 0.8844
10:54:04 Read 1203 rows and found 38 numeric columns
10:54:04 Using Annoy for neighbor search, n_neighbors = 114
10:54:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:54:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721fb3049
10:54:05 Searching Annoy index using 1 thread, search_k = 11400
10:54:06 Annoy recall = 100%
10:54:13 Commencing smooth kNN distance calibration using 1 thread
10:54:29 Initializing from normalized Laplacian + noise
10:54:29 Commencing optimization for 500 epochs, with 157728 positive edges
10:54:41 Optimization finished
[1] "114 0.1"10:54:41 UMAP embedding parameters a = 1.577 b = 0.8951
10:54:41 Read 1203 rows and found 38 numeric columns
10:54:41 Using Annoy for neighbor search, n_neighbors = 114
10:54:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:54:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755de27d7
10:54:41 Searching Annoy index using 1 thread, search_k = 11400
10:54:42 Annoy recall = 100%
10:54:50 Commencing smooth kNN distance calibration using 1 thread
10:55:06 Initializing from normalized Laplacian + noise
10:55:06 Commencing optimization for 500 epochs, with 157728 positive edges
10:55:17 Optimization finished
[1] "114 0.11"10:55:18 UMAP embedding parameters a = 1.544 b = 0.9058
10:55:18 Read 1203 rows and found 38 numeric columns
10:55:18 Using Annoy for neighbor search, n_neighbors = 114
10:55:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:55:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bd6cac4
10:55:18 Searching Annoy index using 1 thread, search_k = 11400
10:55:19 Annoy recall = 100%
10:55:27 Commencing smooth kNN distance calibration using 1 thread
10:55:42 Initializing from normalized Laplacian + noise
10:55:42 Commencing optimization for 500 epochs, with 157728 positive edges
10:55:54 Optimization finished
[1] "114 0.12"10:55:54 UMAP embedding parameters a = 1.51 b = 0.9165
10:55:54 Read 1203 rows and found 38 numeric columns
10:55:54 Using Annoy for neighbor search, n_neighbors = 114
10:55:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:55:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87754dcec3
10:55:55 Searching Annoy index using 1 thread, search_k = 11400
10:55:56 Annoy recall = 100%
10:56:03 Commencing smooth kNN distance calibration using 1 thread
10:56:19 Initializing from normalized Laplacian + noise
10:56:19 Commencing optimization for 500 epochs, with 157728 positive edges
10:56:31 Optimization finished
[1] "114 0.13"10:56:31 UMAP embedding parameters a = 1.478 b = 0.9272
10:56:31 Read 1203 rows and found 38 numeric columns
10:56:31 Using Annoy for neighbor search, n_neighbors = 114
10:56:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:56:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876fc1991f
10:56:31 Searching Annoy index using 1 thread, search_k = 11400
10:56:32 Annoy recall = 100%
10:56:40 Commencing smooth kNN distance calibration using 1 thread
10:56:56 Initializing from normalized Laplacian + noise
10:56:56 Commencing optimization for 500 epochs, with 157728 positive edges
10:57:07 Optimization finished
[1] "114 0.14"10:57:08 UMAP embedding parameters a = 1.446 b = 0.938
10:57:08 Read 1203 rows and found 38 numeric columns
10:57:08 Using Annoy for neighbor search, n_neighbors = 114
10:57:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:57:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87153237eb
10:57:08 Searching Annoy index using 1 thread, search_k = 11400
10:57:09 Annoy recall = 100%
10:57:17 Commencing smooth kNN distance calibration using 1 thread
10:57:32 Initializing from normalized Laplacian + noise
10:57:32 Commencing optimization for 500 epochs, with 157728 positive edges
10:57:44 Optimization finished
[1] "114 0.15"10:57:44 UMAP embedding parameters a = 1.414 b = 0.9488
10:57:44 Read 1203 rows and found 38 numeric columns
10:57:44 Using Annoy for neighbor search, n_neighbors = 114
10:57:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:57:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728fa85f2
10:57:45 Searching Annoy index using 1 thread, search_k = 11400
10:57:46 Annoy recall = 100%
10:57:53 Commencing smooth kNN distance calibration using 1 thread
10:58:09 Initializing from normalized Laplacian + noise
10:58:09 Commencing optimization for 500 epochs, with 157728 positive edges
10:58:21 Optimization finished
[1] "114 0.16"10:58:21 UMAP embedding parameters a = 1.383 b = 0.9596
10:58:21 Read 1203 rows and found 38 numeric columns
10:58:21 Using Annoy for neighbor search, n_neighbors = 114
10:58:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:58:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729d68259
10:58:22 Searching Annoy index using 1 thread, search_k = 11400
10:58:22 Annoy recall = 100%
10:58:30 Commencing smooth kNN distance calibration using 1 thread
10:58:46 Initializing from normalized Laplacian + noise
10:58:46 Commencing optimization for 500 epochs, with 157728 positive edges
10:58:57 Optimization finished
[1] "114 0.17"10:58:58 UMAP embedding parameters a = 1.352 b = 0.9704
10:58:58 Read 1203 rows and found 38 numeric columns
10:58:58 Using Annoy for neighbor search, n_neighbors = 114
10:58:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:58:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ee2804a
10:58:58 Searching Annoy index using 1 thread, search_k = 11400
10:58:59 Annoy recall = 100%
10:59:07 Commencing smooth kNN distance calibration using 1 thread
10:59:22 Initializing from normalized Laplacian + noise
10:59:23 Commencing optimization for 500 epochs, with 157728 positive edges
10:59:34 Optimization finished
[1] "114 0.18"10:59:34 UMAP embedding parameters a = 1.321 b = 0.9813
10:59:34 Read 1203 rows and found 38 numeric columns
10:59:34 Using Annoy for neighbor search, n_neighbors = 114
10:59:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:59:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877228761f
10:59:35 Searching Annoy index using 1 thread, search_k = 11400
10:59:36 Annoy recall = 100%
10:59:43 Commencing smooth kNN distance calibration using 1 thread
10:59:59 Initializing from normalized Laplacian + noise
10:59:59 Commencing optimization for 500 epochs, with 157728 positive edges
11:00:11 Optimization finished
[1] "114 0.19"11:00:11 UMAP embedding parameters a = 1.292 b = 0.9921
11:00:11 Read 1203 rows and found 38 numeric columns
11:00:11 Using Annoy for neighbor search, n_neighbors = 114
11:00:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:00:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879b74f30
11:00:12 Searching Annoy index using 1 thread, search_k = 11400
11:00:13 Annoy recall = 100%
11:00:20 Commencing smooth kNN distance calibration using 1 thread
11:00:36 Initializing from normalized Laplacian + noise
11:00:36 Commencing optimization for 500 epochs, with 157728 positive edges
11:00:47 Optimization finished
[1] "114 0.2"11:00:48 UMAP embedding parameters a = 1.262 b = 1.003
11:00:48 Read 1203 rows and found 38 numeric columns
11:00:48 Using Annoy for neighbor search, n_neighbors = 114
11:00:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:00:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763d71b14
11:00:48 Searching Annoy index using 1 thread, search_k = 11400
11:00:49 Annoy recall = 100%
11:00:57 Commencing smooth kNN distance calibration using 1 thread
11:01:13 Initializing from normalized Laplacian + noise
11:01:13 Commencing optimization for 500 epochs, with 157728 positive edges
11:01:24 Optimization finished
[1] "115 0"11:01:25 UMAP embedding parameters a = 1.933 b = 0.7905
11:01:25 Read 1203 rows and found 38 numeric columns
11:01:25 Using Annoy for neighbor search, n_neighbors = 115
11:01:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:01:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b61d16
11:01:25 Searching Annoy index using 1 thread, search_k = 11500
11:01:26 Annoy recall = 100%
11:01:34 Commencing smooth kNN distance calibration using 1 thread
11:01:49 Initializing from normalized Laplacian + noise
11:01:49 Commencing optimization for 500 epochs, with 158978 positive edges
11:02:01 Optimization finished
[1] "115 0.01"11:02:01 UMAP embedding parameters a = 1.896 b = 0.8006
11:02:01 Read 1203 rows and found 38 numeric columns
11:02:01 Using Annoy for neighbor search, n_neighbors = 115
11:02:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:02:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d608965
11:02:02 Searching Annoy index using 1 thread, search_k = 11500
11:02:03 Annoy recall = 100%
11:02:11 Commencing smooth kNN distance calibration using 1 thread
11:02:26 Initializing from normalized Laplacian + noise
11:02:26 Commencing optimization for 500 epochs, with 158978 positive edges
11:02:38 Optimization finished
[1] "115 0.02"11:02:38 UMAP embedding parameters a = 1.859 b = 0.8109
11:02:38 Read 1203 rows and found 38 numeric columns
11:02:38 Using Annoy for neighbor search, n_neighbors = 115
11:02:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:02:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d038eab
11:02:39 Searching Annoy index using 1 thread, search_k = 11500
11:02:40 Annoy recall = 100%
11:02:47 Commencing smooth kNN distance calibration using 1 thread
11:03:03 Initializing from normalized Laplacian + noise
11:03:04 Commencing optimization for 500 epochs, with 158978 positive edges
11:03:16 Optimization finished
[1] "115 0.03"11:03:16 UMAP embedding parameters a = 1.822 b = 0.8212
11:03:16 Read 1203 rows and found 38 numeric columns
11:03:16 Using Annoy for neighbor search, n_neighbors = 115
11:03:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:03:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775caffa5
11:03:17 Searching Annoy index using 1 thread, search_k = 11500
11:03:17 Annoy recall = 100%
11:03:26 Commencing smooth kNN distance calibration using 1 thread
11:03:43 Initializing from normalized Laplacian + noise
11:03:44 Commencing optimization for 500 epochs, with 158978 positive edges
11:03:56 Optimization finished
[1] "115 0.04"11:03:56 UMAP embedding parameters a = 1.786 b = 0.8316
11:03:56 Read 1203 rows and found 38 numeric columns
11:03:56 Using Annoy for neighbor search, n_neighbors = 115
11:03:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:03:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b9d96b5
11:03:57 Searching Annoy index using 1 thread, search_k = 11500
11:03:57 Annoy recall = 100%
11:04:05 Commencing smooth kNN distance calibration using 1 thread
11:04:21 Initializing from normalized Laplacian + noise
11:04:21 Commencing optimization for 500 epochs, with 158978 positive edges
11:04:33 Optimization finished
[1] "115 0.05"11:04:33 UMAP embedding parameters a = 1.75 b = 0.8421
11:04:33 Read 1203 rows and found 38 numeric columns
11:04:33 Using Annoy for neighbor search, n_neighbors = 115
11:04:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:04:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a84f752
11:04:34 Searching Annoy index using 1 thread, search_k = 11500
11:04:35 Annoy recall = 100%
11:04:43 Commencing smooth kNN distance calibration using 1 thread
11:04:58 Initializing from normalized Laplacian + noise
11:04:58 Commencing optimization for 500 epochs, with 158978 positive edges
11:05:10 Optimization finished
[1] "115 0.06"11:05:10 UMAP embedding parameters a = 1.715 b = 0.8526
11:05:10 Read 1203 rows and found 38 numeric columns
11:05:10 Using Annoy for neighbor search, n_neighbors = 115
11:05:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:05:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fd8470d
11:05:11 Searching Annoy index using 1 thread, search_k = 11500
11:05:11 Annoy recall = 100%
11:05:19 Commencing smooth kNN distance calibration using 1 thread
11:05:35 Initializing from normalized Laplacian + noise
11:05:35 Commencing optimization for 500 epochs, with 158978 positive edges
11:05:47 Optimization finished
[1] "115 0.07"11:05:47 UMAP embedding parameters a = 1.68 b = 0.8631
11:05:47 Read 1203 rows and found 38 numeric columns
11:05:47 Using Annoy for neighbor search, n_neighbors = 115
11:05:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:05:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87550569e5
11:05:47 Searching Annoy index using 1 thread, search_k = 11500
11:05:48 Annoy recall = 100%
11:05:56 Commencing smooth kNN distance calibration using 1 thread
11:06:11 Initializing from normalized Laplacian + noise
11:06:11 Commencing optimization for 500 epochs, with 158978 positive edges
11:06:23 Optimization finished
[1] "115 0.08"11:06:23 UMAP embedding parameters a = 1.645 b = 0.8737
11:06:23 Read 1203 rows and found 38 numeric columns
11:06:23 Using Annoy for neighbor search, n_neighbors = 115
11:06:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:06:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b7b25bd
11:06:24 Searching Annoy index using 1 thread, search_k = 11500
11:06:25 Annoy recall = 100%
11:06:32 Commencing smooth kNN distance calibration using 1 thread
11:06:48 Initializing from normalized Laplacian + noise
11:06:48 Commencing optimization for 500 epochs, with 158978 positive edges
11:07:00 Optimization finished
[1] "115 0.09"11:07:00 UMAP embedding parameters a = 1.611 b = 0.8844
11:07:00 Read 1203 rows and found 38 numeric columns
11:07:00 Using Annoy for neighbor search, n_neighbors = 115
11:07:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:07:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776ad2be1
11:07:00 Searching Annoy index using 1 thread, search_k = 11500
11:07:01 Annoy recall = 100%
11:07:09 Commencing smooth kNN distance calibration using 1 thread
11:07:25 Initializing from normalized Laplacian + noise
11:07:25 Commencing optimization for 500 epochs, with 158978 positive edges
11:07:37 Optimization finished
[1] "115 0.1"11:07:37 UMAP embedding parameters a = 1.577 b = 0.8951
11:07:37 Read 1203 rows and found 38 numeric columns
11:07:37 Using Annoy for neighbor search, n_neighbors = 115
11:07:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:07:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871091f958
11:07:38 Searching Annoy index using 1 thread, search_k = 11500
11:07:38 Annoy recall = 100%
11:07:46 Commencing smooth kNN distance calibration using 1 thread
11:08:02 Initializing from normalized Laplacian + noise
11:08:02 Commencing optimization for 500 epochs, with 158978 positive edges
11:08:15 Optimization finished
[1] "115 0.11"11:08:15 UMAP embedding parameters a = 1.544 b = 0.9058
11:08:15 Read 1203 rows and found 38 numeric columns
11:08:15 Using Annoy for neighbor search, n_neighbors = 115
11:08:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:08:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f8524f2
11:08:15 Searching Annoy index using 1 thread, search_k = 11500
11:08:16 Annoy recall = 100%
11:08:25 Commencing smooth kNN distance calibration using 1 thread
11:08:42 Initializing from normalized Laplacian + noise
11:08:42 Commencing optimization for 500 epochs, with 158978 positive edges
11:08:54 Optimization finished
[1] "115 0.12"11:08:55 UMAP embedding parameters a = 1.51 b = 0.9165
11:08:55 Read 1203 rows and found 38 numeric columns
11:08:55 Using Annoy for neighbor search, n_neighbors = 115
11:08:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:08:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718dd9643
11:08:55 Searching Annoy index using 1 thread, search_k = 11500
11:08:56 Annoy recall = 100%
11:09:04 Commencing smooth kNN distance calibration using 1 thread
11:09:20 Initializing from normalized Laplacian + noise
11:09:20 Commencing optimization for 500 epochs, with 158978 positive edges
11:09:32 Optimization finished
[1] "115 0.13"11:09:32 UMAP embedding parameters a = 1.478 b = 0.9272
11:09:32 Read 1203 rows and found 38 numeric columns
11:09:32 Using Annoy for neighbor search, n_neighbors = 115
11:09:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:09:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773db1c0a
11:09:33 Searching Annoy index using 1 thread, search_k = 11500
11:09:33 Annoy recall = 100%
11:09:41 Commencing smooth kNN distance calibration using 1 thread
11:09:57 Initializing from normalized Laplacian + noise
11:09:57 Commencing optimization for 500 epochs, with 158978 positive edges
11:10:09 Optimization finished
[1] "115 0.14"11:10:09 UMAP embedding parameters a = 1.446 b = 0.938
11:10:09 Read 1203 rows and found 38 numeric columns
11:10:09 Using Annoy for neighbor search, n_neighbors = 115
11:10:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:10:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747471cb9
11:10:09 Searching Annoy index using 1 thread, search_k = 11500
11:10:10 Annoy recall = 100%
11:10:18 Commencing smooth kNN distance calibration using 1 thread
11:10:34 Initializing from normalized Laplacian + noise
11:10:34 Commencing optimization for 500 epochs, with 158978 positive edges
11:10:45 Optimization finished
[1] "115 0.15"11:10:46 UMAP embedding parameters a = 1.414 b = 0.9488
11:10:46 Read 1203 rows and found 38 numeric columns
11:10:46 Using Annoy for neighbor search, n_neighbors = 115
11:10:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:10:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87359e816e
11:10:46 Searching Annoy index using 1 thread, search_k = 11500
11:10:47 Annoy recall = 100%
11:10:55 Commencing smooth kNN distance calibration using 1 thread
11:11:10 Initializing from normalized Laplacian + noise
11:11:10 Commencing optimization for 500 epochs, with 158978 positive edges
11:11:22 Optimization finished
[1] "115 0.16"11:11:22 UMAP embedding parameters a = 1.383 b = 0.9596
11:11:22 Read 1203 rows and found 38 numeric columns
11:11:22 Using Annoy for neighbor search, n_neighbors = 115
11:11:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:11:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a1a7b37
11:11:23 Searching Annoy index using 1 thread, search_k = 11500
11:11:23 Annoy recall = 100%
11:11:31 Commencing smooth kNN distance calibration using 1 thread
11:11:47 Initializing from normalized Laplacian + noise
11:11:47 Commencing optimization for 500 epochs, with 158978 positive edges
11:11:59 Optimization finished
[1] "115 0.17"11:11:59 UMAP embedding parameters a = 1.352 b = 0.9704
11:11:59 Read 1203 rows and found 38 numeric columns
11:11:59 Using Annoy for neighbor search, n_neighbors = 115
11:11:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:11:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742663ec8
11:11:59 Searching Annoy index using 1 thread, search_k = 11500
11:12:00 Annoy recall = 100%
11:12:08 Commencing smooth kNN distance calibration using 1 thread
11:12:23 Initializing from normalized Laplacian + noise
11:12:23 Commencing optimization for 500 epochs, with 158978 positive edges
11:12:35 Optimization finished
[1] "115 0.18"11:12:35 UMAP embedding parameters a = 1.321 b = 0.9813
11:12:35 Read 1203 rows and found 38 numeric columns
11:12:35 Using Annoy for neighbor search, n_neighbors = 115
11:12:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:12:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727f5a7e6
11:12:36 Searching Annoy index using 1 thread, search_k = 11500
11:12:37 Annoy recall = 100%
11:12:45 Commencing smooth kNN distance calibration using 1 thread
11:13:00 Initializing from normalized Laplacian + noise
11:13:00 Commencing optimization for 500 epochs, with 158978 positive edges
11:13:12 Optimization finished
[1] "115 0.19"11:13:12 UMAP embedding parameters a = 1.292 b = 0.9921
11:13:12 Read 1203 rows and found 38 numeric columns
11:13:12 Using Annoy for neighbor search, n_neighbors = 115
11:13:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:13:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c15ab80
11:13:13 Searching Annoy index using 1 thread, search_k = 11500
11:13:13 Annoy recall = 100%
11:13:21 Commencing smooth kNN distance calibration using 1 thread
11:13:37 Initializing from normalized Laplacian + noise
11:13:37 Commencing optimization for 500 epochs, with 158978 positive edges
11:13:48 Optimization finished
[1] "115 0.2"11:13:49 UMAP embedding parameters a = 1.262 b = 1.003
11:13:49 Read 1203 rows and found 38 numeric columns
11:13:49 Using Annoy for neighbor search, n_neighbors = 115
11:13:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:13:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871844669f
11:13:49 Searching Annoy index using 1 thread, search_k = 11500
11:13:50 Annoy recall = 100%
11:13:58 Commencing smooth kNN distance calibration using 1 thread
11:14:13 Initializing from normalized Laplacian + noise
11:14:13 Commencing optimization for 500 epochs, with 158978 positive edges
11:14:25 Optimization finished
[1] "116 0"11:14:25 UMAP embedding parameters a = 1.933 b = 0.7905
11:14:25 Read 1203 rows and found 38 numeric columns
11:14:25 Using Annoy for neighbor search, n_neighbors = 116
11:14:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:14:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cc72aa
11:14:26 Searching Annoy index using 1 thread, search_k = 11600
11:14:27 Annoy recall = 100%
11:14:34 Commencing smooth kNN distance calibration using 1 thread
11:14:50 Initializing from normalized Laplacian + noise
11:14:50 Commencing optimization for 500 epochs, with 160214 positive edges
11:15:02 Optimization finished
[1] "116 0.01"11:15:02 UMAP embedding parameters a = 1.896 b = 0.8006
11:15:02 Read 1203 rows and found 38 numeric columns
11:15:02 Using Annoy for neighbor search, n_neighbors = 116
11:15:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:15:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721637a44
11:15:03 Searching Annoy index using 1 thread, search_k = 11600
11:15:04 Annoy recall = 100%
11:15:11 Commencing smooth kNN distance calibration using 1 thread
11:15:27 Initializing from normalized Laplacian + noise
11:15:27 Commencing optimization for 500 epochs, with 160214 positive edges
11:15:39 Optimization finished
[1] "116 0.02"11:15:39 UMAP embedding parameters a = 1.859 b = 0.8109
11:15:39 Read 1203 rows and found 38 numeric columns
11:15:39 Using Annoy for neighbor search, n_neighbors = 116
11:15:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:15:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87805ffbe
11:15:39 Searching Annoy index using 1 thread, search_k = 11600
11:15:40 Annoy recall = 100%
11:15:48 Commencing smooth kNN distance calibration using 1 thread
11:16:04 Initializing from normalized Laplacian + noise
11:16:04 Commencing optimization for 500 epochs, with 160214 positive edges
11:16:15 Optimization finished
[1] "116 0.03"11:16:16 UMAP embedding parameters a = 1.822 b = 0.8212
11:16:16 Read 1203 rows and found 38 numeric columns
11:16:16 Using Annoy for neighbor search, n_neighbors = 116
11:16:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:16:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718feaa95
11:16:16 Searching Annoy index using 1 thread, search_k = 11600
11:16:17 Annoy recall = 100%
11:16:25 Commencing smooth kNN distance calibration using 1 thread
11:16:41 Initializing from normalized Laplacian + noise
11:16:41 Commencing optimization for 500 epochs, with 160214 positive edges
11:16:52 Optimization finished
[1] "116 0.04"11:16:53 UMAP embedding parameters a = 1.786 b = 0.8316
11:16:53 Read 1203 rows and found 38 numeric columns
11:16:53 Using Annoy for neighbor search, n_neighbors = 116
11:16:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:16:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a5e0036
11:16:53 Searching Annoy index using 1 thread, search_k = 11600
11:16:54 Annoy recall = 100%
11:17:02 Commencing smooth kNN distance calibration using 1 thread
11:17:17 Initializing from normalized Laplacian + noise
11:17:18 Commencing optimization for 500 epochs, with 160214 positive edges
11:17:29 Optimization finished
[1] "116 0.05"11:17:29 UMAP embedding parameters a = 1.75 b = 0.8421
11:17:29 Read 1203 rows and found 38 numeric columns
11:17:29 Using Annoy for neighbor search, n_neighbors = 116
11:17:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:17:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731dc8218
11:17:30 Searching Annoy index using 1 thread, search_k = 11600
11:17:31 Annoy recall = 100%
11:17:39 Commencing smooth kNN distance calibration using 1 thread
11:17:55 Initializing from normalized Laplacian + noise
11:17:55 Commencing optimization for 500 epochs, with 160214 positive edges
11:18:07 Optimization finished
[1] "116 0.06"11:18:07 UMAP embedding parameters a = 1.715 b = 0.8526
11:18:07 Read 1203 rows and found 38 numeric columns
11:18:08 Using Annoy for neighbor search, n_neighbors = 116
11:18:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:18:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e12adf
11:18:08 Searching Annoy index using 1 thread, search_k = 11600
11:18:09 Annoy recall = 100%
11:18:17 Commencing smooth kNN distance calibration using 1 thread
11:18:33 Initializing from normalized Laplacian + noise
11:18:33 Commencing optimization for 500 epochs, with 160214 positive edges
11:18:45 Optimization finished
[1] "116 0.07"11:18:45 UMAP embedding parameters a = 1.68 b = 0.8631
11:18:45 Read 1203 rows and found 38 numeric columns
11:18:45 Using Annoy for neighbor search, n_neighbors = 116
11:18:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:18:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c867655
11:18:46 Searching Annoy index using 1 thread, search_k = 11600
11:18:47 Annoy recall = 100%
11:18:55 Commencing smooth kNN distance calibration using 1 thread
11:19:11 Initializing from normalized Laplacian + noise
11:19:11 Commencing optimization for 500 epochs, with 160214 positive edges
11:19:23 Optimization finished
[1] "116 0.08"11:19:23 UMAP embedding parameters a = 1.645 b = 0.8737
11:19:23 Read 1203 rows and found 38 numeric columns
11:19:23 Using Annoy for neighbor search, n_neighbors = 116
11:19:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:19:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b93d148
11:19:24 Searching Annoy index using 1 thread, search_k = 11600
11:19:25 Annoy recall = 100%
11:19:33 Commencing smooth kNN distance calibration using 1 thread
11:19:49 Initializing from normalized Laplacian + noise
11:19:49 Commencing optimization for 500 epochs, with 160214 positive edges
11:20:01 Optimization finished
[1] "116 0.09"11:20:01 UMAP embedding parameters a = 1.611 b = 0.8844
11:20:01 Read 1203 rows and found 38 numeric columns
11:20:01 Using Annoy for neighbor search, n_neighbors = 116
11:20:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:20:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bb845f3
11:20:02 Searching Annoy index using 1 thread, search_k = 11600
11:20:03 Annoy recall = 100%
11:20:11 Commencing smooth kNN distance calibration using 1 thread
11:20:27 Initializing from normalized Laplacian + noise
11:20:27 Commencing optimization for 500 epochs, with 160214 positive edges
11:20:39 Optimization finished
[1] "116 0.1"11:20:39 UMAP embedding parameters a = 1.577 b = 0.8951
11:20:39 Read 1203 rows and found 38 numeric columns
11:20:39 Using Annoy for neighbor search, n_neighbors = 116
11:20:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:20:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e3c936c
11:20:40 Searching Annoy index using 1 thread, search_k = 11600
11:20:40 Annoy recall = 100%
11:20:48 Commencing smooth kNN distance calibration using 1 thread
11:21:05 Initializing from normalized Laplacian + noise
11:21:05 Commencing optimization for 500 epochs, with 160214 positive edges
11:21:17 Optimization finished
[1] "116 0.11"11:21:17 UMAP embedding parameters a = 1.544 b = 0.9058
11:21:17 Read 1203 rows and found 38 numeric columns
11:21:17 Using Annoy for neighbor search, n_neighbors = 116
11:21:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:21:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768f45aad
11:21:18 Searching Annoy index using 1 thread, search_k = 11600
11:21:18 Annoy recall = 100%
11:21:27 Commencing smooth kNN distance calibration using 1 thread
11:21:43 Initializing from normalized Laplacian + noise
11:21:43 Commencing optimization for 500 epochs, with 160214 positive edges
11:21:55 Optimization finished
[1] "116 0.12"11:21:55 UMAP embedding parameters a = 1.51 b = 0.9165
11:21:55 Read 1203 rows and found 38 numeric columns
11:21:55 Using Annoy for neighbor search, n_neighbors = 116
11:21:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:21:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758bbd49e
11:21:55 Searching Annoy index using 1 thread, search_k = 11600
11:21:56 Annoy recall = 100%
11:22:04 Commencing smooth kNN distance calibration using 1 thread
11:22:21 Initializing from normalized Laplacian + noise
11:22:21 Commencing optimization for 500 epochs, with 160214 positive edges
11:22:33 Optimization finished
[1] "116 0.13"11:22:33 UMAP embedding parameters a = 1.478 b = 0.9272
11:22:33 Read 1203 rows and found 38 numeric columns
11:22:33 Using Annoy for neighbor search, n_neighbors = 116
11:22:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:22:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734079311
11:22:33 Searching Annoy index using 1 thread, search_k = 11600
11:22:34 Annoy recall = 100%
11:22:42 Commencing smooth kNN distance calibration using 1 thread
11:22:59 Initializing from normalized Laplacian + noise
11:22:59 Commencing optimization for 500 epochs, with 160214 positive edges
11:23:11 Optimization finished
[1] "116 0.14"11:23:11 UMAP embedding parameters a = 1.446 b = 0.938
11:23:11 Read 1203 rows and found 38 numeric columns
11:23:11 Using Annoy for neighbor search, n_neighbors = 116
11:23:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:23:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876491f162
11:23:11 Searching Annoy index using 1 thread, search_k = 11600
11:23:12 Annoy recall = 100%
11:23:20 Commencing smooth kNN distance calibration using 1 thread
11:23:37 Initializing from normalized Laplacian + noise
11:23:37 Commencing optimization for 500 epochs, with 160214 positive edges
11:23:49 Optimization finished
[1] "116 0.15"11:23:49 UMAP embedding parameters a = 1.414 b = 0.9488
11:23:49 Read 1203 rows and found 38 numeric columns
11:23:49 Using Annoy for neighbor search, n_neighbors = 116
11:23:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:23:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876340cbf0
11:23:49 Searching Annoy index using 1 thread, search_k = 11600
11:23:50 Annoy recall = 100%
11:23:58 Commencing smooth kNN distance calibration using 1 thread
11:24:15 Initializing from normalized Laplacian + noise
11:24:15 Commencing optimization for 500 epochs, with 160214 positive edges
11:24:27 Optimization finished
[1] "116 0.16"11:24:27 UMAP embedding parameters a = 1.383 b = 0.9596
11:24:27 Read 1203 rows and found 38 numeric columns
11:24:27 Using Annoy for neighbor search, n_neighbors = 116
11:24:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:24:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713dfda1e
11:24:28 Searching Annoy index using 1 thread, search_k = 11600
11:24:28 Annoy recall = 100%
11:24:36 Commencing smooth kNN distance calibration using 1 thread
11:24:52 Initializing from normalized Laplacian + noise
11:24:53 Commencing optimization for 500 epochs, with 160214 positive edges
11:25:05 Optimization finished
[1] "116 0.17"11:25:05 UMAP embedding parameters a = 1.352 b = 0.9704
11:25:05 Read 1203 rows and found 38 numeric columns
11:25:05 Using Annoy for neighbor search, n_neighbors = 116
11:25:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:25:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739975b47
11:25:05 Searching Annoy index using 1 thread, search_k = 11600
11:25:06 Annoy recall = 100%
11:25:14 Commencing smooth kNN distance calibration using 1 thread
11:25:31 Initializing from normalized Laplacian + noise
11:25:31 Commencing optimization for 500 epochs, with 160214 positive edges
11:25:43 Optimization finished
[1] "116 0.18"11:25:43 UMAP embedding parameters a = 1.321 b = 0.9813
11:25:43 Read 1203 rows and found 38 numeric columns
11:25:43 Using Annoy for neighbor search, n_neighbors = 116
11:25:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:25:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ebbf1ae
11:25:44 Searching Annoy index using 1 thread, search_k = 11600
11:25:44 Annoy recall = 100%
11:25:52 Commencing smooth kNN distance calibration using 1 thread
11:26:09 Initializing from normalized Laplacian + noise
11:26:09 Commencing optimization for 500 epochs, with 160214 positive edges
11:26:21 Optimization finished
[1] "116 0.19"11:26:21 UMAP embedding parameters a = 1.292 b = 0.9921
11:26:21 Read 1203 rows and found 38 numeric columns
11:26:21 Using Annoy for neighbor search, n_neighbors = 116
11:26:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:26:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a8d0600
11:26:22 Searching Annoy index using 1 thread, search_k = 11600
11:26:23 Annoy recall = 100%
11:26:31 Commencing smooth kNN distance calibration using 1 thread
11:26:47 Initializing from normalized Laplacian + noise
11:26:47 Commencing optimization for 500 epochs, with 160214 positive edges
11:26:59 Optimization finished
[1] "116 0.2"11:26:59 UMAP embedding parameters a = 1.262 b = 1.003
11:26:59 Read 1203 rows and found 38 numeric columns
11:26:59 Using Annoy for neighbor search, n_neighbors = 116
11:26:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:27:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a29549f
11:27:00 Searching Annoy index using 1 thread, search_k = 11600
11:27:01 Annoy recall = 100%
11:27:09 Commencing smooth kNN distance calibration using 1 thread
11:27:25 Initializing from normalized Laplacian + noise
11:27:25 Commencing optimization for 500 epochs, with 160214 positive edges
11:27:37 Optimization finished
[1] "117 0"11:27:37 UMAP embedding parameters a = 1.933 b = 0.7905
11:27:37 Read 1203 rows and found 38 numeric columns
11:27:37 Using Annoy for neighbor search, n_neighbors = 117
11:27:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:27:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e4116a0
11:27:38 Searching Annoy index using 1 thread, search_k = 11700
11:27:39 Annoy recall = 100%
11:27:47 Commencing smooth kNN distance calibration using 1 thread
11:28:03 Initializing from normalized Laplacian + noise
11:28:03 Commencing optimization for 500 epochs, with 161484 positive edges
11:28:15 Optimization finished
[1] "117 0.01"11:28:15 UMAP embedding parameters a = 1.896 b = 0.8006
11:28:15 Read 1203 rows and found 38 numeric columns
11:28:15 Using Annoy for neighbor search, n_neighbors = 117
11:28:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:28:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87236a9c43
11:28:16 Searching Annoy index using 1 thread, search_k = 11700
11:28:17 Annoy recall = 100%
11:28:25 Commencing smooth kNN distance calibration using 1 thread
11:28:41 Initializing from normalized Laplacian + noise
11:28:41 Commencing optimization for 500 epochs, with 161484 positive edges
11:28:53 Optimization finished
[1] "117 0.02"11:28:54 UMAP embedding parameters a = 1.859 b = 0.8109
11:28:54 Read 1203 rows and found 38 numeric columns
11:28:54 Using Annoy for neighbor search, n_neighbors = 117
11:28:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:28:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e0470a9
11:28:54 Searching Annoy index using 1 thread, search_k = 11700
11:28:55 Annoy recall = 100%
11:29:03 Commencing smooth kNN distance calibration using 1 thread
11:29:19 Initializing from normalized Laplacian + noise
11:29:20 Commencing optimization for 500 epochs, with 161484 positive edges
11:29:32 Optimization finished
[1] "117 0.03"11:29:32 UMAP embedding parameters a = 1.822 b = 0.8212
11:29:32 Read 1203 rows and found 38 numeric columns
11:29:32 Using Annoy for neighbor search, n_neighbors = 117
11:29:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:29:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735883359
11:29:32 Searching Annoy index using 1 thread, search_k = 11700
11:29:33 Annoy recall = 100%
11:29:41 Commencing smooth kNN distance calibration using 1 thread
11:29:58 Initializing from normalized Laplacian + noise
11:29:58 Commencing optimization for 500 epochs, with 161484 positive edges
11:30:10 Optimization finished
[1] "117 0.04"11:30:10 UMAP embedding parameters a = 1.786 b = 0.8316
11:30:10 Read 1203 rows and found 38 numeric columns
11:30:10 Using Annoy for neighbor search, n_neighbors = 117
11:30:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:30:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759091db1
11:30:11 Searching Annoy index using 1 thread, search_k = 11700
11:30:11 Annoy recall = 100%
11:30:20 Commencing smooth kNN distance calibration using 1 thread
11:30:36 Initializing from normalized Laplacian + noise
11:30:36 Commencing optimization for 500 epochs, with 161484 positive edges
11:30:48 Optimization finished
[1] "117 0.05"11:30:48 UMAP embedding parameters a = 1.75 b = 0.8421
11:30:48 Read 1203 rows and found 38 numeric columns
11:30:48 Using Annoy for neighbor search, n_neighbors = 117
11:30:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:30:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87481eebe1
11:30:49 Searching Annoy index using 1 thread, search_k = 11700
11:30:50 Annoy recall = 100%
11:30:58 Commencing smooth kNN distance calibration using 1 thread
11:31:14 Initializing from normalized Laplacian + noise
11:31:14 Commencing optimization for 500 epochs, with 161484 positive edges
11:31:26 Optimization finished
[1] "117 0.06"11:31:26 UMAP embedding parameters a = 1.715 b = 0.8526
11:31:26 Read 1203 rows and found 38 numeric columns
11:31:26 Using Annoy for neighbor search, n_neighbors = 117
11:31:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:31:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777ee7221
11:31:27 Searching Annoy index using 1 thread, search_k = 11700
11:31:28 Annoy recall = 100%
11:31:36 Commencing smooth kNN distance calibration using 1 thread
11:31:52 Initializing from normalized Laplacian + noise
11:31:53 Commencing optimization for 500 epochs, with 161484 positive edges
11:32:05 Optimization finished
[1] "117 0.07"11:32:05 UMAP embedding parameters a = 1.68 b = 0.8631
11:32:05 Read 1203 rows and found 38 numeric columns
11:32:05 Using Annoy for neighbor search, n_neighbors = 117
11:32:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:32:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fec597
11:32:05 Searching Annoy index using 1 thread, search_k = 11700
11:32:06 Annoy recall = 100%
11:32:14 Commencing smooth kNN distance calibration using 1 thread
11:32:31 Initializing from normalized Laplacian + noise
11:32:31 Commencing optimization for 500 epochs, with 161484 positive edges
11:32:43 Optimization finished
[1] "117 0.08"11:32:43 UMAP embedding parameters a = 1.645 b = 0.8737
11:32:43 Read 1203 rows and found 38 numeric columns
11:32:43 Using Annoy for neighbor search, n_neighbors = 117
11:32:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:32:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774349761
11:32:44 Searching Annoy index using 1 thread, search_k = 11700
11:32:44 Annoy recall = 100%
11:32:53 Commencing smooth kNN distance calibration using 1 thread
11:33:09 Initializing from normalized Laplacian + noise
11:33:09 Commencing optimization for 500 epochs, with 161484 positive edges
11:33:21 Optimization finished
[1] "117 0.09"11:33:21 UMAP embedding parameters a = 1.611 b = 0.8844
11:33:21 Read 1203 rows and found 38 numeric columns
11:33:21 Using Annoy for neighbor search, n_neighbors = 117
11:33:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:33:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871032d8c0
11:33:22 Searching Annoy index using 1 thread, search_k = 11700
11:33:23 Annoy recall = 100%
11:33:31 Commencing smooth kNN distance calibration using 1 thread
11:33:47 Initializing from normalized Laplacian + noise
11:33:47 Commencing optimization for 500 epochs, with 161484 positive edges
11:33:59 Optimization finished
[1] "117 0.1"11:34:00 UMAP embedding parameters a = 1.577 b = 0.8951
11:34:00 Read 1203 rows and found 38 numeric columns
11:34:00 Using Annoy for neighbor search, n_neighbors = 117
11:34:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:34:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874cb3841
11:34:00 Searching Annoy index using 1 thread, search_k = 11700
11:34:01 Annoy recall = 100%
11:34:09 Commencing smooth kNN distance calibration using 1 thread
11:34:25 Initializing from normalized Laplacian + noise
11:34:25 Commencing optimization for 500 epochs, with 161484 positive edges
11:34:38 Optimization finished
[1] "117 0.11"11:34:38 UMAP embedding parameters a = 1.544 b = 0.9058
11:34:38 Read 1203 rows and found 38 numeric columns
11:34:38 Using Annoy for neighbor search, n_neighbors = 117
11:34:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:34:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87159811a5
11:34:38 Searching Annoy index using 1 thread, search_k = 11700
11:34:39 Annoy recall = 100%
11:34:47 Commencing smooth kNN distance calibration using 1 thread
11:35:04 Initializing from normalized Laplacian + noise
11:35:04 Commencing optimization for 500 epochs, with 161484 positive edges
11:35:16 Optimization finished
[1] "117 0.12"11:35:16 UMAP embedding parameters a = 1.51 b = 0.9165
11:35:16 Read 1203 rows and found 38 numeric columns
11:35:16 Using Annoy for neighbor search, n_neighbors = 117
11:35:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:35:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871838d87f
11:35:17 Searching Annoy index using 1 thread, search_k = 11700
11:35:18 Annoy recall = 100%
11:35:26 Commencing smooth kNN distance calibration using 1 thread
11:35:42 Initializing from normalized Laplacian + noise
11:35:42 Commencing optimization for 500 epochs, with 161484 positive edges
11:35:54 Optimization finished
[1] "117 0.13"11:35:55 UMAP embedding parameters a = 1.478 b = 0.9272
11:35:55 Read 1203 rows and found 38 numeric columns
11:35:55 Using Annoy for neighbor search, n_neighbors = 117
11:35:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:35:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871dc9e2d6
11:35:55 Searching Annoy index using 1 thread, search_k = 11700
11:35:56 Annoy recall = 100%
11:36:04 Commencing smooth kNN distance calibration using 1 thread
11:36:20 Initializing from normalized Laplacian + noise
11:36:20 Commencing optimization for 500 epochs, with 161484 positive edges
11:36:32 Optimization finished
[1] "117 0.14"11:36:32 UMAP embedding parameters a = 1.446 b = 0.938
11:36:32 Read 1203 rows and found 38 numeric columns
11:36:33 Using Annoy for neighbor search, n_neighbors = 117
11:36:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:36:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ff611dc
11:36:33 Searching Annoy index using 1 thread, search_k = 11700
11:36:34 Annoy recall = 100%
11:36:42 Commencing smooth kNN distance calibration using 1 thread
11:36:58 Initializing from normalized Laplacian + noise
11:36:58 Commencing optimization for 500 epochs, with 161484 positive edges
11:37:10 Optimization finished
[1] "117 0.15"11:37:11 UMAP embedding parameters a = 1.414 b = 0.9488
11:37:11 Read 1203 rows and found 38 numeric columns
11:37:11 Using Annoy for neighbor search, n_neighbors = 117
11:37:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:37:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a155a97
11:37:11 Searching Annoy index using 1 thread, search_k = 11700
11:37:12 Annoy recall = 100%
11:37:20 Commencing smooth kNN distance calibration using 1 thread
11:37:36 Initializing from normalized Laplacian + noise
11:37:36 Commencing optimization for 500 epochs, with 161484 positive edges
11:37:48 Optimization finished
[1] "117 0.16"11:37:48 UMAP embedding parameters a = 1.383 b = 0.9596
11:37:48 Read 1203 rows and found 38 numeric columns
11:37:48 Using Annoy for neighbor search, n_neighbors = 117
11:37:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:37:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725ab0db5
11:37:49 Searching Annoy index using 1 thread, search_k = 11700
11:37:50 Annoy recall = 100%
11:37:58 Commencing smooth kNN distance calibration using 1 thread
11:38:14 Initializing from normalized Laplacian + noise
11:38:14 Commencing optimization for 500 epochs, with 161484 positive edges
11:38:26 Optimization finished
[1] "117 0.17"11:38:26 UMAP embedding parameters a = 1.352 b = 0.9704
11:38:26 Read 1203 rows and found 38 numeric columns
11:38:26 Using Annoy for neighbor search, n_neighbors = 117
11:38:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:38:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c7c8831
11:38:27 Searching Annoy index using 1 thread, search_k = 11700
11:38:28 Annoy recall = 100%
11:38:36 Commencing smooth kNN distance calibration using 1 thread
11:38:52 Initializing from normalized Laplacian + noise
11:38:52 Commencing optimization for 500 epochs, with 161484 positive edges
11:39:04 Optimization finished
[1] "117 0.18"11:39:04 UMAP embedding parameters a = 1.321 b = 0.9813
11:39:04 Read 1203 rows and found 38 numeric columns
11:39:04 Using Annoy for neighbor search, n_neighbors = 117
11:39:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:39:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a92bdf
11:39:05 Searching Annoy index using 1 thread, search_k = 11700
11:39:06 Annoy recall = 100%
11:39:14 Commencing smooth kNN distance calibration using 1 thread
11:39:30 Initializing from normalized Laplacian + noise
11:39:31 Commencing optimization for 500 epochs, with 161484 positive edges
11:39:43 Optimization finished
[1] "117 0.19"11:39:43 UMAP embedding parameters a = 1.292 b = 0.9921
11:39:43 Read 1203 rows and found 38 numeric columns
11:39:43 Using Annoy for neighbor search, n_neighbors = 117
11:39:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:39:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87116353a9
11:39:43 Searching Annoy index using 1 thread, search_k = 11700
11:39:44 Annoy recall = 100%
11:39:52 Commencing smooth kNN distance calibration using 1 thread
11:40:09 Initializing from normalized Laplacian + noise
11:40:09 Commencing optimization for 500 epochs, with 161484 positive edges
11:40:21 Optimization finished
[1] "117 0.2"11:40:21 UMAP embedding parameters a = 1.262 b = 1.003
11:40:21 Read 1203 rows and found 38 numeric columns
11:40:21 Using Annoy for neighbor search, n_neighbors = 117
11:40:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:40:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ab91b9d
11:40:22 Searching Annoy index using 1 thread, search_k = 11700
11:40:23 Annoy recall = 100%
11:40:31 Commencing smooth kNN distance calibration using 1 thread
11:40:47 Initializing from normalized Laplacian + noise
11:40:47 Commencing optimization for 500 epochs, with 161484 positive edges
11:40:59 Optimization finished
[1] "118 0"11:40:59 UMAP embedding parameters a = 1.933 b = 0.7905
11:40:59 Read 1203 rows and found 38 numeric columns
11:40:59 Using Annoy for neighbor search, n_neighbors = 118
11:40:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:41:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e9d868c
11:41:00 Searching Annoy index using 1 thread, search_k = 11800
11:41:01 Annoy recall = 100%
11:41:09 Commencing smooth kNN distance calibration using 1 thread
11:41:26 Initializing from normalized Laplacian + noise
11:41:26 Commencing optimization for 500 epochs, with 162756 positive edges
11:41:38 Optimization finished
[1] "118 0.01"11:41:38 UMAP embedding parameters a = 1.896 b = 0.8006
11:41:38 Read 1203 rows and found 38 numeric columns
11:41:38 Using Annoy for neighbor search, n_neighbors = 118
11:41:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:41:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a1f2847
11:41:38 Searching Annoy index using 1 thread, search_k = 11800
11:41:39 Annoy recall = 100%
11:41:47 Commencing smooth kNN distance calibration using 1 thread
11:42:04 Initializing from normalized Laplacian + noise
11:42:04 Commencing optimization for 500 epochs, with 162756 positive edges
11:42:16 Optimization finished
[1] "118 0.02"11:42:16 UMAP embedding parameters a = 1.859 b = 0.8109
11:42:16 Read 1203 rows and found 38 numeric columns
11:42:16 Using Annoy for neighbor search, n_neighbors = 118
11:42:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:42:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ec0aeaf
11:42:17 Searching Annoy index using 1 thread, search_k = 11800
11:42:18 Annoy recall = 100%
11:42:26 Commencing smooth kNN distance calibration using 1 thread
11:42:42 Initializing from normalized Laplacian + noise
11:42:42 Commencing optimization for 500 epochs, with 162756 positive edges
11:42:54 Optimization finished
[1] "118 0.03"11:42:54 UMAP embedding parameters a = 1.822 b = 0.8212
11:42:54 Read 1203 rows and found 38 numeric columns
11:42:54 Using Annoy for neighbor search, n_neighbors = 118
11:42:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:42:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87532f77ee
11:42:55 Searching Annoy index using 1 thread, search_k = 11800
11:42:56 Annoy recall = 100%
11:43:04 Commencing smooth kNN distance calibration using 1 thread
11:43:20 Initializing from normalized Laplacian + noise
11:43:20 Commencing optimization for 500 epochs, with 162756 positive edges
11:43:32 Optimization finished
[1] "118 0.04"11:43:32 UMAP embedding parameters a = 1.786 b = 0.8316
11:43:32 Read 1203 rows and found 38 numeric columns
11:43:32 Using Annoy for neighbor search, n_neighbors = 118
11:43:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:43:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d5ff438
11:43:33 Searching Annoy index using 1 thread, search_k = 11800
11:43:34 Annoy recall = 100%
11:43:42 Commencing smooth kNN distance calibration using 1 thread
11:43:57 Initializing from normalized Laplacian + noise
11:43:57 Commencing optimization for 500 epochs, with 162756 positive edges
11:44:09 Optimization finished
[1] "118 0.05"11:44:10 UMAP embedding parameters a = 1.75 b = 0.8421
11:44:10 Read 1203 rows and found 38 numeric columns
11:44:10 Using Annoy for neighbor search, n_neighbors = 118
11:44:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:44:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722a088cd
11:44:10 Searching Annoy index using 1 thread, search_k = 11800
11:44:11 Annoy recall = 100%
11:44:19 Commencing smooth kNN distance calibration using 1 thread
11:44:35 Initializing from normalized Laplacian + noise
11:44:35 Commencing optimization for 500 epochs, with 162756 positive edges
11:44:47 Optimization finished
[1] "118 0.06"11:44:47 UMAP embedding parameters a = 1.715 b = 0.8526
11:44:47 Read 1203 rows and found 38 numeric columns
11:44:47 Using Annoy for neighbor search, n_neighbors = 118
11:44:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:44:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cc6d336
11:44:48 Searching Annoy index using 1 thread, search_k = 11800
11:44:49 Annoy recall = 100%
11:44:57 Commencing smooth kNN distance calibration using 1 thread
11:45:13 Initializing from normalized Laplacian + noise
11:45:13 Commencing optimization for 500 epochs, with 162756 positive edges
11:45:25 Optimization finished
[1] "118 0.07"11:45:25 UMAP embedding parameters a = 1.68 b = 0.8631
11:45:25 Read 1203 rows and found 38 numeric columns
11:45:25 Using Annoy for neighbor search, n_neighbors = 118
11:45:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:45:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c1be5e6
11:45:26 Searching Annoy index using 1 thread, search_k = 11800
11:45:27 Annoy recall = 100%
11:45:35 Commencing smooth kNN distance calibration using 1 thread
11:45:51 Initializing from normalized Laplacian + noise
11:45:51 Commencing optimization for 500 epochs, with 162756 positive edges
11:46:02 Optimization finished
[1] "118 0.08"11:46:03 UMAP embedding parameters a = 1.645 b = 0.8737
11:46:03 Read 1203 rows and found 38 numeric columns
11:46:03 Using Annoy for neighbor search, n_neighbors = 118
11:46:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:46:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d2d8ecd
11:46:03 Searching Annoy index using 1 thread, search_k = 11800
11:46:04 Annoy recall = 100%
11:46:12 Commencing smooth kNN distance calibration using 1 thread
11:46:28 Initializing from normalized Laplacian + noise
11:46:28 Commencing optimization for 500 epochs, with 162756 positive edges
11:46:40 Optimization finished
[1] "118 0.09"11:46:41 UMAP embedding parameters a = 1.611 b = 0.8844
11:46:41 Read 1203 rows and found 38 numeric columns
11:46:41 Using Annoy for neighbor search, n_neighbors = 118
11:46:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:46:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756f027d5
11:46:41 Searching Annoy index using 1 thread, search_k = 11800
11:46:42 Annoy recall = 100%
11:46:50 Commencing smooth kNN distance calibration using 1 thread
11:47:06 Initializing from normalized Laplacian + noise
11:47:06 Commencing optimization for 500 epochs, with 162756 positive edges
11:47:18 Optimization finished
[1] "118 0.1"11:47:18 UMAP embedding parameters a = 1.577 b = 0.8951
11:47:18 Read 1203 rows and found 38 numeric columns
11:47:18 Using Annoy for neighbor search, n_neighbors = 118
11:47:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:47:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a5cfc86
11:47:19 Searching Annoy index using 1 thread, search_k = 11800
11:47:19 Annoy recall = 100%
11:47:27 Commencing smooth kNN distance calibration using 1 thread
11:47:44 Initializing from normalized Laplacian + noise
11:47:44 Commencing optimization for 500 epochs, with 162756 positive edges
11:47:55 Optimization finished
[1] "118 0.11"11:47:56 UMAP embedding parameters a = 1.544 b = 0.9058
11:47:56 Read 1203 rows and found 38 numeric columns
11:47:56 Using Annoy for neighbor search, n_neighbors = 118
11:47:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:47:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750982b10
11:47:56 Searching Annoy index using 1 thread, search_k = 11800
11:47:57 Annoy recall = 100%
11:48:05 Commencing smooth kNN distance calibration using 1 thread
11:48:21 Initializing from normalized Laplacian + noise
11:48:21 Commencing optimization for 500 epochs, with 162756 positive edges
11:48:33 Optimization finished
[1] "118 0.12"11:48:33 UMAP embedding parameters a = 1.51 b = 0.9165
11:48:33 Read 1203 rows and found 38 numeric columns
11:48:33 Using Annoy for neighbor search, n_neighbors = 118
11:48:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:48:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714f4987f
11:48:34 Searching Annoy index using 1 thread, search_k = 11800
11:48:35 Annoy recall = 100%
11:48:43 Commencing smooth kNN distance calibration using 1 thread
11:48:59 Initializing from normalized Laplacian + noise
11:48:59 Commencing optimization for 500 epochs, with 162756 positive edges
11:49:11 Optimization finished
[1] "118 0.13"11:49:11 UMAP embedding parameters a = 1.478 b = 0.9272
11:49:11 Read 1203 rows and found 38 numeric columns
11:49:11 Using Annoy for neighbor search, n_neighbors = 118
11:49:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:49:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fe52fdf
11:49:11 Searching Annoy index using 1 thread, search_k = 11800
11:49:12 Annoy recall = 100%
11:49:20 Commencing smooth kNN distance calibration using 1 thread
11:49:36 Initializing from normalized Laplacian + noise
11:49:36 Commencing optimization for 500 epochs, with 162756 positive edges
11:49:48 Optimization finished
[1] "118 0.14"11:49:49 UMAP embedding parameters a = 1.446 b = 0.938
11:49:49 Read 1203 rows and found 38 numeric columns
11:49:49 Using Annoy for neighbor search, n_neighbors = 118
11:49:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:49:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729a148c2
11:49:49 Searching Annoy index using 1 thread, search_k = 11800
11:49:50 Annoy recall = 100%
11:49:58 Commencing smooth kNN distance calibration using 1 thread
11:50:14 Initializing from normalized Laplacian + noise
11:50:14 Commencing optimization for 500 epochs, with 162756 positive edges
11:50:26 Optimization finished
[1] "118 0.15"11:50:26 UMAP embedding parameters a = 1.414 b = 0.9488
11:50:26 Read 1203 rows and found 38 numeric columns
11:50:26 Using Annoy for neighbor search, n_neighbors = 118
11:50:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:50:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d138460
11:50:27 Searching Annoy index using 1 thread, search_k = 11800
11:50:28 Annoy recall = 100%
11:50:36 Commencing smooth kNN distance calibration using 1 thread
11:50:52 Initializing from normalized Laplacian + noise
11:50:52 Commencing optimization for 500 epochs, with 162756 positive edges
11:51:04 Optimization finished
[1] "118 0.16"11:51:04 UMAP embedding parameters a = 1.383 b = 0.9596
11:51:04 Read 1203 rows and found 38 numeric columns
11:51:04 Using Annoy for neighbor search, n_neighbors = 118
11:51:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:51:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747d3a201
11:51:04 Searching Annoy index using 1 thread, search_k = 11800
11:51:05 Annoy recall = 100%
11:51:13 Commencing smooth kNN distance calibration using 1 thread
11:51:29 Initializing from normalized Laplacian + noise
11:51:29 Commencing optimization for 500 epochs, with 162756 positive edges
11:51:41 Optimization finished
[1] "118 0.17"11:51:42 UMAP embedding parameters a = 1.352 b = 0.9704
11:51:42 Read 1203 rows and found 38 numeric columns
11:51:42 Using Annoy for neighbor search, n_neighbors = 118
11:51:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:51:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872aa00e59
11:51:42 Searching Annoy index using 1 thread, search_k = 11800
11:51:43 Annoy recall = 100%
11:51:51 Commencing smooth kNN distance calibration using 1 thread
11:52:07 Initializing from normalized Laplacian + noise
11:52:07 Commencing optimization for 500 epochs, with 162756 positive edges
11:52:19 Optimization finished
[1] "118 0.18"11:52:19 UMAP embedding parameters a = 1.321 b = 0.9813
11:52:19 Read 1203 rows and found 38 numeric columns
11:52:19 Using Annoy for neighbor search, n_neighbors = 118
11:52:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:52:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751481bc1
11:52:20 Searching Annoy index using 1 thread, search_k = 11800
11:52:21 Annoy recall = 100%
11:52:29 Commencing smooth kNN distance calibration using 1 thread
11:52:45 Initializing from normalized Laplacian + noise
11:52:45 Commencing optimization for 500 epochs, with 162756 positive edges
11:52:57 Optimization finished
[1] "118 0.19"11:52:57 UMAP embedding parameters a = 1.292 b = 0.9921
11:52:57 Read 1203 rows and found 38 numeric columns
11:52:57 Using Annoy for neighbor search, n_neighbors = 118
11:52:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:52:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758067ac1
11:52:58 Searching Annoy index using 1 thread, search_k = 11800
11:52:58 Annoy recall = 100%
11:53:06 Commencing smooth kNN distance calibration using 1 thread
11:53:22 Initializing from normalized Laplacian + noise
11:53:22 Commencing optimization for 500 epochs, with 162756 positive edges
11:53:34 Optimization finished
[1] "118 0.2"11:53:35 UMAP embedding parameters a = 1.262 b = 1.003
11:53:35 Read 1203 rows and found 38 numeric columns
11:53:35 Using Annoy for neighbor search, n_neighbors = 118
11:53:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:53:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f6b469b
11:53:35 Searching Annoy index using 1 thread, search_k = 11800
11:53:36 Annoy recall = 100%
11:53:44 Commencing smooth kNN distance calibration using 1 thread
11:54:00 Initializing from normalized Laplacian + noise
11:54:00 Commencing optimization for 500 epochs, with 162756 positive edges
11:54:12 Optimization finished
[1] "119 0"11:54:12 UMAP embedding parameters a = 1.933 b = 0.7905
11:54:12 Read 1203 rows and found 38 numeric columns
11:54:12 Using Annoy for neighbor search, n_neighbors = 119
11:54:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:54:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766e02d67
11:54:13 Searching Annoy index using 1 thread, search_k = 11900
11:54:14 Annoy recall = 100%
11:54:22 Commencing smooth kNN distance calibration using 1 thread
11:54:38 Initializing from normalized Laplacian + noise
11:54:38 Commencing optimization for 500 epochs, with 163994 positive edges
11:54:50 Optimization finished
[1] "119 0.01"11:54:50 UMAP embedding parameters a = 1.896 b = 0.8006
11:54:50 Read 1203 rows and found 38 numeric columns
11:54:50 Using Annoy for neighbor search, n_neighbors = 119
11:54:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:54:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87703f5340
11:54:51 Searching Annoy index using 1 thread, search_k = 11900
11:54:52 Annoy recall = 100%
11:55:00 Commencing smooth kNN distance calibration using 1 thread
11:55:16 Initializing from normalized Laplacian + noise
11:55:16 Commencing optimization for 500 epochs, with 163994 positive edges
11:55:28 Optimization finished
[1] "119 0.02"11:55:28 UMAP embedding parameters a = 1.859 b = 0.8109
11:55:28 Read 1203 rows and found 38 numeric columns
11:55:28 Using Annoy for neighbor search, n_neighbors = 119
11:55:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:55:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d352971
11:55:28 Searching Annoy index using 1 thread, search_k = 11900
11:55:29 Annoy recall = 100%
11:55:37 Commencing smooth kNN distance calibration using 1 thread
11:55:54 Initializing from normalized Laplacian + noise
11:55:54 Commencing optimization for 500 epochs, with 163994 positive edges
11:56:06 Optimization finished
[1] "119 0.03"11:56:07 UMAP embedding parameters a = 1.822 b = 0.8212
11:56:07 Read 1203 rows and found 38 numeric columns
11:56:07 Using Annoy for neighbor search, n_neighbors = 119
11:56:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:56:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746d63f43
11:56:07 Searching Annoy index using 1 thread, search_k = 11900
11:56:08 Annoy recall = 100%
11:56:16 Commencing smooth kNN distance calibration using 1 thread
11:56:32 Initializing from normalized Laplacian + noise
11:56:33 Commencing optimization for 500 epochs, with 163994 positive edges
11:56:45 Optimization finished
[1] "119 0.04"11:56:45 UMAP embedding parameters a = 1.786 b = 0.8316
11:56:45 Read 1203 rows and found 38 numeric columns
11:56:45 Using Annoy for neighbor search, n_neighbors = 119
11:56:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:56:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a54add7
11:56:46 Searching Annoy index using 1 thread, search_k = 11900
11:56:46 Annoy recall = 100%
11:56:55 Commencing smooth kNN distance calibration using 1 thread
11:57:11 Initializing from normalized Laplacian + noise
11:57:11 Commencing optimization for 500 epochs, with 163994 positive edges
11:57:23 Optimization finished
[1] "119 0.05"11:57:23 UMAP embedding parameters a = 1.75 b = 0.8421
11:57:23 Read 1203 rows and found 38 numeric columns
11:57:23 Using Annoy for neighbor search, n_neighbors = 119
11:57:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:57:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772e03727
11:57:24 Searching Annoy index using 1 thread, search_k = 11900
11:57:25 Annoy recall = 100%
11:57:33 Commencing smooth kNN distance calibration using 1 thread
11:57:50 Initializing from normalized Laplacian + noise
11:57:50 Commencing optimization for 500 epochs, with 163994 positive edges
11:58:02 Optimization finished
[1] "119 0.06"11:58:02 UMAP embedding parameters a = 1.715 b = 0.8526
11:58:02 Read 1203 rows and found 38 numeric columns
11:58:02 Using Annoy for neighbor search, n_neighbors = 119
11:58:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:58:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876352c774
11:58:03 Searching Annoy index using 1 thread, search_k = 11900
11:58:04 Annoy recall = 100%
11:58:12 Commencing smooth kNN distance calibration using 1 thread
11:58:28 Initializing from normalized Laplacian + noise
11:58:28 Commencing optimization for 500 epochs, with 163994 positive edges
11:58:40 Optimization finished
[1] "119 0.07"11:58:41 UMAP embedding parameters a = 1.68 b = 0.8631
11:58:41 Read 1203 rows and found 38 numeric columns
11:58:41 Using Annoy for neighbor search, n_neighbors = 119
11:58:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:58:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ffdd9b6
11:58:41 Searching Annoy index using 1 thread, search_k = 11900
11:58:42 Annoy recall = 100%
11:58:50 Commencing smooth kNN distance calibration using 1 thread
11:59:07 Initializing from normalized Laplacian + noise
11:59:07 Commencing optimization for 500 epochs, with 163994 positive edges
11:59:19 Optimization finished
[1] "119 0.08"11:59:19 UMAP embedding parameters a = 1.645 b = 0.8737
11:59:19 Read 1203 rows and found 38 numeric columns
11:59:19 Using Annoy for neighbor search, n_neighbors = 119
11:59:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:59:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874438ad0
11:59:20 Searching Annoy index using 1 thread, search_k = 11900
11:59:21 Annoy recall = 100%
11:59:29 Commencing smooth kNN distance calibration using 1 thread
11:59:45 Initializing from normalized Laplacian + noise
11:59:45 Commencing optimization for 500 epochs, with 163994 positive edges
11:59:58 Optimization finished
[1] "119 0.09"11:59:58 UMAP embedding parameters a = 1.611 b = 0.8844
11:59:58 Read 1203 rows and found 38 numeric columns
11:59:58 Using Annoy for neighbor search, n_neighbors = 119
11:59:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:59:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e0be312
11:59:58 Searching Annoy index using 1 thread, search_k = 11900
11:59:59 Annoy recall = 100%
12:00:08 Commencing smooth kNN distance calibration using 1 thread
12:00:24 Initializing from normalized Laplacian + noise
12:00:24 Commencing optimization for 500 epochs, with 163994 positive edges
12:00:36 Optimization finished
[1] "119 0.1"12:00:36 UMAP embedding parameters a = 1.577 b = 0.8951
12:00:36 Read 1203 rows and found 38 numeric columns
12:00:36 Using Annoy for neighbor search, n_neighbors = 119
12:00:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:00:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e9b6042
12:00:37 Searching Annoy index using 1 thread, search_k = 11900
12:00:38 Annoy recall = 100%
12:00:46 Commencing smooth kNN distance calibration using 1 thread
12:01:03 Initializing from normalized Laplacian + noise
12:01:03 Commencing optimization for 500 epochs, with 163994 positive edges
12:01:15 Optimization finished
[1] "119 0.11"12:01:15 UMAP embedding parameters a = 1.544 b = 0.9058
12:01:15 Read 1203 rows and found 38 numeric columns
12:01:15 Using Annoy for neighbor search, n_neighbors = 119
12:01:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:01:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e62b317
12:01:16 Searching Annoy index using 1 thread, search_k = 11900
12:01:17 Annoy recall = 100%
12:01:25 Commencing smooth kNN distance calibration using 1 thread
12:01:41 Initializing from normalized Laplacian + noise
12:01:41 Commencing optimization for 500 epochs, with 163994 positive edges
12:01:53 Optimization finished
[1] "119 0.12"12:01:54 UMAP embedding parameters a = 1.51 b = 0.9165
12:01:54 Read 1203 rows and found 38 numeric columns
12:01:54 Using Annoy for neighbor search, n_neighbors = 119
12:01:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:01:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ccc91c1
12:01:54 Searching Annoy index using 1 thread, search_k = 11900
12:01:55 Annoy recall = 100%
12:02:03 Commencing smooth kNN distance calibration using 1 thread
12:02:20 Initializing from normalized Laplacian + noise
12:02:20 Commencing optimization for 500 epochs, with 163994 positive edges
12:02:32 Optimization finished
[1] "119 0.13"12:02:32 UMAP embedding parameters a = 1.478 b = 0.9272
12:02:32 Read 1203 rows and found 38 numeric columns
12:02:32 Using Annoy for neighbor search, n_neighbors = 119
12:02:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:02:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871cad831
12:02:33 Searching Annoy index using 1 thread, search_k = 11900
12:02:34 Annoy recall = 100%
12:02:42 Commencing smooth kNN distance calibration using 1 thread
12:02:59 Initializing from normalized Laplacian + noise
12:02:59 Commencing optimization for 500 epochs, with 163994 positive edges
12:03:11 Optimization finished
[1] "119 0.14"12:03:11 UMAP embedding parameters a = 1.446 b = 0.938
12:03:11 Read 1203 rows and found 38 numeric columns
12:03:11 Using Annoy for neighbor search, n_neighbors = 119
12:03:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:03:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bc2a74f
12:03:12 Searching Annoy index using 1 thread, search_k = 11900
12:03:13 Annoy recall = 100%
12:03:21 Commencing smooth kNN distance calibration using 1 thread
12:03:37 Initializing from normalized Laplacian + noise
12:03:37 Commencing optimization for 500 epochs, with 163994 positive edges
12:03:49 Optimization finished
[1] "119 0.15"12:03:50 UMAP embedding parameters a = 1.414 b = 0.9488
12:03:50 Read 1203 rows and found 38 numeric columns
12:03:50 Using Annoy for neighbor search, n_neighbors = 119
12:03:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:03:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f6d1a8e
12:03:50 Searching Annoy index using 1 thread, search_k = 11900
12:03:51 Annoy recall = 100%
12:03:59 Commencing smooth kNN distance calibration using 1 thread
12:04:16 Initializing from normalized Laplacian + noise
12:04:16 Commencing optimization for 500 epochs, with 163994 positive edges
12:04:28 Optimization finished
[1] "119 0.16"12:04:29 UMAP embedding parameters a = 1.383 b = 0.9596
12:04:29 Read 1203 rows and found 38 numeric columns
12:04:29 Using Annoy for neighbor search, n_neighbors = 119
12:04:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:04:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e91ab67
12:04:29 Searching Annoy index using 1 thread, search_k = 11900
12:04:30 Annoy recall = 100%
12:04:38 Commencing smooth kNN distance calibration using 1 thread
12:04:55 Initializing from normalized Laplacian + noise
12:04:55 Commencing optimization for 500 epochs, with 163994 positive edges
12:05:07 Optimization finished
[1] "119 0.17"12:05:07 UMAP embedding parameters a = 1.352 b = 0.9704
12:05:07 Read 1203 rows and found 38 numeric columns
12:05:07 Using Annoy for neighbor search, n_neighbors = 119
12:05:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:05:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767de8d35
12:05:08 Searching Annoy index using 1 thread, search_k = 11900
12:05:09 Annoy recall = 100%
12:05:17 Commencing smooth kNN distance calibration using 1 thread
12:05:34 Initializing from normalized Laplacian + noise
12:05:34 Commencing optimization for 500 epochs, with 163994 positive edges
12:05:46 Optimization finished
[1] "119 0.18"12:05:46 UMAP embedding parameters a = 1.321 b = 0.9813
12:05:46 Read 1203 rows and found 38 numeric columns
12:05:46 Using Annoy for neighbor search, n_neighbors = 119
12:05:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:05:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c9aa95c
12:05:46 Searching Annoy index using 1 thread, search_k = 11900
12:05:47 Annoy recall = 100%
12:05:56 Commencing smooth kNN distance calibration using 1 thread
12:06:12 Initializing from normalized Laplacian + noise
12:06:12 Commencing optimization for 500 epochs, with 163994 positive edges
12:06:25 Optimization finished
[1] "119 0.19"12:06:25 UMAP embedding parameters a = 1.292 b = 0.9921
12:06:25 Read 1203 rows and found 38 numeric columns
12:06:25 Using Annoy for neighbor search, n_neighbors = 119
12:06:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:06:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876581d33c
12:06:25 Searching Annoy index using 1 thread, search_k = 11900
12:06:26 Annoy recall = 100%
12:06:34 Commencing smooth kNN distance calibration using 1 thread
12:06:51 Initializing from normalized Laplacian + noise
12:06:51 Commencing optimization for 500 epochs, with 163994 positive edges
12:07:03 Optimization finished
[1] "119 0.2"12:07:04 UMAP embedding parameters a = 1.262 b = 1.003
12:07:04 Read 1203 rows and found 38 numeric columns
12:07:04 Using Annoy for neighbor search, n_neighbors = 119
12:07:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:07:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723b89bb
12:07:04 Searching Annoy index using 1 thread, search_k = 11900
12:07:05 Annoy recall = 100%
12:07:13 Commencing smooth kNN distance calibration using 1 thread
12:07:30 Initializing from normalized Laplacian + noise
12:07:30 Commencing optimization for 500 epochs, with 163994 positive edges
12:07:42 Optimization finished
[1] "120 0"12:07:42 UMAP embedding parameters a = 1.933 b = 0.7905
12:07:43 Read 1203 rows and found 38 numeric columns
12:07:43 Using Annoy for neighbor search, n_neighbors = 120
12:07:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:07:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d32d46c
12:07:43 Searching Annoy index using 1 thread, search_k = 12000
12:07:44 Annoy recall = 100%
12:07:52 Commencing smooth kNN distance calibration using 1 thread
12:08:09 Initializing from normalized Laplacian + noise
12:08:09 Commencing optimization for 500 epochs, with 165306 positive edges
12:08:21 Optimization finished
[1] "120 0.01"12:08:21 UMAP embedding parameters a = 1.896 b = 0.8006
12:08:21 Read 1203 rows and found 38 numeric columns
12:08:21 Using Annoy for neighbor search, n_neighbors = 120
12:08:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:08:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a766bbb
12:08:22 Searching Annoy index using 1 thread, search_k = 12000
12:08:23 Annoy recall = 100%
12:08:31 Commencing smooth kNN distance calibration using 1 thread
12:08:47 Initializing from normalized Laplacian + noise
12:08:47 Commencing optimization for 500 epochs, with 165306 positive edges
12:08:59 Optimization finished
[1] "120 0.02"12:09:00 UMAP embedding parameters a = 1.859 b = 0.8109
12:09:00 Read 1203 rows and found 38 numeric columns
12:09:00 Using Annoy for neighbor search, n_neighbors = 120
12:09:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:09:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875220b99b
12:09:00 Searching Annoy index using 1 thread, search_k = 12000
12:09:01 Annoy recall = 100%
12:09:09 Commencing smooth kNN distance calibration using 1 thread
12:09:25 Initializing from normalized Laplacian + noise
12:09:26 Commencing optimization for 500 epochs, with 165306 positive edges
12:09:38 Optimization finished
[1] "120 0.03"12:09:38 UMAP embedding parameters a = 1.822 b = 0.8212
12:09:38 Read 1203 rows and found 38 numeric columns
12:09:38 Using Annoy for neighbor search, n_neighbors = 120
12:09:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:09:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716d41d2e
12:09:39 Searching Annoy index using 1 thread, search_k = 12000
12:09:39 Annoy recall = 100%
12:09:47 Commencing smooth kNN distance calibration using 1 thread
12:10:04 Initializing from normalized Laplacian + noise
12:10:04 Commencing optimization for 500 epochs, with 165306 positive edges
12:10:16 Optimization finished
[1] "120 0.04"12:10:16 UMAP embedding parameters a = 1.786 b = 0.8316
12:10:16 Read 1203 rows and found 38 numeric columns
12:10:16 Using Annoy for neighbor search, n_neighbors = 120
12:10:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:10:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875789f01b
12:10:17 Searching Annoy index using 1 thread, search_k = 12000
12:10:18 Annoy recall = 100%
12:10:26 Commencing smooth kNN distance calibration using 1 thread
12:10:42 Initializing from normalized Laplacian + noise
12:10:42 Commencing optimization for 500 epochs, with 165306 positive edges
12:10:54 Optimization finished
[1] "120 0.05"12:10:54 UMAP embedding parameters a = 1.75 b = 0.8421
12:10:54 Read 1203 rows and found 38 numeric columns
12:10:54 Using Annoy for neighbor search, n_neighbors = 120
12:10:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:10:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719f45b9c
12:10:55 Searching Annoy index using 1 thread, search_k = 12000
12:10:56 Annoy recall = 100%
12:11:04 Commencing smooth kNN distance calibration using 1 thread
12:11:20 Initializing from normalized Laplacian + noise
12:11:20 Commencing optimization for 500 epochs, with 165306 positive edges
12:11:32 Optimization finished
[1] "120 0.06"12:11:33 UMAP embedding parameters a = 1.715 b = 0.8526
12:11:33 Read 1203 rows and found 38 numeric columns
12:11:33 Using Annoy for neighbor search, n_neighbors = 120
12:11:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:11:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741742b88
12:11:33 Searching Annoy index using 1 thread, search_k = 12000
12:11:34 Annoy recall = 100%
12:11:42 Commencing smooth kNN distance calibration using 1 thread
12:11:59 Initializing from normalized Laplacian + noise
12:11:59 Commencing optimization for 500 epochs, with 165306 positive edges
12:12:11 Optimization finished
[1] "120 0.07"12:12:11 UMAP embedding parameters a = 1.68 b = 0.8631
12:12:11 Read 1203 rows and found 38 numeric columns
12:12:11 Using Annoy for neighbor search, n_neighbors = 120
12:12:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:12:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728d20bdd
12:12:11 Searching Annoy index using 1 thread, search_k = 12000
12:12:12 Annoy recall = 100%
12:12:20 Commencing smooth kNN distance calibration using 1 thread
12:12:37 Initializing from normalized Laplacian + noise
12:12:37 Commencing optimization for 500 epochs, with 165306 positive edges
12:12:49 Optimization finished
[1] "120 0.08"12:12:49 UMAP embedding parameters a = 1.645 b = 0.8737
12:12:49 Read 1203 rows and found 38 numeric columns
12:12:49 Using Annoy for neighbor search, n_neighbors = 120
12:12:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:12:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771fad65d
12:12:50 Searching Annoy index using 1 thread, search_k = 12000
12:12:51 Annoy recall = 100%
12:12:59 Commencing smooth kNN distance calibration using 1 thread
12:13:15 Initializing from normalized Laplacian + noise
12:13:15 Commencing optimization for 500 epochs, with 165306 positive edges
12:13:27 Optimization finished
[1] "120 0.09"12:13:28 UMAP embedding parameters a = 1.611 b = 0.8844
12:13:28 Read 1203 rows and found 38 numeric columns
12:13:28 Using Annoy for neighbor search, n_neighbors = 120
12:13:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:13:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770df7223
12:13:28 Searching Annoy index using 1 thread, search_k = 12000
12:13:29 Annoy recall = 100%
12:13:37 Commencing smooth kNN distance calibration using 1 thread
12:13:54 Initializing from normalized Laplacian + noise
12:13:54 Commencing optimization for 500 epochs, with 165306 positive edges
12:14:06 Optimization finished
[1] "120 0.1"12:14:06 UMAP embedding parameters a = 1.577 b = 0.8951
12:14:06 Read 1203 rows and found 38 numeric columns
12:14:06 Using Annoy for neighbor search, n_neighbors = 120
12:14:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:14:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fb23944
12:14:06 Searching Annoy index using 1 thread, search_k = 12000
12:14:07 Annoy recall = 100%
12:14:15 Commencing smooth kNN distance calibration using 1 thread
12:14:32 Initializing from normalized Laplacian + noise
12:14:32 Commencing optimization for 500 epochs, with 165306 positive edges
12:14:44 Optimization finished
[1] "120 0.11"12:14:44 UMAP embedding parameters a = 1.544 b = 0.9058
12:14:44 Read 1203 rows and found 38 numeric columns
12:14:44 Using Annoy for neighbor search, n_neighbors = 120
12:14:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:14:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87623a299e
12:14:45 Searching Annoy index using 1 thread, search_k = 12000
12:14:46 Annoy recall = 100%
12:14:54 Commencing smooth kNN distance calibration using 1 thread
12:15:10 Initializing from normalized Laplacian + noise
12:15:10 Commencing optimization for 500 epochs, with 165306 positive edges
12:15:22 Optimization finished
[1] "120 0.12"12:15:23 UMAP embedding parameters a = 1.51 b = 0.9165
12:15:23 Read 1203 rows and found 38 numeric columns
12:15:23 Using Annoy for neighbor search, n_neighbors = 120
12:15:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:15:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e149b94
12:15:23 Searching Annoy index using 1 thread, search_k = 12000
12:15:24 Annoy recall = 100%
12:15:32 Commencing smooth kNN distance calibration using 1 thread
12:15:49 Initializing from normalized Laplacian + noise
12:15:49 Commencing optimization for 500 epochs, with 165306 positive edges
12:16:01 Optimization finished
[1] "120 0.13"12:16:01 UMAP embedding parameters a = 1.478 b = 0.9272
12:16:01 Read 1203 rows and found 38 numeric columns
12:16:01 Using Annoy for neighbor search, n_neighbors = 120
12:16:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:16:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756887887
12:16:02 Searching Annoy index using 1 thread, search_k = 12000
12:16:03 Annoy recall = 100%
12:16:11 Commencing smooth kNN distance calibration using 1 thread
12:16:27 Initializing from normalized Laplacian + noise
12:16:27 Commencing optimization for 500 epochs, with 165306 positive edges
12:16:39 Optimization finished
[1] "120 0.14"12:16:39 UMAP embedding parameters a = 1.446 b = 0.938
12:16:39 Read 1203 rows and found 38 numeric columns
12:16:39 Using Annoy for neighbor search, n_neighbors = 120
12:16:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:16:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c8ed775
12:16:40 Searching Annoy index using 1 thread, search_k = 12000
12:16:41 Annoy recall = 100%
12:16:49 Commencing smooth kNN distance calibration using 1 thread
12:17:06 Initializing from normalized Laplacian + noise
12:17:06 Commencing optimization for 500 epochs, with 165306 positive edges
12:17:18 Optimization finished
[1] "120 0.15"12:17:18 UMAP embedding parameters a = 1.414 b = 0.9488
12:17:18 Read 1203 rows and found 38 numeric columns
12:17:18 Using Annoy for neighbor search, n_neighbors = 120
12:17:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:17:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730f4d2bb
12:17:18 Searching Annoy index using 1 thread, search_k = 12000
12:17:19 Annoy recall = 100%
12:17:27 Commencing smooth kNN distance calibration using 1 thread
12:17:44 Initializing from normalized Laplacian + noise
12:17:44 Commencing optimization for 500 epochs, with 165306 positive edges
12:17:56 Optimization finished
[1] "120 0.16"12:17:56 UMAP embedding parameters a = 1.383 b = 0.9596
12:17:56 Read 1203 rows and found 38 numeric columns
12:17:56 Using Annoy for neighbor search, n_neighbors = 120
12:17:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:17:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739db3ffb
12:17:57 Searching Annoy index using 1 thread, search_k = 12000
12:17:58 Annoy recall = 100%
12:18:06 Commencing smooth kNN distance calibration using 1 thread
12:18:22 Initializing from normalized Laplacian + noise
12:18:22 Commencing optimization for 500 epochs, with 165306 positive edges
12:18:35 Optimization finished
[1] "120 0.17"12:18:35 UMAP embedding parameters a = 1.352 b = 0.9704
12:18:35 Read 1203 rows and found 38 numeric columns
12:18:35 Using Annoy for neighbor search, n_neighbors = 120
12:18:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:18:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c8cb12c
12:18:35 Searching Annoy index using 1 thread, search_k = 12000
12:18:36 Annoy recall = 100%
12:18:44 Commencing smooth kNN distance calibration using 1 thread
12:19:01 Initializing from normalized Laplacian + noise
12:19:01 Commencing optimization for 500 epochs, with 165306 positive edges
12:19:13 Optimization finished
[1] "120 0.18"12:19:13 UMAP embedding parameters a = 1.321 b = 0.9813
12:19:13 Read 1203 rows and found 38 numeric columns
12:19:13 Using Annoy for neighbor search, n_neighbors = 120
12:19:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:19:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735385d8b
12:19:14 Searching Annoy index using 1 thread, search_k = 12000
12:19:15 Annoy recall = 100%
12:19:23 Commencing smooth kNN distance calibration using 1 thread
12:19:39 Initializing from normalized Laplacian + noise
12:19:39 Commencing optimization for 500 epochs, with 165306 positive edges
12:19:52 Optimization finished
[1] "120 0.19"12:19:52 UMAP embedding parameters a = 1.292 b = 0.9921
12:19:52 Read 1203 rows and found 38 numeric columns
12:19:52 Using Annoy for neighbor search, n_neighbors = 120
12:19:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:19:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777e7230d
12:19:52 Searching Annoy index using 1 thread, search_k = 12000
12:19:53 Annoy recall = 100%
12:20:01 Commencing smooth kNN distance calibration using 1 thread
12:20:18 Initializing from normalized Laplacian + noise
12:20:18 Commencing optimization for 500 epochs, with 165306 positive edges
12:20:30 Optimization finished
[1] "120 0.2"12:20:30 UMAP embedding parameters a = 1.262 b = 1.003
12:20:30 Read 1203 rows and found 38 numeric columns
12:20:30 Using Annoy for neighbor search, n_neighbors = 120
12:20:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:20:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b28116e
12:20:31 Searching Annoy index using 1 thread, search_k = 12000
12:20:32 Annoy recall = 100%
12:20:40 Commencing smooth kNN distance calibration using 1 thread
12:20:56 Initializing from normalized Laplacian + noise
12:20:56 Commencing optimization for 500 epochs, with 165306 positive edges
12:21:09 Optimization finished
[1] "121 0"12:21:09 UMAP embedding parameters a = 1.933 b = 0.7905
12:21:09 Read 1203 rows and found 38 numeric columns
12:21:09 Using Annoy for neighbor search, n_neighbors = 121
12:21:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:21:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87239b10a3
12:21:09 Searching Annoy index using 1 thread, search_k = 12100
12:21:10 Annoy recall = 100%
12:21:18 Commencing smooth kNN distance calibration using 1 thread
12:21:35 Initializing from normalized Laplacian + noise
12:21:35 Commencing optimization for 500 epochs, with 166570 positive edges
12:21:47 Optimization finished
[1] "121 0.01"12:21:47 UMAP embedding parameters a = 1.896 b = 0.8006
12:21:47 Read 1203 rows and found 38 numeric columns
12:21:47 Using Annoy for neighbor search, n_neighbors = 121
12:21:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:21:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744b3b4ce
12:21:48 Searching Annoy index using 1 thread, search_k = 12100
12:21:49 Annoy recall = 100%
12:21:57 Commencing smooth kNN distance calibration using 1 thread
12:22:13 Initializing from normalized Laplacian + noise
12:22:13 Commencing optimization for 500 epochs, with 166570 positive edges
12:22:26 Optimization finished
[1] "121 0.02"12:22:26 UMAP embedding parameters a = 1.859 b = 0.8109
12:22:26 Read 1203 rows and found 38 numeric columns
12:22:26 Using Annoy for neighbor search, n_neighbors = 121
12:22:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:22:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87cf2e99f
12:22:26 Searching Annoy index using 1 thread, search_k = 12100
12:22:27 Annoy recall = 100%
12:22:35 Commencing smooth kNN distance calibration using 1 thread
12:22:52 Initializing from normalized Laplacian + noise
12:22:52 Commencing optimization for 500 epochs, with 166570 positive edges
12:23:04 Optimization finished
[1] "121 0.03"12:23:04 UMAP embedding parameters a = 1.822 b = 0.8212
12:23:04 Read 1203 rows and found 38 numeric columns
12:23:04 Using Annoy for neighbor search, n_neighbors = 121
12:23:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:23:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f5db7f2
12:23:05 Searching Annoy index using 1 thread, search_k = 12100
12:23:06 Annoy recall = 100%
12:23:14 Commencing smooth kNN distance calibration using 1 thread
12:23:30 Initializing from normalized Laplacian + noise
12:23:30 Commencing optimization for 500 epochs, with 166570 positive edges
12:23:43 Optimization finished
[1] "121 0.04"12:23:43 UMAP embedding parameters a = 1.786 b = 0.8316
12:23:43 Read 1203 rows and found 38 numeric columns
12:23:43 Using Annoy for neighbor search, n_neighbors = 121
12:23:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:23:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873420cf5d
12:23:43 Searching Annoy index using 1 thread, search_k = 12100
12:23:44 Annoy recall = 100%
12:23:52 Commencing smooth kNN distance calibration using 1 thread
12:24:09 Initializing from normalized Laplacian + noise
12:24:09 Commencing optimization for 500 epochs, with 166570 positive edges
12:24:21 Optimization finished
[1] "121 0.05"12:24:22 UMAP embedding parameters a = 1.75 b = 0.8421
12:24:22 Read 1203 rows and found 38 numeric columns
12:24:22 Using Annoy for neighbor search, n_neighbors = 121
12:24:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:24:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b849506
12:24:22 Searching Annoy index using 1 thread, search_k = 12100
12:24:23 Annoy recall = 100%
12:24:31 Commencing smooth kNN distance calibration using 1 thread
12:24:47 Initializing from normalized Laplacian + noise
12:24:48 Commencing optimization for 500 epochs, with 166570 positive edges
12:25:00 Optimization finished
[1] "121 0.06"12:25:00 UMAP embedding parameters a = 1.715 b = 0.8526
12:25:00 Read 1203 rows and found 38 numeric columns
12:25:00 Using Annoy for neighbor search, n_neighbors = 121
12:25:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:25:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87473c4528
12:25:01 Searching Annoy index using 1 thread, search_k = 12100
12:25:01 Annoy recall = 100%
12:25:10 Commencing smooth kNN distance calibration using 1 thread
12:25:26 Initializing from normalized Laplacian + noise
12:25:26 Commencing optimization for 500 epochs, with 166570 positive edges
12:25:38 Optimization finished
[1] "121 0.07"12:25:39 UMAP embedding parameters a = 1.68 b = 0.8631
12:25:39 Read 1203 rows and found 38 numeric columns
12:25:39 Using Annoy for neighbor search, n_neighbors = 121
12:25:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:25:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750bb78b9
12:25:39 Searching Annoy index using 1 thread, search_k = 12100
12:25:40 Annoy recall = 100%
12:25:48 Commencing smooth kNN distance calibration using 1 thread
12:26:05 Initializing from normalized Laplacian + noise
12:26:05 Commencing optimization for 500 epochs, with 166570 positive edges
12:26:17 Optimization finished
[1] "121 0.08"12:26:17 UMAP embedding parameters a = 1.645 b = 0.8737
12:26:17 Read 1203 rows and found 38 numeric columns
12:26:17 Using Annoy for neighbor search, n_neighbors = 121
12:26:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:26:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871066843
12:26:18 Searching Annoy index using 1 thread, search_k = 12100
12:26:19 Annoy recall = 100%
12:26:27 Commencing smooth kNN distance calibration using 1 thread
12:26:43 Initializing from normalized Laplacian + noise
12:26:43 Commencing optimization for 500 epochs, with 166570 positive edges
12:26:56 Optimization finished
[1] "121 0.09"12:26:56 UMAP embedding parameters a = 1.611 b = 0.8844
12:26:56 Read 1203 rows and found 38 numeric columns
12:26:56 Using Annoy for neighbor search, n_neighbors = 121
12:26:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:26:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874977cee3
12:26:56 Searching Annoy index using 1 thread, search_k = 12100
12:26:57 Annoy recall = 100%
12:27:05 Commencing smooth kNN distance calibration using 1 thread
12:27:22 Initializing from normalized Laplacian + noise
12:27:22 Commencing optimization for 500 epochs, with 166570 positive edges
12:27:34 Optimization finished
[1] "121 0.1"12:27:35 UMAP embedding parameters a = 1.577 b = 0.8951
12:27:35 Read 1203 rows and found 38 numeric columns
12:27:35 Using Annoy for neighbor search, n_neighbors = 121
12:27:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:27:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873dee4d25
12:27:35 Searching Annoy index using 1 thread, search_k = 12100
12:27:36 Annoy recall = 100%
12:27:44 Commencing smooth kNN distance calibration using 1 thread
12:28:01 Initializing from normalized Laplacian + noise
12:28:01 Commencing optimization for 500 epochs, with 166570 positive edges
12:28:13 Optimization finished
[1] "121 0.11"12:28:13 UMAP embedding parameters a = 1.544 b = 0.9058
12:28:13 Read 1203 rows and found 38 numeric columns
12:28:13 Using Annoy for neighbor search, n_neighbors = 121
12:28:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:28:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b7cd3fe
12:28:14 Searching Annoy index using 1 thread, search_k = 12100
12:28:15 Annoy recall = 100%
12:28:23 Commencing smooth kNN distance calibration using 1 thread
12:28:39 Initializing from normalized Laplacian + noise
12:28:40 Commencing optimization for 500 epochs, with 166570 positive edges
12:28:52 Optimization finished
[1] "121 0.12"12:28:52 UMAP embedding parameters a = 1.51 b = 0.9165
12:28:52 Read 1203 rows and found 38 numeric columns
12:28:52 Using Annoy for neighbor search, n_neighbors = 121
12:28:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:28:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b98887e
12:28:52 Searching Annoy index using 1 thread, search_k = 12100
12:28:53 Annoy recall = 100%
12:29:01 Commencing smooth kNN distance calibration using 1 thread
12:29:18 Initializing from normalized Laplacian + noise
12:29:18 Commencing optimization for 500 epochs, with 166570 positive edges
12:29:30 Optimization finished
[1] "121 0.13"12:29:31 UMAP embedding parameters a = 1.478 b = 0.9272
12:29:31 Read 1203 rows and found 38 numeric columns
12:29:31 Using Annoy for neighbor search, n_neighbors = 121
12:29:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:29:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754c26a54
12:29:31 Searching Annoy index using 1 thread, search_k = 12100
12:29:32 Annoy recall = 100%
12:29:40 Commencing smooth kNN distance calibration using 1 thread
12:29:57 Initializing from normalized Laplacian + noise
12:29:57 Commencing optimization for 500 epochs, with 166570 positive edges
12:30:09 Optimization finished
[1] "121 0.14"12:30:09 UMAP embedding parameters a = 1.446 b = 0.938
12:30:09 Read 1203 rows and found 38 numeric columns
12:30:09 Using Annoy for neighbor search, n_neighbors = 121
12:30:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:30:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875306c41a
12:30:10 Searching Annoy index using 1 thread, search_k = 12100
12:30:11 Annoy recall = 100%
12:30:19 Commencing smooth kNN distance calibration using 1 thread
12:30:35 Initializing from normalized Laplacian + noise
12:30:36 Commencing optimization for 500 epochs, with 166570 positive edges
12:30:48 Optimization finished
[1] "121 0.15"12:30:48 UMAP embedding parameters a = 1.414 b = 0.9488
12:30:48 Read 1203 rows and found 38 numeric columns
12:30:48 Using Annoy for neighbor search, n_neighbors = 121
12:30:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:30:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87358ce41a
12:30:49 Searching Annoy index using 1 thread, search_k = 12100
12:30:49 Annoy recall = 100%
12:30:58 Commencing smooth kNN distance calibration using 1 thread
12:31:14 Initializing from normalized Laplacian + noise
12:31:14 Commencing optimization for 500 epochs, with 166570 positive edges
12:31:26 Optimization finished
[1] "121 0.16"12:31:27 UMAP embedding parameters a = 1.383 b = 0.9596
12:31:27 Read 1203 rows and found 38 numeric columns
12:31:27 Using Annoy for neighbor search, n_neighbors = 121
12:31:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:31:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87163695dc
12:31:27 Searching Annoy index using 1 thread, search_k = 12100
12:31:28 Annoy recall = 100%
12:31:36 Commencing smooth kNN distance calibration using 1 thread
12:31:53 Initializing from normalized Laplacian + noise
12:31:53 Commencing optimization for 500 epochs, with 166570 positive edges
12:32:05 Optimization finished
[1] "121 0.17"12:32:05 UMAP embedding parameters a = 1.352 b = 0.9704
12:32:05 Read 1203 rows and found 38 numeric columns
12:32:05 Using Annoy for neighbor search, n_neighbors = 121
12:32:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:32:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bd8cff7
12:32:06 Searching Annoy index using 1 thread, search_k = 12100
12:32:07 Annoy recall = 100%
12:32:15 Commencing smooth kNN distance calibration using 1 thread
12:32:32 Initializing from normalized Laplacian + noise
12:32:32 Commencing optimization for 500 epochs, with 166570 positive edges
12:32:44 Optimization finished
[1] "121 0.18"12:32:44 UMAP embedding parameters a = 1.321 b = 0.9813
12:32:44 Read 1203 rows and found 38 numeric columns
12:32:44 Using Annoy for neighbor search, n_neighbors = 121
12:32:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:32:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872787ba78
12:32:45 Searching Annoy index using 1 thread, search_k = 12100
12:32:46 Annoy recall = 100%
12:32:54 Commencing smooth kNN distance calibration using 1 thread
12:33:10 Initializing from normalized Laplacian + noise
12:33:10 Commencing optimization for 500 epochs, with 166570 positive edges
12:33:23 Optimization finished
[1] "121 0.19"12:33:23 UMAP embedding parameters a = 1.292 b = 0.9921
12:33:23 Read 1203 rows and found 38 numeric columns
12:33:23 Using Annoy for neighbor search, n_neighbors = 121
12:33:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:33:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771607ff
12:33:23 Searching Annoy index using 1 thread, search_k = 12100
12:33:24 Annoy recall = 100%
12:33:32 Commencing smooth kNN distance calibration using 1 thread
12:33:49 Initializing from normalized Laplacian + noise
12:33:49 Commencing optimization for 500 epochs, with 166570 positive edges
12:34:01 Optimization finished
[1] "121 0.2"12:34:02 UMAP embedding parameters a = 1.262 b = 1.003
12:34:02 Read 1203 rows and found 38 numeric columns
12:34:02 Using Annoy for neighbor search, n_neighbors = 121
12:34:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:34:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b8b093b
12:34:02 Searching Annoy index using 1 thread, search_k = 12100
12:34:03 Annoy recall = 100%
12:34:11 Commencing smooth kNN distance calibration using 1 thread
12:34:28 Initializing from normalized Laplacian + noise
12:34:28 Commencing optimization for 500 epochs, with 166570 positive edges
12:34:40 Optimization finished
[1] "122 0"12:34:40 UMAP embedding parameters a = 1.933 b = 0.7905
12:34:40 Read 1203 rows and found 38 numeric columns
12:34:40 Using Annoy for neighbor search, n_neighbors = 122
12:34:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:34:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879c1e416
12:34:41 Searching Annoy index using 1 thread, search_k = 12200
12:34:42 Annoy recall = 100%
12:34:50 Commencing smooth kNN distance calibration using 1 thread
12:35:07 Initializing from normalized Laplacian + noise
12:35:07 Commencing optimization for 500 epochs, with 167790 positive edges
12:35:19 Optimization finished
[1] "122 0.01"12:35:19 UMAP embedding parameters a = 1.896 b = 0.8006
12:35:19 Read 1203 rows and found 38 numeric columns
12:35:19 Using Annoy for neighbor search, n_neighbors = 122
12:35:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:35:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87452aa393
12:35:20 Searching Annoy index using 1 thread, search_k = 12200
12:35:21 Annoy recall = 100%
12:35:29 Commencing smooth kNN distance calibration using 1 thread
12:35:45 Initializing from normalized Laplacian + noise
12:35:46 Commencing optimization for 500 epochs, with 167790 positive edges
12:35:58 Optimization finished
[1] "122 0.02"12:35:58 UMAP embedding parameters a = 1.859 b = 0.8109
12:35:58 Read 1203 rows and found 38 numeric columns
12:35:58 Using Annoy for neighbor search, n_neighbors = 122
12:35:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:35:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87621381c2
12:35:59 Searching Annoy index using 1 thread, search_k = 12200
12:36:00 Annoy recall = 100%
12:36:08 Commencing smooth kNN distance calibration using 1 thread
12:36:24 Initializing from normalized Laplacian + noise
12:36:24 Commencing optimization for 500 epochs, with 167790 positive edges
12:36:37 Optimization finished
[1] "122 0.03"12:36:37 UMAP embedding parameters a = 1.822 b = 0.8212
12:36:37 Read 1203 rows and found 38 numeric columns
12:36:37 Using Annoy for neighbor search, n_neighbors = 122
12:36:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:36:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872650bb8b
12:36:37 Searching Annoy index using 1 thread, search_k = 12200
12:36:38 Annoy recall = 100%
12:36:46 Commencing smooth kNN distance calibration using 1 thread
12:37:03 Initializing from normalized Laplacian + noise
12:37:03 Commencing optimization for 500 epochs, with 167790 positive edges
12:37:15 Optimization finished
[1] "122 0.04"12:37:16 UMAP embedding parameters a = 1.786 b = 0.8316
12:37:16 Read 1203 rows and found 38 numeric columns
12:37:16 Using Annoy for neighbor search, n_neighbors = 122
12:37:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:37:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87761f764f
12:37:16 Searching Annoy index using 1 thread, search_k = 12200
12:37:17 Annoy recall = 100%
12:37:25 Commencing smooth kNN distance calibration using 1 thread
12:37:42 Initializing from normalized Laplacian + noise
12:37:42 Commencing optimization for 500 epochs, with 167790 positive edges
12:37:54 Optimization finished
[1] "122 0.05"12:37:55 UMAP embedding parameters a = 1.75 b = 0.8421
12:37:55 Read 1203 rows and found 38 numeric columns
12:37:55 Using Annoy for neighbor search, n_neighbors = 122
12:37:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:37:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871beec1bd
12:37:55 Searching Annoy index using 1 thread, search_k = 12200
12:37:56 Annoy recall = 100%
12:38:04 Commencing smooth kNN distance calibration using 1 thread
12:38:21 Initializing from normalized Laplacian + noise
12:38:21 Commencing optimization for 500 epochs, with 167790 positive edges
12:38:33 Optimization finished
[1] "122 0.06"12:38:33 UMAP embedding parameters a = 1.715 b = 0.8526
12:38:33 Read 1203 rows and found 38 numeric columns
12:38:33 Using Annoy for neighbor search, n_neighbors = 122
12:38:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:38:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dd6cb7
12:38:34 Searching Annoy index using 1 thread, search_k = 12200
12:38:35 Annoy recall = 100%
12:38:43 Commencing smooth kNN distance calibration using 1 thread
12:39:00 Initializing from normalized Laplacian + noise
12:39:00 Commencing optimization for 500 epochs, with 167790 positive edges
12:39:12 Optimization finished
[1] "122 0.07"12:39:13 UMAP embedding parameters a = 1.68 b = 0.8631
12:39:13 Read 1203 rows and found 38 numeric columns
12:39:13 Using Annoy for neighbor search, n_neighbors = 122
12:39:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:39:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b57d3da
12:39:13 Searching Annoy index using 1 thread, search_k = 12200
12:39:14 Annoy recall = 100%
12:39:22 Commencing smooth kNN distance calibration using 1 thread
12:39:39 Initializing from normalized Laplacian + noise
12:39:39 Commencing optimization for 500 epochs, with 167790 positive edges
12:39:51 Optimization finished
[1] "122 0.08"12:39:51 UMAP embedding parameters a = 1.645 b = 0.8737
12:39:51 Read 1203 rows and found 38 numeric columns
12:39:51 Using Annoy for neighbor search, n_neighbors = 122
12:39:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:39:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713d5e4cb
12:39:52 Searching Annoy index using 1 thread, search_k = 12200
12:39:53 Annoy recall = 100%
12:40:01 Commencing smooth kNN distance calibration using 1 thread
12:40:18 Initializing from normalized Laplacian + noise
12:40:18 Commencing optimization for 500 epochs, with 167790 positive edges
12:40:30 Optimization finished
[1] "122 0.09"12:40:30 UMAP embedding parameters a = 1.611 b = 0.8844
12:40:30 Read 1203 rows and found 38 numeric columns
12:40:30 Using Annoy for neighbor search, n_neighbors = 122
12:40:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:40:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e057e26
12:40:31 Searching Annoy index using 1 thread, search_k = 12200
12:40:32 Annoy recall = 100%
12:40:40 Commencing smooth kNN distance calibration using 1 thread
12:40:57 Initializing from normalized Laplacian + noise
12:40:57 Commencing optimization for 500 epochs, with 167790 positive edges
12:41:09 Optimization finished
[1] "122 0.1"12:41:09 UMAP embedding parameters a = 1.577 b = 0.8951
12:41:09 Read 1203 rows and found 38 numeric columns
12:41:09 Using Annoy for neighbor search, n_neighbors = 122
12:41:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:41:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ef2e47d
12:41:10 Searching Annoy index using 1 thread, search_k = 12200
12:41:11 Annoy recall = 100%
12:41:19 Commencing smooth kNN distance calibration using 1 thread
12:41:36 Initializing from normalized Laplacian + noise
12:41:36 Commencing optimization for 500 epochs, with 167790 positive edges
12:41:48 Optimization finished
[1] "122 0.11"12:41:48 UMAP embedding parameters a = 1.544 b = 0.9058
12:41:48 Read 1203 rows and found 38 numeric columns
12:41:48 Using Annoy for neighbor search, n_neighbors = 122
12:41:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:41:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758899999
12:41:49 Searching Annoy index using 1 thread, search_k = 12200
12:41:50 Annoy recall = 100%
12:41:58 Commencing smooth kNN distance calibration using 1 thread
12:42:15 Initializing from normalized Laplacian + noise
12:42:15 Commencing optimization for 500 epochs, with 167790 positive edges
12:42:27 Optimization finished
[1] "122 0.12"12:42:27 UMAP embedding parameters a = 1.51 b = 0.9165
12:42:27 Read 1203 rows and found 38 numeric columns
12:42:27 Using Annoy for neighbor search, n_neighbors = 122
12:42:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:42:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871af867c5
12:42:28 Searching Annoy index using 1 thread, search_k = 12200
12:42:29 Annoy recall = 100%
12:42:37 Commencing smooth kNN distance calibration using 1 thread
12:42:54 Initializing from normalized Laplacian + noise
12:42:54 Commencing optimization for 500 epochs, with 167790 positive edges
12:43:06 Optimization finished
[1] "122 0.13"12:43:06 UMAP embedding parameters a = 1.478 b = 0.9272
12:43:06 Read 1203 rows and found 38 numeric columns
12:43:06 Using Annoy for neighbor search, n_neighbors = 122
12:43:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:43:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e509c70
12:43:07 Searching Annoy index using 1 thread, search_k = 12200
12:43:08 Annoy recall = 100%
12:43:16 Commencing smooth kNN distance calibration using 1 thread
12:43:33 Initializing from normalized Laplacian + noise
12:43:33 Commencing optimization for 500 epochs, with 167790 positive edges
12:43:45 Optimization finished
[1] "122 0.14"12:43:45 UMAP embedding parameters a = 1.446 b = 0.938
12:43:45 Read 1203 rows and found 38 numeric columns
12:43:45 Using Annoy for neighbor search, n_neighbors = 122
12:43:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:43:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87caa68f6
12:43:46 Searching Annoy index using 1 thread, search_k = 12200
12:43:47 Annoy recall = 100%
12:43:55 Commencing smooth kNN distance calibration using 1 thread
12:44:12 Initializing from normalized Laplacian + noise
12:44:12 Commencing optimization for 500 epochs, with 167790 positive edges
12:44:24 Optimization finished
[1] "122 0.15"12:44:24 UMAP embedding parameters a = 1.414 b = 0.9488
12:44:24 Read 1203 rows and found 38 numeric columns
12:44:24 Using Annoy for neighbor search, n_neighbors = 122
12:44:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:44:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87367cfccc
12:44:25 Searching Annoy index using 1 thread, search_k = 12200
12:44:26 Annoy recall = 100%
12:44:34 Commencing smooth kNN distance calibration using 1 thread
12:44:51 Initializing from normalized Laplacian + noise
12:44:51 Commencing optimization for 500 epochs, with 167790 positive edges
12:45:03 Optimization finished
[1] "122 0.16"12:45:03 UMAP embedding parameters a = 1.383 b = 0.9596
12:45:03 Read 1203 rows and found 38 numeric columns
12:45:03 Using Annoy for neighbor search, n_neighbors = 122
12:45:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:45:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87758ce198
12:45:04 Searching Annoy index using 1 thread, search_k = 12200
12:45:05 Annoy recall = 100%
12:45:13 Commencing smooth kNN distance calibration using 1 thread
12:45:30 Initializing from normalized Laplacian + noise
12:45:30 Commencing optimization for 500 epochs, with 167790 positive edges
12:45:42 Optimization finished
[1] "122 0.17"12:45:42 UMAP embedding parameters a = 1.352 b = 0.9704
12:45:42 Read 1203 rows and found 38 numeric columns
12:45:42 Using Annoy for neighbor search, n_neighbors = 122
12:45:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:45:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d65e1af
12:45:43 Searching Annoy index using 1 thread, search_k = 12200
12:45:44 Annoy recall = 100%
12:45:52 Commencing smooth kNN distance calibration using 1 thread
12:46:09 Initializing from normalized Laplacian + noise
12:46:09 Commencing optimization for 500 epochs, with 167790 positive edges
12:46:21 Optimization finished
[1] "122 0.18"12:46:21 UMAP embedding parameters a = 1.321 b = 0.9813
12:46:21 Read 1203 rows and found 38 numeric columns
12:46:22 Using Annoy for neighbor search, n_neighbors = 122
12:46:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:46:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873783650f
12:46:22 Searching Annoy index using 1 thread, search_k = 12200
12:46:23 Annoy recall = 100%
12:46:31 Commencing smooth kNN distance calibration using 1 thread
12:46:48 Initializing from normalized Laplacian + noise
12:46:48 Commencing optimization for 500 epochs, with 167790 positive edges
12:47:00 Optimization finished
[1] "122 0.19"12:47:00 UMAP embedding parameters a = 1.292 b = 0.9921
12:47:00 Read 1203 rows and found 38 numeric columns
12:47:01 Using Annoy for neighbor search, n_neighbors = 122
12:47:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:47:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f04b07b
12:47:01 Searching Annoy index using 1 thread, search_k = 12200
12:47:02 Annoy recall = 100%
12:47:10 Commencing smooth kNN distance calibration using 1 thread
12:47:27 Initializing from normalized Laplacian + noise
12:47:27 Commencing optimization for 500 epochs, with 167790 positive edges
12:47:40 Optimization finished
[1] "122 0.2"12:47:40 UMAP embedding parameters a = 1.262 b = 1.003
12:47:40 Read 1203 rows and found 38 numeric columns
12:47:40 Using Annoy for neighbor search, n_neighbors = 122
12:47:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:47:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b542ed5
12:47:40 Searching Annoy index using 1 thread, search_k = 12200
12:47:41 Annoy recall = 100%
12:47:49 Commencing smooth kNN distance calibration using 1 thread
12:48:06 Initializing from normalized Laplacian + noise
12:48:06 Commencing optimization for 500 epochs, with 167790 positive edges
12:48:18 Optimization finished
[1] "123 0"12:48:19 UMAP embedding parameters a = 1.933 b = 0.7905
12:48:19 Read 1203 rows and found 38 numeric columns
12:48:19 Using Annoy for neighbor search, n_neighbors = 123
12:48:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:48:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873300390d
12:48:19 Searching Annoy index using 1 thread, search_k = 12300
12:48:20 Annoy recall = 100%
12:48:29 Commencing smooth kNN distance calibration using 1 thread
12:48:45 Initializing from normalized Laplacian + noise
12:48:45 Commencing optimization for 500 epochs, with 169024 positive edges
12:48:58 Optimization finished
[1] "123 0.01"12:48:58 UMAP embedding parameters a = 1.896 b = 0.8006
12:48:58 Read 1203 rows and found 38 numeric columns
12:48:58 Using Annoy for neighbor search, n_neighbors = 123
12:48:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:48:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a9d38fa
12:48:59 Searching Annoy index using 1 thread, search_k = 12300
12:48:59 Annoy recall = 100%
12:49:08 Commencing smooth kNN distance calibration using 1 thread
12:49:24 Initializing from normalized Laplacian + noise
12:49:25 Commencing optimization for 500 epochs, with 169024 positive edges
12:49:37 Optimization finished
[1] "123 0.02"12:49:37 UMAP embedding parameters a = 1.859 b = 0.8109
12:49:37 Read 1203 rows and found 38 numeric columns
12:49:37 Using Annoy for neighbor search, n_neighbors = 123
12:49:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:49:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770169929
12:49:38 Searching Annoy index using 1 thread, search_k = 12300
12:49:39 Annoy recall = 100%
12:49:47 Commencing smooth kNN distance calibration using 1 thread
12:50:04 Initializing from normalized Laplacian + noise
12:50:04 Commencing optimization for 500 epochs, with 169024 positive edges
12:50:16 Optimization finished
[1] "123 0.03"12:50:16 UMAP embedding parameters a = 1.822 b = 0.8212
12:50:16 Read 1203 rows and found 38 numeric columns
12:50:16 Using Annoy for neighbor search, n_neighbors = 123
12:50:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:50:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87606fd27
12:50:17 Searching Annoy index using 1 thread, search_k = 12300
12:50:18 Annoy recall = 100%
12:50:26 Commencing smooth kNN distance calibration using 1 thread
12:50:43 Initializing from normalized Laplacian + noise
12:50:43 Commencing optimization for 500 epochs, with 169024 positive edges
12:50:55 Optimization finished
[1] "123 0.04"12:50:56 UMAP embedding parameters a = 1.786 b = 0.8316
12:50:56 Read 1203 rows and found 38 numeric columns
12:50:56 Using Annoy for neighbor search, n_neighbors = 123
12:50:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:50:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87102a1d14
12:50:56 Searching Annoy index using 1 thread, search_k = 12300
12:50:57 Annoy recall = 100%
12:51:05 Commencing smooth kNN distance calibration using 1 thread
12:51:22 Initializing from normalized Laplacian + noise
12:51:22 Commencing optimization for 500 epochs, with 169024 positive edges
12:51:35 Optimization finished
[1] "123 0.05"12:51:35 UMAP embedding parameters a = 1.75 b = 0.8421
12:51:35 Read 1203 rows and found 38 numeric columns
12:51:35 Using Annoy for neighbor search, n_neighbors = 123
12:51:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:51:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764d2f05
12:51:35 Searching Annoy index using 1 thread, search_k = 12300
12:51:36 Annoy recall = 100%
12:51:45 Commencing smooth kNN distance calibration using 1 thread
12:52:02 Initializing from normalized Laplacian + noise
12:52:02 Commencing optimization for 500 epochs, with 169024 positive edges
12:52:14 Optimization finished
[1] "123 0.06"12:52:14 UMAP embedding parameters a = 1.715 b = 0.8526
12:52:14 Read 1203 rows and found 38 numeric columns
12:52:14 Using Annoy for neighbor search, n_neighbors = 123
12:52:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:52:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871dfcd1e
12:52:15 Searching Annoy index using 1 thread, search_k = 12300
12:52:15 Annoy recall = 100%
12:52:24 Commencing smooth kNN distance calibration using 1 thread
12:52:41 Initializing from normalized Laplacian + noise
12:52:41 Commencing optimization for 500 epochs, with 169024 positive edges
12:52:53 Optimization finished
[1] "123 0.07"12:52:53 UMAP embedding parameters a = 1.68 b = 0.8631
12:52:53 Read 1203 rows and found 38 numeric columns
12:52:53 Using Annoy for neighbor search, n_neighbors = 123
12:52:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:52:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737b1d78c
12:52:54 Searching Annoy index using 1 thread, search_k = 12300
12:52:55 Annoy recall = 100%
12:53:03 Commencing smooth kNN distance calibration using 1 thread
12:53:20 Initializing from normalized Laplacian + noise
12:53:20 Commencing optimization for 500 epochs, with 169024 positive edges
12:53:32 Optimization finished
[1] "123 0.08"12:53:32 UMAP embedding parameters a = 1.645 b = 0.8737
12:53:32 Read 1203 rows and found 38 numeric columns
12:53:32 Using Annoy for neighbor search, n_neighbors = 123
12:53:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:53:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d633704
12:53:33 Searching Annoy index using 1 thread, search_k = 12300
12:53:34 Annoy recall = 100%
12:53:42 Commencing smooth kNN distance calibration using 1 thread
12:53:59 Initializing from normalized Laplacian + noise
12:53:59 Commencing optimization for 500 epochs, with 169024 positive edges
12:54:11 Optimization finished
[1] "123 0.09"12:54:12 UMAP embedding parameters a = 1.611 b = 0.8844
12:54:12 Read 1203 rows and found 38 numeric columns
12:54:12 Using Annoy for neighbor search, n_neighbors = 123
12:54:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:54:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d6ad659
12:54:12 Searching Annoy index using 1 thread, search_k = 12300
12:54:13 Annoy recall = 100%
12:54:21 Commencing smooth kNN distance calibration using 1 thread
12:54:38 Initializing from normalized Laplacian + noise
12:54:38 Commencing optimization for 500 epochs, with 169024 positive edges
12:54:51 Optimization finished
[1] "123 0.1"12:54:51 UMAP embedding parameters a = 1.577 b = 0.8951
12:54:51 Read 1203 rows and found 38 numeric columns
12:54:51 Using Annoy for neighbor search, n_neighbors = 123
12:54:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:54:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874173bba2
12:54:51 Searching Annoy index using 1 thread, search_k = 12300
12:54:52 Annoy recall = 100%
12:55:01 Commencing smooth kNN distance calibration using 1 thread
12:55:17 Initializing from normalized Laplacian + noise
12:55:17 Commencing optimization for 500 epochs, with 169024 positive edges
12:55:30 Optimization finished
[1] "123 0.11"12:55:30 UMAP embedding parameters a = 1.544 b = 0.9058
12:55:30 Read 1203 rows and found 38 numeric columns
12:55:30 Using Annoy for neighbor search, n_neighbors = 123
12:55:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:55:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87528dda97
12:55:31 Searching Annoy index using 1 thread, search_k = 12300
12:55:31 Annoy recall = 100%
12:55:40 Commencing smooth kNN distance calibration using 1 thread
12:55:57 Initializing from normalized Laplacian + noise
12:55:57 Commencing optimization for 500 epochs, with 169024 positive edges
12:56:09 Optimization finished
[1] "123 0.12"12:56:09 UMAP embedding parameters a = 1.51 b = 0.9165
12:56:09 Read 1203 rows and found 38 numeric columns
12:56:09 Using Annoy for neighbor search, n_neighbors = 123
12:56:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:56:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f7e581b
12:56:10 Searching Annoy index using 1 thread, search_k = 12300
12:56:11 Annoy recall = 100%
12:56:19 Commencing smooth kNN distance calibration using 1 thread
12:56:36 Initializing from normalized Laplacian + noise
12:56:36 Commencing optimization for 500 epochs, with 169024 positive edges
12:56:48 Optimization finished
[1] "123 0.13"12:56:48 UMAP embedding parameters a = 1.478 b = 0.9272
12:56:49 Read 1203 rows and found 38 numeric columns
12:56:49 Using Annoy for neighbor search, n_neighbors = 123
12:56:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:56:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767c4772e
12:56:49 Searching Annoy index using 1 thread, search_k = 12300
12:56:50 Annoy recall = 100%
12:56:58 Commencing smooth kNN distance calibration using 1 thread
12:57:15 Initializing from normalized Laplacian + noise
12:57:15 Commencing optimization for 500 epochs, with 169024 positive edges
12:57:28 Optimization finished
[1] "123 0.14"12:57:28 UMAP embedding parameters a = 1.446 b = 0.938
12:57:28 Read 1203 rows and found 38 numeric columns
12:57:28 Using Annoy for neighbor search, n_neighbors = 123
12:57:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:57:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748ad50e6
12:57:28 Searching Annoy index using 1 thread, search_k = 12300
12:57:29 Annoy recall = 100%
12:57:38 Commencing smooth kNN distance calibration using 1 thread
12:57:54 Initializing from normalized Laplacian + noise
12:57:54 Commencing optimization for 500 epochs, with 169024 positive edges
12:58:07 Optimization finished
[1] "123 0.15"12:58:07 UMAP embedding parameters a = 1.414 b = 0.9488
12:58:07 Read 1203 rows and found 38 numeric columns
12:58:07 Using Annoy for neighbor search, n_neighbors = 123
12:58:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:58:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b6d19d9
12:58:08 Searching Annoy index using 1 thread, search_k = 12300
12:58:09 Annoy recall = 100%
12:58:17 Commencing smooth kNN distance calibration using 1 thread
12:58:34 Initializing from normalized Laplacian + noise
12:58:34 Commencing optimization for 500 epochs, with 169024 positive edges
12:58:46 Optimization finished
[1] "123 0.16"12:58:46 UMAP embedding parameters a = 1.383 b = 0.9596
12:58:46 Read 1203 rows and found 38 numeric columns
12:58:46 Using Annoy for neighbor search, n_neighbors = 123
12:58:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:58:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aa1e3e5
12:58:47 Searching Annoy index using 1 thread, search_k = 12300
12:58:48 Annoy recall = 100%
12:58:56 Commencing smooth kNN distance calibration using 1 thread
12:59:13 Initializing from normalized Laplacian + noise
12:59:13 Commencing optimization for 500 epochs, with 169024 positive edges
12:59:26 Optimization finished
[1] "123 0.17"12:59:26 UMAP embedding parameters a = 1.352 b = 0.9704
12:59:26 Read 1203 rows and found 38 numeric columns
12:59:26 Using Annoy for neighbor search, n_neighbors = 123
12:59:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:59:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87740524c1
12:59:26 Searching Annoy index using 1 thread, search_k = 12300
12:59:27 Annoy recall = 100%
12:59:35 Commencing smooth kNN distance calibration using 1 thread
12:59:52 Initializing from normalized Laplacian + noise
12:59:52 Commencing optimization for 500 epochs, with 169024 positive edges
13:00:05 Optimization finished
[1] "123 0.18"13:00:05 UMAP embedding parameters a = 1.321 b = 0.9813
13:00:05 Read 1203 rows and found 38 numeric columns
13:00:05 Using Annoy for neighbor search, n_neighbors = 123
13:00:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:00:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f42fea4
13:00:06 Searching Annoy index using 1 thread, search_k = 12300
13:00:06 Annoy recall = 100%
13:00:15 Commencing smooth kNN distance calibration using 1 thread
13:00:32 Initializing from normalized Laplacian + noise
13:00:32 Commencing optimization for 500 epochs, with 169024 positive edges
13:00:44 Optimization finished
[1] "123 0.19"13:00:44 UMAP embedding parameters a = 1.292 b = 0.9921
13:00:44 Read 1203 rows and found 38 numeric columns
13:00:44 Using Annoy for neighbor search, n_neighbors = 123
13:00:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:00:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778a7620b
13:00:45 Searching Annoy index using 1 thread, search_k = 12300
13:00:46 Annoy recall = 100%
13:00:54 Commencing smooth kNN distance calibration using 1 thread
13:01:11 Initializing from normalized Laplacian + noise
13:01:11 Commencing optimization for 500 epochs, with 169024 positive edges
13:01:23 Optimization finished
[1] "123 0.2"13:01:24 UMAP embedding parameters a = 1.262 b = 1.003
13:01:24 Read 1203 rows and found 38 numeric columns
13:01:24 Using Annoy for neighbor search, n_neighbors = 123
13:01:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:01:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742f8093e
13:01:24 Searching Annoy index using 1 thread, search_k = 12300
13:01:25 Annoy recall = 100%
13:01:34 Commencing smooth kNN distance calibration using 1 thread
13:01:50 Initializing from normalized Laplacian + noise
13:01:50 Commencing optimization for 500 epochs, with 169024 positive edges
13:02:03 Optimization finished
[1] "124 0"13:02:03 UMAP embedding parameters a = 1.933 b = 0.7905
13:02:03 Read 1203 rows and found 38 numeric columns
13:02:03 Using Annoy for neighbor search, n_neighbors = 124
13:02:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:02:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777cc983d
13:02:03 Searching Annoy index using 1 thread, search_k = 12400
13:02:04 Annoy recall = 100%
13:02:13 Commencing smooth kNN distance calibration using 1 thread
13:02:30 Initializing from normalized Laplacian + noise
13:02:30 Commencing optimization for 500 epochs, with 170324 positive edges
13:02:42 Optimization finished
[1] "124 0.01"13:02:42 UMAP embedding parameters a = 1.896 b = 0.8006
13:02:42 Read 1203 rows and found 38 numeric columns
13:02:42 Using Annoy for neighbor search, n_neighbors = 124
13:02:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:02:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87139fc9d1
13:02:43 Searching Annoy index using 1 thread, search_k = 12400
13:02:44 Annoy recall = 100%
13:02:52 Commencing smooth kNN distance calibration using 1 thread
13:03:09 Initializing from normalized Laplacian + noise
13:03:09 Commencing optimization for 500 epochs, with 170324 positive edges
13:03:22 Optimization finished
[1] "124 0.02"13:03:22 UMAP embedding parameters a = 1.859 b = 0.8109
13:03:22 Read 1203 rows and found 38 numeric columns
13:03:22 Using Annoy for neighbor search, n_neighbors = 124
13:03:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877148a5ae
13:03:22 Searching Annoy index using 1 thread, search_k = 12400
13:03:23 Annoy recall = 100%
13:03:32 Commencing smooth kNN distance calibration using 1 thread
13:03:48 Initializing from normalized Laplacian + noise
13:03:49 Commencing optimization for 500 epochs, with 170324 positive edges
13:04:01 Optimization finished
[1] "124 0.03"13:04:01 UMAP embedding parameters a = 1.822 b = 0.8212
13:04:01 Read 1203 rows and found 38 numeric columns
13:04:01 Using Annoy for neighbor search, n_neighbors = 124
13:04:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874770134
13:04:02 Searching Annoy index using 1 thread, search_k = 12400
13:04:03 Annoy recall = 100%
13:04:11 Commencing smooth kNN distance calibration using 1 thread
13:04:28 Initializing from normalized Laplacian + noise
13:04:28 Commencing optimization for 500 epochs, with 170324 positive edges
13:04:41 Optimization finished
[1] "124 0.04"13:04:41 UMAP embedding parameters a = 1.786 b = 0.8316
13:04:41 Read 1203 rows and found 38 numeric columns
13:04:41 Using Annoy for neighbor search, n_neighbors = 124
13:04:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:04:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a1cc69d
13:04:41 Searching Annoy index using 1 thread, search_k = 12400
13:04:42 Annoy recall = 100%
13:04:50 Commencing smooth kNN distance calibration using 1 thread
13:05:07 Initializing from normalized Laplacian + noise
13:05:07 Commencing optimization for 500 epochs, with 170324 positive edges
13:05:20 Optimization finished
[1] "124 0.05"13:05:20 UMAP embedding parameters a = 1.75 b = 0.8421
13:05:20 Read 1203 rows and found 38 numeric columns
13:05:20 Using Annoy for neighbor search, n_neighbors = 124
13:05:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:05:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766d58746
13:05:21 Searching Annoy index using 1 thread, search_k = 12400
13:05:21 Annoy recall = 100%
13:05:30 Commencing smooth kNN distance calibration using 1 thread
13:05:47 Initializing from normalized Laplacian + noise
13:05:47 Commencing optimization for 500 epochs, with 170324 positive edges
13:05:59 Optimization finished
[1] "124 0.06"13:05:59 UMAP embedding parameters a = 1.715 b = 0.8526
13:05:59 Read 1203 rows and found 38 numeric columns
13:06:00 Using Annoy for neighbor search, n_neighbors = 124
13:06:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:06:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761dce2e3
13:06:00 Searching Annoy index using 1 thread, search_k = 12400
13:06:01 Annoy recall = 100%
13:06:09 Commencing smooth kNN distance calibration using 1 thread
13:06:26 Initializing from normalized Laplacian + noise
13:06:26 Commencing optimization for 500 epochs, with 170324 positive edges
13:06:39 Optimization finished
[1] "124 0.07"13:06:39 UMAP embedding parameters a = 1.68 b = 0.8631
13:06:39 Read 1203 rows and found 38 numeric columns
13:06:39 Using Annoy for neighbor search, n_neighbors = 124
13:06:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:06:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a02bac
13:06:40 Searching Annoy index using 1 thread, search_k = 12400
13:06:41 Annoy recall = 100%
13:06:49 Commencing smooth kNN distance calibration using 1 thread
13:07:06 Initializing from normalized Laplacian + noise
13:07:06 Commencing optimization for 500 epochs, with 170324 positive edges
13:07:18 Optimization finished
[1] "124 0.08"13:07:18 UMAP embedding parameters a = 1.645 b = 0.8737
13:07:18 Read 1203 rows and found 38 numeric columns
13:07:18 Using Annoy for neighbor search, n_neighbors = 124
13:07:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:07:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725da37c2
13:07:19 Searching Annoy index using 1 thread, search_k = 12400
13:07:20 Annoy recall = 100%
13:07:28 Commencing smooth kNN distance calibration using 1 thread
13:07:45 Initializing from normalized Laplacian + noise
13:07:46 Commencing optimization for 500 epochs, with 170324 positive edges
13:07:58 Optimization finished
[1] "124 0.09"13:07:58 UMAP embedding parameters a = 1.611 b = 0.8844
13:07:58 Read 1203 rows and found 38 numeric columns
13:07:58 Using Annoy for neighbor search, n_neighbors = 124
13:07:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:07:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d3111b8
13:07:59 Searching Annoy index using 1 thread, search_k = 12400
13:08:00 Annoy recall = 100%
13:08:08 Commencing smooth kNN distance calibration using 1 thread
13:08:25 Initializing from normalized Laplacian + noise
13:08:25 Commencing optimization for 500 epochs, with 170324 positive edges
13:08:37 Optimization finished
[1] "124 0.1"13:08:38 UMAP embedding parameters a = 1.577 b = 0.8951
13:08:38 Read 1203 rows and found 38 numeric columns
13:08:38 Using Annoy for neighbor search, n_neighbors = 124
13:08:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:08:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734a064b9
13:08:38 Searching Annoy index using 1 thread, search_k = 12400
13:08:39 Annoy recall = 100%
13:08:48 Commencing smooth kNN distance calibration using 1 thread
13:09:04 Initializing from normalized Laplacian + noise
13:09:04 Commencing optimization for 500 epochs, with 170324 positive edges
13:09:17 Optimization finished
[1] "124 0.11"13:09:17 UMAP embedding parameters a = 1.544 b = 0.9058
13:09:17 Read 1203 rows and found 38 numeric columns
13:09:17 Using Annoy for neighbor search, n_neighbors = 124
13:09:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:09:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877770bc
13:09:18 Searching Annoy index using 1 thread, search_k = 12400
13:09:19 Annoy recall = 100%
13:09:27 Commencing smooth kNN distance calibration using 1 thread
13:09:44 Initializing from normalized Laplacian + noise
13:09:44 Commencing optimization for 500 epochs, with 170324 positive edges
13:09:57 Optimization finished
[1] "124 0.12"13:09:57 UMAP embedding parameters a = 1.51 b = 0.9165
13:09:57 Read 1203 rows and found 38 numeric columns
13:09:57 Using Annoy for neighbor search, n_neighbors = 124
13:09:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:09:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d47aae1
13:09:57 Searching Annoy index using 1 thread, search_k = 12400
13:09:58 Annoy recall = 100%
13:10:07 Commencing smooth kNN distance calibration using 1 thread
13:10:23 Initializing from normalized Laplacian + noise
13:10:24 Commencing optimization for 500 epochs, with 170324 positive edges
13:10:36 Optimization finished
[1] "124 0.13"13:10:36 UMAP embedding parameters a = 1.478 b = 0.9272
13:10:36 Read 1203 rows and found 38 numeric columns
13:10:36 Using Annoy for neighbor search, n_neighbors = 124
13:10:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:10:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873aa761e1
13:10:37 Searching Annoy index using 1 thread, search_k = 12400
13:10:38 Annoy recall = 100%
13:10:46 Commencing smooth kNN distance calibration using 1 thread
13:11:03 Initializing from normalized Laplacian + noise
13:11:03 Commencing optimization for 500 epochs, with 170324 positive edges
13:11:16 Optimization finished
[1] "124 0.14"13:11:16 UMAP embedding parameters a = 1.446 b = 0.938
13:11:16 Read 1203 rows and found 38 numeric columns
13:11:16 Using Annoy for neighbor search, n_neighbors = 124
13:11:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:11:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710a18dd0
13:11:16 Searching Annoy index using 1 thread, search_k = 12400
13:11:17 Annoy recall = 100%
13:11:26 Commencing smooth kNN distance calibration using 1 thread
13:11:43 Initializing from normalized Laplacian + noise
13:11:43 Commencing optimization for 500 epochs, with 170324 positive edges
13:11:55 Optimization finished
[1] "124 0.15"13:11:56 UMAP embedding parameters a = 1.414 b = 0.9488
13:11:56 Read 1203 rows and found 38 numeric columns
13:11:56 Using Annoy for neighbor search, n_neighbors = 124
13:11:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:11:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877394d9e6
13:11:56 Searching Annoy index using 1 thread, search_k = 12400
13:11:57 Annoy recall = 100%
13:12:05 Commencing smooth kNN distance calibration using 1 thread
13:12:22 Initializing from normalized Laplacian + noise
13:12:22 Commencing optimization for 500 epochs, with 170324 positive edges
13:12:35 Optimization finished
[1] "124 0.16"13:12:35 UMAP embedding parameters a = 1.383 b = 0.9596
13:12:35 Read 1203 rows and found 38 numeric columns
13:12:35 Using Annoy for neighbor search, n_neighbors = 124
13:12:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:12:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c872eff
13:12:36 Searching Annoy index using 1 thread, search_k = 12400
13:12:37 Annoy recall = 100%
13:12:45 Commencing smooth kNN distance calibration using 1 thread
13:13:02 Initializing from normalized Laplacian + noise
13:13:02 Commencing optimization for 500 epochs, with 170324 positive edges
13:13:15 Optimization finished
[1] "124 0.17"13:13:15 UMAP embedding parameters a = 1.352 b = 0.9704
13:13:15 Read 1203 rows and found 38 numeric columns
13:13:15 Using Annoy for neighbor search, n_neighbors = 124
13:13:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:13:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874853655d
13:13:15 Searching Annoy index using 1 thread, search_k = 12400
13:13:16 Annoy recall = 100%
13:13:25 Commencing smooth kNN distance calibration using 1 thread
13:13:42 Initializing from normalized Laplacian + noise
13:13:42 Commencing optimization for 500 epochs, with 170324 positive edges
13:13:54 Optimization finished
[1] "124 0.18"13:13:54 UMAP embedding parameters a = 1.321 b = 0.9813
13:13:55 Read 1203 rows and found 38 numeric columns
13:13:55 Using Annoy for neighbor search, n_neighbors = 124
13:13:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:13:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f810ea
13:13:55 Searching Annoy index using 1 thread, search_k = 12400
13:13:56 Annoy recall = 100%
13:14:04 Commencing smooth kNN distance calibration using 1 thread
13:14:21 Initializing from normalized Laplacian + noise
13:14:21 Commencing optimization for 500 epochs, with 170324 positive edges
13:14:34 Optimization finished
[1] "124 0.19"13:14:34 UMAP embedding parameters a = 1.292 b = 0.9921
13:14:34 Read 1203 rows and found 38 numeric columns
13:14:34 Using Annoy for neighbor search, n_neighbors = 124
13:14:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:14:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749f20559
13:14:35 Searching Annoy index using 1 thread, search_k = 12400
13:14:36 Annoy recall = 100%
13:14:44 Commencing smooth kNN distance calibration using 1 thread
13:15:01 Initializing from normalized Laplacian + noise
13:15:01 Commencing optimization for 500 epochs, with 170324 positive edges
13:15:14 Optimization finished
[1] "124 0.2"13:15:14 UMAP embedding parameters a = 1.262 b = 1.003
13:15:14 Read 1203 rows and found 38 numeric columns
13:15:14 Using Annoy for neighbor search, n_neighbors = 124
13:15:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:15:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879c720ff
13:15:14 Searching Annoy index using 1 thread, search_k = 12400
13:15:15 Annoy recall = 100%
13:15:24 Commencing smooth kNN distance calibration using 1 thread
13:15:41 Initializing from normalized Laplacian + noise
13:15:41 Commencing optimization for 500 epochs, with 170324 positive edges
13:15:53 Optimization finished
[1] "125 0"13:15:53 UMAP embedding parameters a = 1.933 b = 0.7905
13:15:53 Read 1203 rows and found 38 numeric columns
13:15:53 Using Annoy for neighbor search, n_neighbors = 125
13:15:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:15:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875385eb82
13:15:54 Searching Annoy index using 1 thread, search_k = 12500
13:15:55 Annoy recall = 100%
13:16:03 Commencing smooth kNN distance calibration using 1 thread
13:16:20 Initializing from normalized Laplacian + noise
13:16:21 Commencing optimization for 500 epochs, with 171606 positive edges
13:16:33 Optimization finished
[1] "125 0.01"13:16:33 UMAP embedding parameters a = 1.896 b = 0.8006
13:16:33 Read 1203 rows and found 38 numeric columns
13:16:33 Using Annoy for neighbor search, n_neighbors = 125
13:16:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:16:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739705d74
13:16:34 Searching Annoy index using 1 thread, search_k = 12500
13:16:35 Annoy recall = 100%
13:16:43 Commencing smooth kNN distance calibration using 1 thread
13:17:00 Initializing from normalized Laplacian + noise
13:17:00 Commencing optimization for 500 epochs, with 171606 positive edges
13:17:13 Optimization finished
[1] "125 0.02"13:17:13 UMAP embedding parameters a = 1.859 b = 0.8109
13:17:13 Read 1203 rows and found 38 numeric columns
13:17:13 Using Annoy for neighbor search, n_neighbors = 125
13:17:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:17:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87718b982d
13:17:14 Searching Annoy index using 1 thread, search_k = 12500
13:17:15 Annoy recall = 100%
13:17:23 Commencing smooth kNN distance calibration using 1 thread
13:17:40 Initializing from normalized Laplacian + noise
13:17:40 Commencing optimization for 500 epochs, with 171606 positive edges
13:17:52 Optimization finished
[1] "125 0.03"13:17:53 UMAP embedding parameters a = 1.822 b = 0.8212
13:17:53 Read 1203 rows and found 38 numeric columns
13:17:53 Using Annoy for neighbor search, n_neighbors = 125
13:17:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:17:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c333c68
13:17:53 Searching Annoy index using 1 thread, search_k = 12500
13:17:54 Annoy recall = 100%
13:18:03 Commencing smooth kNN distance calibration using 1 thread
13:18:20 Initializing from normalized Laplacian + noise
13:18:20 Commencing optimization for 500 epochs, with 171606 positive edges
13:18:32 Optimization finished
[1] "125 0.04"13:18:32 UMAP embedding parameters a = 1.786 b = 0.8316
13:18:32 Read 1203 rows and found 38 numeric columns
13:18:32 Using Annoy for neighbor search, n_neighbors = 125
13:18:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:18:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744dd774d
13:18:33 Searching Annoy index using 1 thread, search_k = 12500
13:18:34 Annoy recall = 100%
13:18:42 Commencing smooth kNN distance calibration using 1 thread
13:18:59 Initializing from normalized Laplacian + noise
13:18:59 Commencing optimization for 500 epochs, with 171606 positive edges
13:19:12 Optimization finished
[1] "125 0.05"13:19:12 UMAP embedding parameters a = 1.75 b = 0.8421
13:19:12 Read 1203 rows and found 38 numeric columns
13:19:12 Using Annoy for neighbor search, n_neighbors = 125
13:19:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:19:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c2d7c13
13:19:13 Searching Annoy index using 1 thread, search_k = 12500
13:19:14 Annoy recall = 100%
13:19:22 Commencing smooth kNN distance calibration using 1 thread
13:19:39 Initializing from normalized Laplacian + noise
13:19:39 Commencing optimization for 500 epochs, with 171606 positive edges
13:19:51 Optimization finished
[1] "125 0.06"13:19:52 UMAP embedding parameters a = 1.715 b = 0.8526
13:19:52 Read 1203 rows and found 38 numeric columns
13:19:52 Using Annoy for neighbor search, n_neighbors = 125
13:19:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:19:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710386129
13:19:52 Searching Annoy index using 1 thread, search_k = 12500
13:19:53 Annoy recall = 100%
13:20:02 Commencing smooth kNN distance calibration using 1 thread
13:20:19 Initializing from normalized Laplacian + noise
13:20:19 Commencing optimization for 500 epochs, with 171606 positive edges
13:20:31 Optimization finished
[1] "125 0.07"13:20:31 UMAP embedding parameters a = 1.68 b = 0.8631
13:20:31 Read 1203 rows and found 38 numeric columns
13:20:31 Using Annoy for neighbor search, n_neighbors = 125
13:20:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:20:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87642075f1
13:20:32 Searching Annoy index using 1 thread, search_k = 12500
13:20:33 Annoy recall = 100%
13:20:41 Commencing smooth kNN distance calibration using 1 thread
13:20:58 Initializing from normalized Laplacian + noise
13:20:58 Commencing optimization for 500 epochs, with 171606 positive edges
13:21:11 Optimization finished
[1] "125 0.08"13:21:11 UMAP embedding parameters a = 1.645 b = 0.8737
13:21:11 Read 1203 rows and found 38 numeric columns
13:21:11 Using Annoy for neighbor search, n_neighbors = 125
13:21:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:21:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754d4de1e
13:21:11 Searching Annoy index using 1 thread, search_k = 12500
13:21:12 Annoy recall = 100%
13:21:21 Commencing smooth kNN distance calibration using 1 thread
13:21:37 Initializing from normalized Laplacian + noise
13:21:38 Commencing optimization for 500 epochs, with 171606 positive edges
13:21:50 Optimization finished
[1] "125 0.09"13:21:50 UMAP embedding parameters a = 1.611 b = 0.8844
13:21:50 Read 1203 rows and found 38 numeric columns
13:21:50 Using Annoy for neighbor search, n_neighbors = 125
13:21:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:21:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753306a68
13:21:51 Searching Annoy index using 1 thread, search_k = 12500
13:21:52 Annoy recall = 100%
13:22:00 Commencing smooth kNN distance calibration using 1 thread
13:22:17 Initializing from normalized Laplacian + noise
13:22:17 Commencing optimization for 500 epochs, with 171606 positive edges
13:22:29 Optimization finished
[1] "125 0.1"13:22:30 UMAP embedding parameters a = 1.577 b = 0.8951
13:22:30 Read 1203 rows and found 38 numeric columns
13:22:30 Using Annoy for neighbor search, n_neighbors = 125
13:22:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:22:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bed0e2f
13:22:30 Searching Annoy index using 1 thread, search_k = 12500
13:22:31 Annoy recall = 100%
13:22:39 Commencing smooth kNN distance calibration using 1 thread
13:22:56 Initializing from normalized Laplacian + noise
13:22:56 Commencing optimization for 500 epochs, with 171606 positive edges
13:23:09 Optimization finished
[1] "125 0.11"13:23:09 UMAP embedding parameters a = 1.544 b = 0.9058
13:23:09 Read 1203 rows and found 38 numeric columns
13:23:09 Using Annoy for neighbor search, n_neighbors = 125
13:23:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:23:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876874a7ef
13:23:09 Searching Annoy index using 1 thread, search_k = 12500
13:23:10 Annoy recall = 100%
13:23:19 Commencing smooth kNN distance calibration using 1 thread
13:23:36 Initializing from normalized Laplacian + noise
13:23:36 Commencing optimization for 500 epochs, with 171606 positive edges
13:23:48 Optimization finished
[1] "125 0.12"13:23:48 UMAP embedding parameters a = 1.51 b = 0.9165
13:23:48 Read 1203 rows and found 38 numeric columns
13:23:48 Using Annoy for neighbor search, n_neighbors = 125
13:23:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:23:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744791016
13:23:49 Searching Annoy index using 1 thread, search_k = 12500
13:23:50 Annoy recall = 100%
13:23:58 Commencing smooth kNN distance calibration using 1 thread
13:24:15 Initializing from normalized Laplacian + noise
13:24:15 Commencing optimization for 500 epochs, with 171606 positive edges
13:24:28 Optimization finished
[1] "125 0.13"13:24:28 UMAP embedding parameters a = 1.478 b = 0.9272
13:24:28 Read 1203 rows and found 38 numeric columns
13:24:28 Using Annoy for neighbor search, n_neighbors = 125
13:24:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:24:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760640f63
13:24:28 Searching Annoy index using 1 thread, search_k = 12500
13:24:29 Annoy recall = 100%
13:24:38 Commencing smooth kNN distance calibration using 1 thread
13:24:54 Initializing from normalized Laplacian + noise
13:24:54 Commencing optimization for 500 epochs, with 171606 positive edges
13:25:07 Optimization finished
[1] "125 0.14"13:25:07 UMAP embedding parameters a = 1.446 b = 0.938
13:25:07 Read 1203 rows and found 38 numeric columns
13:25:07 Using Annoy for neighbor search, n_neighbors = 125
13:25:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:25:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732916e8c
13:25:08 Searching Annoy index using 1 thread, search_k = 12500
13:25:09 Annoy recall = 100%
13:25:17 Commencing smooth kNN distance calibration using 1 thread
13:25:34 Initializing from normalized Laplacian + noise
13:25:34 Commencing optimization for 500 epochs, with 171606 positive edges
13:25:46 Optimization finished
[1] "125 0.15"13:25:47 UMAP embedding parameters a = 1.414 b = 0.9488
13:25:47 Read 1203 rows and found 38 numeric columns
13:25:47 Using Annoy for neighbor search, n_neighbors = 125
13:25:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:25:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b4e975d
13:25:47 Searching Annoy index using 1 thread, search_k = 12500
13:25:48 Annoy recall = 100%
13:25:56 Commencing smooth kNN distance calibration using 1 thread
13:26:13 Initializing from normalized Laplacian + noise
13:26:13 Commencing optimization for 500 epochs, with 171606 positive edges
13:26:26 Optimization finished
[1] "125 0.16"13:26:26 UMAP embedding parameters a = 1.383 b = 0.9596
13:26:26 Read 1203 rows and found 38 numeric columns
13:26:26 Using Annoy for neighbor search, n_neighbors = 125
13:26:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:26:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874240f246
13:26:27 Searching Annoy index using 1 thread, search_k = 12500
13:26:27 Annoy recall = 100%
13:26:36 Commencing smooth kNN distance calibration using 1 thread
13:26:53 Initializing from normalized Laplacian + noise
13:26:53 Commencing optimization for 500 epochs, with 171606 positive edges
13:27:05 Optimization finished
[1] "125 0.17"13:27:05 UMAP embedding parameters a = 1.352 b = 0.9704
13:27:05 Read 1203 rows and found 38 numeric columns
13:27:05 Using Annoy for neighbor search, n_neighbors = 125
13:27:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:27:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734319a38
13:27:06 Searching Annoy index using 1 thread, search_k = 12500
13:27:07 Annoy recall = 100%
13:27:15 Commencing smooth kNN distance calibration using 1 thread
13:27:32 Initializing from normalized Laplacian + noise
13:27:32 Commencing optimization for 500 epochs, with 171606 positive edges
13:27:45 Optimization finished
[1] "125 0.18"13:27:45 UMAP embedding parameters a = 1.321 b = 0.9813
13:27:45 Read 1203 rows and found 38 numeric columns
13:27:45 Using Annoy for neighbor search, n_neighbors = 125
13:27:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:27:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875128cf1f
13:27:46 Searching Annoy index using 1 thread, search_k = 12500
13:27:47 Annoy recall = 100%
13:27:55 Commencing smooth kNN distance calibration using 1 thread
13:28:12 Initializing from normalized Laplacian + noise
13:28:12 Commencing optimization for 500 epochs, with 171606 positive edges
13:28:24 Optimization finished
[1] "125 0.19"13:28:24 UMAP embedding parameters a = 1.292 b = 0.9921
13:28:24 Read 1203 rows and found 38 numeric columns
13:28:24 Using Annoy for neighbor search, n_neighbors = 125
13:28:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:28:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f7203ff
13:28:25 Searching Annoy index using 1 thread, search_k = 12500
13:28:26 Annoy recall = 100%
13:28:34 Commencing smooth kNN distance calibration using 1 thread
13:28:51 Initializing from normalized Laplacian + noise
13:28:51 Commencing optimization for 500 epochs, with 171606 positive edges
13:29:04 Optimization finished
[1] "125 0.2"13:29:04 UMAP embedding parameters a = 1.262 b = 1.003
13:29:04 Read 1203 rows and found 38 numeric columns
13:29:04 Using Annoy for neighbor search, n_neighbors = 125
13:29:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:29:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768d1fef2
13:29:04 Searching Annoy index using 1 thread, search_k = 12500
13:29:05 Annoy recall = 100%
13:29:14 Commencing smooth kNN distance calibration using 1 thread
13:29:31 Initializing from normalized Laplacian + noise
13:29:31 Commencing optimization for 500 epochs, with 171606 positive edges
13:29:43 Optimization finished
[1] "126 0"13:29:44 UMAP embedding parameters a = 1.933 b = 0.7905
13:29:44 Read 1203 rows and found 38 numeric columns
13:29:44 Using Annoy for neighbor search, n_neighbors = 126
13:29:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:29:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751a03fdb
13:29:44 Searching Annoy index using 1 thread, search_k = 12600
13:29:45 Annoy recall = 100%
13:29:53 Commencing smooth kNN distance calibration using 1 thread
13:30:10 Initializing from normalized Laplacian + noise
13:30:10 Commencing optimization for 500 epochs, with 172856 positive edges
13:30:23 Optimization finished
[1] "126 0.01"13:30:23 UMAP embedding parameters a = 1.896 b = 0.8006
13:30:23 Read 1203 rows and found 38 numeric columns
13:30:23 Using Annoy for neighbor search, n_neighbors = 126
13:30:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:30:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cb9aee0
13:30:24 Searching Annoy index using 1 thread, search_k = 12600
13:30:24 Annoy recall = 100%
13:30:33 Commencing smooth kNN distance calibration using 1 thread
13:30:50 Initializing from normalized Laplacian + noise
13:30:50 Commencing optimization for 500 epochs, with 172856 positive edges
13:31:02 Optimization finished
[1] "126 0.02"13:31:03 UMAP embedding parameters a = 1.859 b = 0.8109
13:31:03 Read 1203 rows and found 38 numeric columns
13:31:03 Using Annoy for neighbor search, n_neighbors = 126
13:31:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:31:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87237960d3
13:31:03 Searching Annoy index using 1 thread, search_k = 12600
13:31:04 Annoy recall = 100%
13:31:13 Commencing smooth kNN distance calibration using 1 thread
13:31:30 Initializing from normalized Laplacian + noise
13:31:30 Commencing optimization for 500 epochs, with 172856 positive edges
13:31:42 Optimization finished
[1] "126 0.03"13:31:42 UMAP embedding parameters a = 1.822 b = 0.8212
13:31:42 Read 1203 rows and found 38 numeric columns
13:31:42 Using Annoy for neighbor search, n_neighbors = 126
13:31:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:31:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876241cdab
13:31:43 Searching Annoy index using 1 thread, search_k = 12600
13:31:44 Annoy recall = 100%
13:31:52 Commencing smooth kNN distance calibration using 1 thread
13:32:09 Initializing from normalized Laplacian + noise
13:32:09 Commencing optimization for 500 epochs, with 172856 positive edges
13:32:22 Optimization finished
[1] "126 0.04"13:32:22 UMAP embedding parameters a = 1.786 b = 0.8316
13:32:22 Read 1203 rows and found 38 numeric columns
13:32:22 Using Annoy for neighbor search, n_neighbors = 126
13:32:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:32:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87204e88c7
13:32:22 Searching Annoy index using 1 thread, search_k = 12600
13:32:23 Annoy recall = 100%
13:32:32 Commencing smooth kNN distance calibration using 1 thread
13:32:49 Initializing from normalized Laplacian + noise
13:32:49 Commencing optimization for 500 epochs, with 172856 positive edges
13:33:02 Optimization finished
[1] "126 0.05"13:33:02 UMAP embedding parameters a = 1.75 b = 0.8421
13:33:02 Read 1203 rows and found 38 numeric columns
13:33:02 Using Annoy for neighbor search, n_neighbors = 126
13:33:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:33:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760008fd2
13:33:02 Searching Annoy index using 1 thread, search_k = 12600
13:33:03 Annoy recall = 100%
13:33:12 Commencing smooth kNN distance calibration using 1 thread
13:33:29 Initializing from normalized Laplacian + noise
13:33:29 Commencing optimization for 500 epochs, with 172856 positive edges
13:33:41 Optimization finished
[1] "126 0.06"13:33:41 UMAP embedding parameters a = 1.715 b = 0.8526
13:33:41 Read 1203 rows and found 38 numeric columns
13:33:41 Using Annoy for neighbor search, n_neighbors = 126
13:33:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:33:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a953308
13:33:42 Searching Annoy index using 1 thread, search_k = 12600
13:33:43 Annoy recall = 100%
13:33:51 Commencing smooth kNN distance calibration using 1 thread
13:34:08 Initializing from normalized Laplacian + noise
13:34:08 Commencing optimization for 500 epochs, with 172856 positive edges
13:34:21 Optimization finished
[1] "126 0.07"13:34:21 UMAP embedding parameters a = 1.68 b = 0.8631
13:34:21 Read 1203 rows and found 38 numeric columns
13:34:21 Using Annoy for neighbor search, n_neighbors = 126
13:34:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:34:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87214699b1
13:34:22 Searching Annoy index using 1 thread, search_k = 12600
13:34:23 Annoy recall = 100%
13:34:31 Commencing smooth kNN distance calibration using 1 thread
13:34:48 Initializing from normalized Laplacian + noise
13:34:48 Commencing optimization for 500 epochs, with 172856 positive edges
13:35:01 Optimization finished
[1] "126 0.08"13:35:01 UMAP embedding parameters a = 1.645 b = 0.8737
13:35:01 Read 1203 rows and found 38 numeric columns
13:35:01 Using Annoy for neighbor search, n_neighbors = 126
13:35:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:35:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729f2952b
13:35:01 Searching Annoy index using 1 thread, search_k = 12600
13:35:02 Annoy recall = 100%
13:35:11 Commencing smooth kNN distance calibration using 1 thread
13:35:28 Initializing from normalized Laplacian + noise
13:35:28 Commencing optimization for 500 epochs, with 172856 positive edges
13:35:40 Optimization finished
[1] "126 0.09"13:35:40 UMAP embedding parameters a = 1.611 b = 0.8844
13:35:40 Read 1203 rows and found 38 numeric columns
13:35:40 Using Annoy for neighbor search, n_neighbors = 126
13:35:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:35:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87345c5408
13:35:41 Searching Annoy index using 1 thread, search_k = 12600
13:35:42 Annoy recall = 100%
13:35:50 Commencing smooth kNN distance calibration using 1 thread
13:36:08 Initializing from normalized Laplacian + noise
13:36:08 Commencing optimization for 500 epochs, with 172856 positive edges
13:36:20 Optimization finished
[1] "126 0.1"13:36:20 UMAP embedding parameters a = 1.577 b = 0.8951
13:36:20 Read 1203 rows and found 38 numeric columns
13:36:20 Using Annoy for neighbor search, n_neighbors = 126
13:36:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:36:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774cc8533
13:36:21 Searching Annoy index using 1 thread, search_k = 12600
13:36:22 Annoy recall = 100%
13:36:30 Commencing smooth kNN distance calibration using 1 thread
13:36:47 Initializing from normalized Laplacian + noise
13:36:47 Commencing optimization for 500 epochs, with 172856 positive edges
13:37:00 Optimization finished
[1] "126 0.11"13:37:00 UMAP embedding parameters a = 1.544 b = 0.9058
13:37:00 Read 1203 rows and found 38 numeric columns
13:37:00 Using Annoy for neighbor search, n_neighbors = 126
13:37:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:37:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876362f2a0
13:37:01 Searching Annoy index using 1 thread, search_k = 12600
13:37:02 Annoy recall = 100%
13:37:10 Commencing smooth kNN distance calibration using 1 thread
13:37:27 Initializing from normalized Laplacian + noise
13:37:27 Commencing optimization for 500 epochs, with 172856 positive edges
13:37:39 Optimization finished
[1] "126 0.12"13:37:40 UMAP embedding parameters a = 1.51 b = 0.9165
13:37:40 Read 1203 rows and found 38 numeric columns
13:37:40 Using Annoy for neighbor search, n_neighbors = 126
13:37:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:37:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725e7ec35
13:37:40 Searching Annoy index using 1 thread, search_k = 12600
13:37:41 Annoy recall = 100%
13:37:50 Commencing smooth kNN distance calibration using 1 thread
13:38:07 Initializing from normalized Laplacian + noise
13:38:07 Commencing optimization for 500 epochs, with 172856 positive edges
13:38:19 Optimization finished
[1] "126 0.13"13:38:20 UMAP embedding parameters a = 1.478 b = 0.9272
13:38:20 Read 1203 rows and found 38 numeric columns
13:38:20 Using Annoy for neighbor search, n_neighbors = 126
13:38:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:38:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710ffc19c
13:38:20 Searching Annoy index using 1 thread, search_k = 12600
13:38:21 Annoy recall = 100%
13:38:29 Commencing smooth kNN distance calibration using 1 thread
13:38:46 Initializing from normalized Laplacian + noise
13:38:47 Commencing optimization for 500 epochs, with 172856 positive edges
13:38:59 Optimization finished
[1] "126 0.14"13:38:59 UMAP embedding parameters a = 1.446 b = 0.938
13:38:59 Read 1203 rows and found 38 numeric columns
13:38:59 Using Annoy for neighbor search, n_neighbors = 126
13:38:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:39:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87284069ed
13:39:00 Searching Annoy index using 1 thread, search_k = 12600
13:39:01 Annoy recall = 100%
13:39:09 Commencing smooth kNN distance calibration using 1 thread
13:39:26 Initializing from normalized Laplacian + noise
13:39:26 Commencing optimization for 500 epochs, with 172856 positive edges
13:39:39 Optimization finished
[1] "126 0.15"13:39:39 UMAP embedding parameters a = 1.414 b = 0.9488
13:39:39 Read 1203 rows and found 38 numeric columns
13:39:39 Using Annoy for neighbor search, n_neighbors = 126
13:39:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:39:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872156848
13:39:40 Searching Annoy index using 1 thread, search_k = 12600
13:39:41 Annoy recall = 100%
13:39:49 Commencing smooth kNN distance calibration using 1 thread
13:40:06 Initializing from normalized Laplacian + noise
13:40:06 Commencing optimization for 500 epochs, with 172856 positive edges
13:40:19 Optimization finished
[1] "126 0.16"13:40:19 UMAP embedding parameters a = 1.383 b = 0.9596
13:40:19 Read 1203 rows and found 38 numeric columns
13:40:19 Using Annoy for neighbor search, n_neighbors = 126
13:40:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:40:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87213822c5
13:40:20 Searching Annoy index using 1 thread, search_k = 12600
13:40:21 Annoy recall = 100%
13:40:29 Commencing smooth kNN distance calibration using 1 thread
13:40:46 Initializing from normalized Laplacian + noise
13:40:46 Commencing optimization for 500 epochs, with 172856 positive edges
13:40:58 Optimization finished
[1] "126 0.17"13:40:59 UMAP embedding parameters a = 1.352 b = 0.9704
13:40:59 Read 1203 rows and found 38 numeric columns
13:40:59 Using Annoy for neighbor search, n_neighbors = 126
13:40:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:40:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c60dfdf
13:40:59 Searching Annoy index using 1 thread, search_k = 12600
13:41:00 Annoy recall = 100%
13:41:09 Commencing smooth kNN distance calibration using 1 thread
13:41:26 Initializing from normalized Laplacian + noise
13:41:26 Commencing optimization for 500 epochs, with 172856 positive edges
13:41:38 Optimization finished
[1] "126 0.18"13:41:39 UMAP embedding parameters a = 1.321 b = 0.9813
13:41:39 Read 1203 rows and found 38 numeric columns
13:41:39 Using Annoy for neighbor search, n_neighbors = 126
13:41:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:41:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756ea4667
13:41:39 Searching Annoy index using 1 thread, search_k = 12600
13:41:40 Annoy recall = 100%
13:41:48 Commencing smooth kNN distance calibration using 1 thread
13:42:06 Initializing from normalized Laplacian + noise
13:42:06 Commencing optimization for 500 epochs, with 172856 positive edges
13:42:18 Optimization finished
[1] "126 0.19"13:42:19 UMAP embedding parameters a = 1.292 b = 0.9921
13:42:19 Read 1203 rows and found 38 numeric columns
13:42:19 Using Annoy for neighbor search, n_neighbors = 126
13:42:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:42:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774688d2d
13:42:19 Searching Annoy index using 1 thread, search_k = 12600
13:42:20 Annoy recall = 100%
13:42:29 Commencing smooth kNN distance calibration using 1 thread
13:42:45 Initializing from normalized Laplacian + noise
13:42:46 Commencing optimization for 500 epochs, with 172856 positive edges
13:42:58 Optimization finished
[1] "126 0.2"13:42:58 UMAP embedding parameters a = 1.262 b = 1.003
13:42:58 Read 1203 rows and found 38 numeric columns
13:42:58 Using Annoy for neighbor search, n_neighbors = 126
13:42:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:42:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87684dee0e
13:42:59 Searching Annoy index using 1 thread, search_k = 12600
13:43:00 Annoy recall = 100%
13:43:08 Commencing smooth kNN distance calibration using 1 thread
13:43:25 Initializing from normalized Laplacian + noise
13:43:26 Commencing optimization for 500 epochs, with 172856 positive edges
13:43:38 Optimization finished
[1] "127 0"13:43:38 UMAP embedding parameters a = 1.933 b = 0.7905
13:43:38 Read 1203 rows and found 38 numeric columns
13:43:38 Using Annoy for neighbor search, n_neighbors = 127
13:43:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:43:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f5eee56
13:43:39 Searching Annoy index using 1 thread, search_k = 12700
13:43:40 Annoy recall = 100%
13:43:48 Commencing smooth kNN distance calibration using 1 thread
13:44:05 Initializing from normalized Laplacian + noise
13:44:05 Commencing optimization for 500 epochs, with 174066 positive edges
13:44:18 Optimization finished
[1] "127 0.01"13:44:18 UMAP embedding parameters a = 1.896 b = 0.8006
13:44:18 Read 1203 rows and found 38 numeric columns
13:44:18 Using Annoy for neighbor search, n_neighbors = 127
13:44:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:44:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738e19d44
13:44:19 Searching Annoy index using 1 thread, search_k = 12700
13:44:20 Annoy recall = 100%
13:44:28 Commencing smooth kNN distance calibration using 1 thread
13:44:45 Initializing from normalized Laplacian + noise
13:44:45 Commencing optimization for 500 epochs, with 174066 positive edges
13:44:58 Optimization finished
[1] "127 0.02"13:44:58 UMAP embedding parameters a = 1.859 b = 0.8109
13:44:58 Read 1203 rows and found 38 numeric columns
13:44:58 Using Annoy for neighbor search, n_neighbors = 127
13:44:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:44:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748b1fd71
13:44:59 Searching Annoy index using 1 thread, search_k = 12700
13:45:00 Annoy recall = 100%
13:45:08 Commencing smooth kNN distance calibration using 1 thread
13:45:25 Initializing from normalized Laplacian + noise
13:45:25 Commencing optimization for 500 epochs, with 174066 positive edges
13:45:38 Optimization finished
[1] "127 0.03"13:45:38 UMAP embedding parameters a = 1.822 b = 0.8212
13:45:38 Read 1203 rows and found 38 numeric columns
13:45:38 Using Annoy for neighbor search, n_neighbors = 127
13:45:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:45:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771f05ce3
13:45:39 Searching Annoy index using 1 thread, search_k = 12700
13:45:40 Annoy recall = 100%
13:45:48 Commencing smooth kNN distance calibration using 1 thread
13:46:05 Initializing from normalized Laplacian + noise
13:46:05 Commencing optimization for 500 epochs, with 174066 positive edges
13:46:18 Optimization finished
[1] "127 0.04"13:46:18 UMAP embedding parameters a = 1.786 b = 0.8316
13:46:18 Read 1203 rows and found 38 numeric columns
13:46:18 Using Annoy for neighbor search, n_neighbors = 127
13:46:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:46:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87643034a1
13:46:18 Searching Annoy index using 1 thread, search_k = 12700
13:46:19 Annoy recall = 100%
13:46:28 Commencing smooth kNN distance calibration using 1 thread
13:46:45 Initializing from normalized Laplacian + noise
13:46:45 Commencing optimization for 500 epochs, with 174066 positive edges
13:46:58 Optimization finished
[1] "127 0.05"13:46:58 UMAP embedding parameters a = 1.75 b = 0.8421
13:46:58 Read 1203 rows and found 38 numeric columns
13:46:58 Using Annoy for neighbor search, n_neighbors = 127
13:46:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:46:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87af2efb7
13:46:59 Searching Annoy index using 1 thread, search_k = 12700
13:46:59 Annoy recall = 100%
13:47:08 Commencing smooth kNN distance calibration using 1 thread
13:47:25 Initializing from normalized Laplacian + noise
13:47:25 Commencing optimization for 500 epochs, with 174066 positive edges
13:47:38 Optimization finished
[1] "127 0.06"13:47:38 UMAP embedding parameters a = 1.715 b = 0.8526
13:47:38 Read 1203 rows and found 38 numeric columns
13:47:38 Using Annoy for neighbor search, n_neighbors = 127
13:47:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:47:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872621f71b
13:47:39 Searching Annoy index using 1 thread, search_k = 12700
13:47:40 Annoy recall = 100%
13:47:48 Commencing smooth kNN distance calibration using 1 thread
13:48:05 Initializing from normalized Laplacian + noise
13:48:05 Commencing optimization for 500 epochs, with 174066 positive edges
13:48:18 Optimization finished
[1] "127 0.07"13:48:18 UMAP embedding parameters a = 1.68 b = 0.8631
13:48:18 Read 1203 rows and found 38 numeric columns
13:48:18 Using Annoy for neighbor search, n_neighbors = 127
13:48:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:48:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87355903c0
13:48:19 Searching Annoy index using 1 thread, search_k = 12700
13:48:19 Annoy recall = 100%
13:48:28 Commencing smooth kNN distance calibration using 1 thread
13:48:45 Initializing from normalized Laplacian + noise
13:48:45 Commencing optimization for 500 epochs, with 174066 positive edges
13:48:58 Optimization finished
[1] "127 0.08"13:48:58 UMAP embedding parameters a = 1.645 b = 0.8737
13:48:58 Read 1203 rows and found 38 numeric columns
13:48:58 Using Annoy for neighbor search, n_neighbors = 127
13:48:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:48:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a64f3b6
13:48:59 Searching Annoy index using 1 thread, search_k = 12700
13:49:00 Annoy recall = 100%
13:49:08 Commencing smooth kNN distance calibration using 1 thread
13:49:25 Initializing from normalized Laplacian + noise
13:49:25 Commencing optimization for 500 epochs, with 174066 positive edges
13:49:38 Optimization finished
[1] "127 0.09"13:49:38 UMAP embedding parameters a = 1.611 b = 0.8844
13:49:38 Read 1203 rows and found 38 numeric columns
13:49:38 Using Annoy for neighbor search, n_neighbors = 127
13:49:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:49:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ef3f60d
13:49:39 Searching Annoy index using 1 thread, search_k = 12700
13:49:40 Annoy recall = 100%
13:49:48 Commencing smooth kNN distance calibration using 1 thread
13:50:05 Initializing from normalized Laplacian + noise
13:50:05 Commencing optimization for 500 epochs, with 174066 positive edges
13:50:18 Optimization finished
[1] "127 0.1"13:50:18 UMAP embedding parameters a = 1.577 b = 0.8951
13:50:18 Read 1203 rows and found 38 numeric columns
13:50:18 Using Annoy for neighbor search, n_neighbors = 127
13:50:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:50:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f9439b
13:50:19 Searching Annoy index using 1 thread, search_k = 12700
13:50:20 Annoy recall = 100%
13:50:28 Commencing smooth kNN distance calibration using 1 thread
13:50:45 Initializing from normalized Laplacian + noise
13:50:45 Commencing optimization for 500 epochs, with 174066 positive edges
13:50:58 Optimization finished
[1] "127 0.11"13:50:58 UMAP embedding parameters a = 1.544 b = 0.9058
13:50:58 Read 1203 rows and found 38 numeric columns
13:50:58 Using Annoy for neighbor search, n_neighbors = 127
13:50:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:50:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87771ea297
13:50:59 Searching Annoy index using 1 thread, search_k = 12700
13:51:00 Annoy recall = 100%
13:51:08 Commencing smooth kNN distance calibration using 1 thread
13:51:25 Initializing from normalized Laplacian + noise
13:51:25 Commencing optimization for 500 epochs, with 174066 positive edges
13:51:38 Optimization finished
[1] "127 0.12"13:51:38 UMAP embedding parameters a = 1.51 b = 0.9165
13:51:38 Read 1203 rows and found 38 numeric columns
13:51:38 Using Annoy for neighbor search, n_neighbors = 127
13:51:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:51:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87326d56e0
13:51:39 Searching Annoy index using 1 thread, search_k = 12700
13:51:40 Annoy recall = 100%
13:51:48 Commencing smooth kNN distance calibration using 1 thread
13:52:06 Initializing from normalized Laplacian + noise
13:52:06 Commencing optimization for 500 epochs, with 174066 positive edges
13:52:18 Optimization finished
[1] "127 0.13"13:52:18 UMAP embedding parameters a = 1.478 b = 0.9272
13:52:18 Read 1203 rows and found 38 numeric columns
13:52:18 Using Annoy for neighbor search, n_neighbors = 127
13:52:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:52:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87693b1146
13:52:19 Searching Annoy index using 1 thread, search_k = 12700
13:52:20 Annoy recall = 100%
13:52:28 Commencing smooth kNN distance calibration using 1 thread
13:52:46 Initializing from normalized Laplacian + noise
13:52:46 Commencing optimization for 500 epochs, with 174066 positive edges
13:52:58 Optimization finished
[1] "127 0.14"13:52:59 UMAP embedding parameters a = 1.446 b = 0.938
13:52:59 Read 1203 rows and found 38 numeric columns
13:52:59 Using Annoy for neighbor search, n_neighbors = 127
13:52:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:52:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87176d2b5e
13:52:59 Searching Annoy index using 1 thread, search_k = 12700
13:53:00 Annoy recall = 100%
13:53:09 Commencing smooth kNN distance calibration using 1 thread
13:53:26 Initializing from normalized Laplacian + noise
13:53:26 Commencing optimization for 500 epochs, with 174066 positive edges
13:53:38 Optimization finished
[1] "127 0.15"13:53:38 UMAP embedding parameters a = 1.414 b = 0.9488
13:53:39 Read 1203 rows and found 38 numeric columns
13:53:39 Using Annoy for neighbor search, n_neighbors = 127
13:53:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:53:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87126de6b3
13:53:39 Searching Annoy index using 1 thread, search_k = 12700
13:53:40 Annoy recall = 100%
13:53:49 Commencing smooth kNN distance calibration using 1 thread
13:54:06 Initializing from normalized Laplacian + noise
13:54:06 Commencing optimization for 500 epochs, with 174066 positive edges
13:54:19 Optimization finished
[1] "127 0.16"13:54:19 UMAP embedding parameters a = 1.383 b = 0.9596
13:54:19 Read 1203 rows and found 38 numeric columns
13:54:19 Using Annoy for neighbor search, n_neighbors = 127
13:54:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:54:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713d0444f
13:54:19 Searching Annoy index using 1 thread, search_k = 12700
13:54:20 Annoy recall = 100%
13:54:29 Commencing smooth kNN distance calibration using 1 thread
13:54:46 Initializing from normalized Laplacian + noise
13:54:46 Commencing optimization for 500 epochs, with 174066 positive edges
13:54:59 Optimization finished
[1] "127 0.17"13:54:59 UMAP embedding parameters a = 1.352 b = 0.9704
13:54:59 Read 1203 rows and found 38 numeric columns
13:54:59 Using Annoy for neighbor search, n_neighbors = 127
13:54:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:54:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738b3c50f
13:54:59 Searching Annoy index using 1 thread, search_k = 12700
13:55:00 Annoy recall = 100%
13:55:09 Commencing smooth kNN distance calibration using 1 thread
13:55:26 Initializing from normalized Laplacian + noise
13:55:26 Commencing optimization for 500 epochs, with 174066 positive edges
13:55:39 Optimization finished
[1] "127 0.18"13:55:39 UMAP embedding parameters a = 1.321 b = 0.9813
13:55:39 Read 1203 rows and found 38 numeric columns
13:55:39 Using Annoy for neighbor search, n_neighbors = 127
13:55:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:55:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c607bde
13:55:40 Searching Annoy index using 1 thread, search_k = 12700
13:55:40 Annoy recall = 100%
13:55:49 Commencing smooth kNN distance calibration using 1 thread
13:56:06 Initializing from normalized Laplacian + noise
13:56:06 Commencing optimization for 500 epochs, with 174066 positive edges
13:56:19 Optimization finished
[1] "127 0.19"13:56:19 UMAP embedding parameters a = 1.292 b = 0.9921
13:56:19 Read 1203 rows and found 38 numeric columns
13:56:19 Using Annoy for neighbor search, n_neighbors = 127
13:56:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:56:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87482c9857
13:56:20 Searching Annoy index using 1 thread, search_k = 12700
13:56:21 Annoy recall = 100%
13:56:29 Commencing smooth kNN distance calibration using 1 thread
13:56:46 Initializing from normalized Laplacian + noise
13:56:46 Commencing optimization for 500 epochs, with 174066 positive edges
13:56:59 Optimization finished
[1] "127 0.2"13:56:59 UMAP embedding parameters a = 1.262 b = 1.003
13:56:59 Read 1203 rows and found 38 numeric columns
13:56:59 Using Annoy for neighbor search, n_neighbors = 127
13:56:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:57:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d804a43
13:57:00 Searching Annoy index using 1 thread, search_k = 12700
13:57:01 Annoy recall = 100%
13:57:09 Commencing smooth kNN distance calibration using 1 thread
13:57:27 Initializing from normalized Laplacian + noise
13:57:27 Commencing optimization for 500 epochs, with 174066 positive edges
13:57:39 Optimization finished
[1] "128 0"13:57:39 UMAP embedding parameters a = 1.933 b = 0.7905
13:57:40 Read 1203 rows and found 38 numeric columns
13:57:40 Using Annoy for neighbor search, n_neighbors = 128
13:57:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:57:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871fc36e7e
13:57:40 Searching Annoy index using 1 thread, search_k = 12800
13:57:41 Annoy recall = 100%
13:57:50 Commencing smooth kNN distance calibration using 1 thread
13:58:07 Initializing from normalized Laplacian + noise
13:58:07 Commencing optimization for 500 epochs, with 175322 positive edges
13:58:20 Optimization finished
[1] "128 0.01"13:58:20 UMAP embedding parameters a = 1.896 b = 0.8006
13:58:20 Read 1203 rows and found 38 numeric columns
13:58:20 Using Annoy for neighbor search, n_neighbors = 128
13:58:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:58:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e14848c
13:58:20 Searching Annoy index using 1 thread, search_k = 12800
13:58:21 Annoy recall = 100%
13:58:30 Commencing smooth kNN distance calibration using 1 thread
13:58:47 Initializing from normalized Laplacian + noise
13:58:47 Commencing optimization for 500 epochs, with 175322 positive edges
13:59:00 Optimization finished
[1] "128 0.02"13:59:00 UMAP embedding parameters a = 1.859 b = 0.8109
13:59:00 Read 1203 rows and found 38 numeric columns
13:59:00 Using Annoy for neighbor search, n_neighbors = 128
13:59:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:59:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e800bdf
13:59:00 Searching Annoy index using 1 thread, search_k = 12800
13:59:01 Annoy recall = 100%
13:59:10 Commencing smooth kNN distance calibration using 1 thread
13:59:27 Initializing from normalized Laplacian + noise
13:59:27 Commencing optimization for 500 epochs, with 175322 positive edges
13:59:40 Optimization finished
[1] "128 0.03"13:59:40 UMAP embedding parameters a = 1.822 b = 0.8212
13:59:40 Read 1203 rows and found 38 numeric columns
13:59:40 Using Annoy for neighbor search, n_neighbors = 128
13:59:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:59:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874803d86c
13:59:41 Searching Annoy index using 1 thread, search_k = 12800
13:59:42 Annoy recall = 100%
13:59:50 Commencing smooth kNN distance calibration using 1 thread
14:00:08 Initializing from normalized Laplacian + noise
14:00:08 Commencing optimization for 500 epochs, with 175322 positive edges
14:00:20 Optimization finished
[1] "128 0.04"14:00:21 UMAP embedding parameters a = 1.786 b = 0.8316
14:00:21 Read 1203 rows and found 38 numeric columns
14:00:21 Using Annoy for neighbor search, n_neighbors = 128
14:00:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:00:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877029ecd5
14:00:21 Searching Annoy index using 1 thread, search_k = 12800
14:00:22 Annoy recall = 100%
14:00:31 Commencing smooth kNN distance calibration using 1 thread
14:00:48 Initializing from normalized Laplacian + noise
14:00:48 Commencing optimization for 500 epochs, with 175322 positive edges
14:01:00 Optimization finished
[1] "128 0.05"14:01:01 UMAP embedding parameters a = 1.75 b = 0.8421
14:01:01 Read 1203 rows and found 38 numeric columns
14:01:01 Using Annoy for neighbor search, n_neighbors = 128
14:01:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:01:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fb82ea4
14:01:01 Searching Annoy index using 1 thread, search_k = 12800
14:01:02 Annoy recall = 100%
14:01:11 Commencing smooth kNN distance calibration using 1 thread
14:01:28 Initializing from normalized Laplacian + noise
14:01:28 Commencing optimization for 500 epochs, with 175322 positive edges
14:01:41 Optimization finished
[1] "128 0.06"14:01:41 UMAP embedding parameters a = 1.715 b = 0.8526
14:01:41 Read 1203 rows and found 38 numeric columns
14:01:41 Using Annoy for neighbor search, n_neighbors = 128
14:01:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:01:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875464b84b
14:01:42 Searching Annoy index using 1 thread, search_k = 12800
14:01:43 Annoy recall = 100%
14:01:51 Commencing smooth kNN distance calibration using 1 thread
14:02:08 Initializing from normalized Laplacian + noise
14:02:08 Commencing optimization for 500 epochs, with 175322 positive edges
14:02:21 Optimization finished
[1] "128 0.07"14:02:21 UMAP embedding parameters a = 1.68 b = 0.8631
14:02:21 Read 1203 rows and found 38 numeric columns
14:02:21 Using Annoy for neighbor search, n_neighbors = 128
14:02:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:02:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874714333c
14:02:22 Searching Annoy index using 1 thread, search_k = 12800
14:02:23 Annoy recall = 100%
14:02:32 Commencing smooth kNN distance calibration using 1 thread
14:02:49 Initializing from normalized Laplacian + noise
14:02:49 Commencing optimization for 500 epochs, with 175322 positive edges
14:03:01 Optimization finished
[1] "128 0.08"14:03:02 UMAP embedding parameters a = 1.645 b = 0.8737
14:03:02 Read 1203 rows and found 38 numeric columns
14:03:02 Using Annoy for neighbor search, n_neighbors = 128
14:03:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:03:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875420bbd2
14:03:02 Searching Annoy index using 1 thread, search_k = 12800
14:03:03 Annoy recall = 100%
14:03:12 Commencing smooth kNN distance calibration using 1 thread
14:03:29 Initializing from normalized Laplacian + noise
14:03:29 Commencing optimization for 500 epochs, with 175322 positive edges
14:03:42 Optimization finished
[1] "128 0.09"14:03:42 UMAP embedding parameters a = 1.611 b = 0.8844
14:03:42 Read 1203 rows and found 38 numeric columns
14:03:42 Using Annoy for neighbor search, n_neighbors = 128
14:03:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:03:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cb2a659
14:03:43 Searching Annoy index using 1 thread, search_k = 12800
14:03:44 Annoy recall = 100%
14:03:52 Commencing smooth kNN distance calibration using 1 thread
14:04:09 Initializing from normalized Laplacian + noise
14:04:09 Commencing optimization for 500 epochs, with 175322 positive edges
14:04:22 Optimization finished
[1] "128 0.1"14:04:22 UMAP embedding parameters a = 1.577 b = 0.8951
14:04:22 Read 1203 rows and found 38 numeric columns
14:04:22 Using Annoy for neighbor search, n_neighbors = 128
14:04:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:04:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876732192
14:04:23 Searching Annoy index using 1 thread, search_k = 12800
14:04:24 Annoy recall = 100%
14:04:32 Commencing smooth kNN distance calibration using 1 thread
14:04:50 Initializing from normalized Laplacian + noise
14:04:50 Commencing optimization for 500 epochs, with 175322 positive edges
14:05:02 Optimization finished
[1] "128 0.11"14:05:03 UMAP embedding parameters a = 1.544 b = 0.9058
14:05:03 Read 1203 rows and found 38 numeric columns
14:05:03 Using Annoy for neighbor search, n_neighbors = 128
14:05:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:05:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d025916
14:05:03 Searching Annoy index using 1 thread, search_k = 12800
14:05:04 Annoy recall = 100%
14:05:13 Commencing smooth kNN distance calibration using 1 thread
14:05:30 Initializing from normalized Laplacian + noise
14:05:30 Commencing optimization for 500 epochs, with 175322 positive edges
14:05:43 Optimization finished
[1] "128 0.12"14:05:43 UMAP embedding parameters a = 1.51 b = 0.9165
14:05:43 Read 1203 rows and found 38 numeric columns
14:05:43 Using Annoy for neighbor search, n_neighbors = 128
14:05:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:05:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87564a3ca
14:05:44 Searching Annoy index using 1 thread, search_k = 12800
14:05:45 Annoy recall = 100%
14:05:53 Commencing smooth kNN distance calibration using 1 thread
14:06:10 Initializing from normalized Laplacian + noise
14:06:10 Commencing optimization for 500 epochs, with 175322 positive edges
14:06:23 Optimization finished
[1] "128 0.13"14:06:23 UMAP embedding parameters a = 1.478 b = 0.9272
14:06:23 Read 1203 rows and found 38 numeric columns
14:06:23 Using Annoy for neighbor search, n_neighbors = 128
14:06:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:06:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778637e75
14:06:24 Searching Annoy index using 1 thread, search_k = 12800
14:06:25 Annoy recall = 100%
14:06:34 Commencing smooth kNN distance calibration using 1 thread
14:06:51 Initializing from normalized Laplacian + noise
14:06:51 Commencing optimization for 500 epochs, with 175322 positive edges
14:07:04 Optimization finished
[1] "128 0.14"14:07:04 UMAP embedding parameters a = 1.446 b = 0.938
14:07:04 Read 1203 rows and found 38 numeric columns
14:07:04 Using Annoy for neighbor search, n_neighbors = 128
14:07:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:07:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771328db7
14:07:04 Searching Annoy index using 1 thread, search_k = 12800
14:07:05 Annoy recall = 100%
14:07:14 Commencing smooth kNN distance calibration using 1 thread
14:07:31 Initializing from normalized Laplacian + noise
14:07:31 Commencing optimization for 500 epochs, with 175322 positive edges
14:07:44 Optimization finished
[1] "128 0.15"14:07:44 UMAP embedding parameters a = 1.414 b = 0.9488
14:07:44 Read 1203 rows and found 38 numeric columns
14:07:44 Using Annoy for neighbor search, n_neighbors = 128
14:07:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:07:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710579381
14:07:45 Searching Annoy index using 1 thread, search_k = 12800
14:07:46 Annoy recall = 100%
14:07:54 Commencing smooth kNN distance calibration using 1 thread
14:08:12 Initializing from normalized Laplacian + noise
14:08:12 Commencing optimization for 500 epochs, with 175322 positive edges
14:08:24 Optimization finished
[1] "128 0.16"14:08:25 UMAP embedding parameters a = 1.383 b = 0.9596
14:08:25 Read 1203 rows and found 38 numeric columns
14:08:25 Using Annoy for neighbor search, n_neighbors = 128
14:08:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:08:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e857591
14:08:25 Searching Annoy index using 1 thread, search_k = 12800
14:08:26 Annoy recall = 100%
14:08:35 Commencing smooth kNN distance calibration using 1 thread
14:08:52 Initializing from normalized Laplacian + noise
14:08:52 Commencing optimization for 500 epochs, with 175322 positive edges
14:09:05 Optimization finished
[1] "128 0.17"14:09:05 UMAP embedding parameters a = 1.352 b = 0.9704
14:09:05 Read 1203 rows and found 38 numeric columns
14:09:05 Using Annoy for neighbor search, n_neighbors = 128
14:09:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:09:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87268b9177
14:09:06 Searching Annoy index using 1 thread, search_k = 12800
14:09:07 Annoy recall = 100%
14:09:15 Commencing smooth kNN distance calibration using 1 thread
14:09:32 Initializing from normalized Laplacian + noise
14:09:33 Commencing optimization for 500 epochs, with 175322 positive edges
14:09:45 Optimization finished
[1] "128 0.18"14:09:45 UMAP embedding parameters a = 1.321 b = 0.9813
14:09:45 Read 1203 rows and found 38 numeric columns
14:09:46 Using Annoy for neighbor search, n_neighbors = 128
14:09:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:09:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875abc8738
14:09:46 Searching Annoy index using 1 thread, search_k = 12800
14:09:47 Annoy recall = 100%
14:09:56 Commencing smooth kNN distance calibration using 1 thread
14:10:13 Initializing from normalized Laplacian + noise
14:10:13 Commencing optimization for 500 epochs, with 175322 positive edges
14:10:26 Optimization finished
[1] "128 0.19"14:10:26 UMAP embedding parameters a = 1.292 b = 0.9921
14:10:26 Read 1203 rows and found 38 numeric columns
14:10:26 Using Annoy for neighbor search, n_neighbors = 128
14:10:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:10:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d796b9e
14:10:27 Searching Annoy index using 1 thread, search_k = 12800
14:10:27 Annoy recall = 100%
14:10:36 Commencing smooth kNN distance calibration using 1 thread
14:10:53 Initializing from normalized Laplacian + noise
14:10:54 Commencing optimization for 500 epochs, with 175322 positive edges
14:11:06 Optimization finished
[1] "128 0.2"14:11:07 UMAP embedding parameters a = 1.262 b = 1.003
14:11:07 Read 1203 rows and found 38 numeric columns
14:11:07 Using Annoy for neighbor search, n_neighbors = 128
14:11:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:11:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d84d512
14:11:07 Searching Annoy index using 1 thread, search_k = 12800
14:11:08 Annoy recall = 100%
14:11:17 Commencing smooth kNN distance calibration using 1 thread
14:11:34 Initializing from normalized Laplacian + noise
14:11:34 Commencing optimization for 500 epochs, with 175322 positive edges
14:11:47 Optimization finished
[1] "129 0"14:11:47 UMAP embedding parameters a = 1.933 b = 0.7905
14:11:47 Read 1203 rows and found 38 numeric columns
14:11:47 Using Annoy for neighbor search, n_neighbors = 129
14:11:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:11:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751db29cf
14:11:47 Searching Annoy index using 1 thread, search_k = 12900
14:11:48 Annoy recall = 100%
14:11:57 Commencing smooth kNN distance calibration using 1 thread
14:12:15 Initializing from normalized Laplacian + noise
14:12:15 Commencing optimization for 500 epochs, with 176544 positive edges
14:12:27 Optimization finished
[1] "129 0.01"14:12:27 UMAP embedding parameters a = 1.896 b = 0.8006
14:12:27 Read 1203 rows and found 38 numeric columns
14:12:27 Using Annoy for neighbor search, n_neighbors = 129
14:12:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:12:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fe6c27f
14:12:28 Searching Annoy index using 1 thread, search_k = 12900
14:12:29 Annoy recall = 100%
14:12:38 Commencing smooth kNN distance calibration using 1 thread
14:12:55 Initializing from normalized Laplacian + noise
14:12:55 Commencing optimization for 500 epochs, with 176544 positive edges
14:13:08 Optimization finished
[1] "129 0.02"14:13:08 UMAP embedding parameters a = 1.859 b = 0.8109
14:13:08 Read 1203 rows and found 38 numeric columns
14:13:08 Using Annoy for neighbor search, n_neighbors = 129
14:13:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:13:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716bfe658
14:13:09 Searching Annoy index using 1 thread, search_k = 12900
14:13:10 Annoy recall = 100%
14:13:18 Commencing smooth kNN distance calibration using 1 thread
14:13:35 Initializing from normalized Laplacian + noise
14:13:36 Commencing optimization for 500 epochs, with 176544 positive edges
14:13:48 Optimization finished
[1] "129 0.03"14:13:48 UMAP embedding parameters a = 1.822 b = 0.8212
14:13:48 Read 1203 rows and found 38 numeric columns
14:13:48 Using Annoy for neighbor search, n_neighbors = 129
14:13:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:13:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876948552d
14:13:49 Searching Annoy index using 1 thread, search_k = 12900
14:13:50 Annoy recall = 100%
14:13:59 Commencing smooth kNN distance calibration using 1 thread
14:14:16 Initializing from normalized Laplacian + noise
14:14:16 Commencing optimization for 500 epochs, with 176544 positive edges
14:14:29 Optimization finished
[1] "129 0.04"14:14:29 UMAP embedding parameters a = 1.786 b = 0.8316
14:14:29 Read 1203 rows and found 38 numeric columns
14:14:29 Using Annoy for neighbor search, n_neighbors = 129
14:14:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:14:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877254a932
14:14:30 Searching Annoy index using 1 thread, search_k = 12900
14:14:31 Annoy recall = 100%
14:14:39 Commencing smooth kNN distance calibration using 1 thread
14:14:57 Initializing from normalized Laplacian + noise
14:14:57 Commencing optimization for 500 epochs, with 176544 positive edges
14:15:09 Optimization finished
[1] "129 0.05"14:15:10 UMAP embedding parameters a = 1.75 b = 0.8421
14:15:10 Read 1203 rows and found 38 numeric columns
14:15:10 Using Annoy for neighbor search, n_neighbors = 129
14:15:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:15:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a902aa7
14:15:10 Searching Annoy index using 1 thread, search_k = 12900
14:15:11 Annoy recall = 100%
14:15:20 Commencing smooth kNN distance calibration using 1 thread
14:15:37 Initializing from normalized Laplacian + noise
14:15:37 Commencing optimization for 500 epochs, with 176544 positive edges
14:15:50 Optimization finished
[1] "129 0.06"14:15:50 UMAP embedding parameters a = 1.715 b = 0.8526
14:15:50 Read 1203 rows and found 38 numeric columns
14:15:50 Using Annoy for neighbor search, n_neighbors = 129
14:15:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:15:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721fc1a3c
14:15:51 Searching Annoy index using 1 thread, search_k = 12900
14:15:52 Annoy recall = 100%
14:16:00 Commencing smooth kNN distance calibration using 1 thread
14:16:18 Initializing from normalized Laplacian + noise
14:16:18 Commencing optimization for 500 epochs, with 176544 positive edges
14:16:31 Optimization finished
[1] "129 0.07"14:16:31 UMAP embedding parameters a = 1.68 b = 0.8631
14:16:31 Read 1203 rows and found 38 numeric columns
14:16:31 Using Annoy for neighbor search, n_neighbors = 129
14:16:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:16:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872eb52510
14:16:32 Searching Annoy index using 1 thread, search_k = 12900
14:16:33 Annoy recall = 100%
14:16:41 Commencing smooth kNN distance calibration using 1 thread
14:16:58 Initializing from normalized Laplacian + noise
14:16:59 Commencing optimization for 500 epochs, with 176544 positive edges
14:17:11 Optimization finished
[1] "129 0.08"14:17:12 UMAP embedding parameters a = 1.645 b = 0.8737
14:17:12 Read 1203 rows and found 38 numeric columns
14:17:12 Using Annoy for neighbor search, n_neighbors = 129
14:17:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:17:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772bcc2fe
14:17:12 Searching Annoy index using 1 thread, search_k = 12900
14:17:13 Annoy recall = 100%
14:17:22 Commencing smooth kNN distance calibration using 1 thread
14:17:39 Initializing from normalized Laplacian + noise
14:17:39 Commencing optimization for 500 epochs, with 176544 positive edges
14:17:52 Optimization finished
[1] "129 0.09"14:17:52 UMAP embedding parameters a = 1.611 b = 0.8844
14:17:52 Read 1203 rows and found 38 numeric columns
14:17:52 Using Annoy for neighbor search, n_neighbors = 129
14:17:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:17:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f7c647f
14:17:53 Searching Annoy index using 1 thread, search_k = 12900
14:17:54 Annoy recall = 100%
14:18:02 Commencing smooth kNN distance calibration using 1 thread
14:18:20 Initializing from normalized Laplacian + noise
14:18:20 Commencing optimization for 500 epochs, with 176544 positive edges
14:18:33 Optimization finished
[1] "129 0.1"14:18:33 UMAP embedding parameters a = 1.577 b = 0.8951
14:18:33 Read 1203 rows and found 38 numeric columns
14:18:33 Using Annoy for neighbor search, n_neighbors = 129
14:18:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:18:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e78938f
14:18:34 Searching Annoy index using 1 thread, search_k = 12900
14:18:35 Annoy recall = 100%
14:18:43 Commencing smooth kNN distance calibration using 1 thread
14:19:00 Initializing from normalized Laplacian + noise
14:19:00 Commencing optimization for 500 epochs, with 176544 positive edges
14:19:13 Optimization finished
[1] "129 0.11"14:19:13 UMAP embedding parameters a = 1.544 b = 0.9058
14:19:14 Read 1203 rows and found 38 numeric columns
14:19:14 Using Annoy for neighbor search, n_neighbors = 129
14:19:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:19:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760d1478b
14:19:14 Searching Annoy index using 1 thread, search_k = 12900
14:19:15 Annoy recall = 100%
14:19:24 Commencing smooth kNN distance calibration using 1 thread
14:19:42 Initializing from normalized Laplacian + noise
14:19:42 Commencing optimization for 500 epochs, with 176544 positive edges
14:19:59 Optimization finished
[1] "129 0.12"14:19:59 UMAP embedding parameters a = 1.51 b = 0.9165
14:19:59 Read 1203 rows and found 38 numeric columns
14:19:59 Using Annoy for neighbor search, n_neighbors = 129
14:19:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:20:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dfc705e
14:20:00 Searching Annoy index using 1 thread, search_k = 12900
14:20:01 Annoy recall = 100%
14:20:13 Commencing smooth kNN distance calibration using 1 thread
14:20:32 Initializing from normalized Laplacian + noise
14:20:33 Commencing optimization for 500 epochs, with 176544 positive edges
14:20:46 Optimization finished
[1] "129 0.13"14:20:47 UMAP embedding parameters a = 1.478 b = 0.9272
14:20:47 Read 1203 rows and found 38 numeric columns
14:20:47 Using Annoy for neighbor search, n_neighbors = 129
14:20:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:20:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87167c6bfb
14:20:47 Searching Annoy index using 1 thread, search_k = 12900
14:20:48 Annoy recall = 100%
14:20:57 Commencing smooth kNN distance calibration using 1 thread
14:21:16 Initializing from normalized Laplacian + noise
14:21:16 Commencing optimization for 500 epochs, with 176544 positive edges
14:21:29 Optimization finished
[1] "129 0.14"14:21:29 UMAP embedding parameters a = 1.446 b = 0.938
14:21:29 Read 1203 rows and found 38 numeric columns
14:21:29 Using Annoy for neighbor search, n_neighbors = 129
14:21:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:21:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750fb3460
14:21:30 Searching Annoy index using 1 thread, search_k = 12900
14:21:31 Annoy recall = 100%
14:21:40 Commencing smooth kNN distance calibration using 1 thread
14:21:58 Initializing from normalized Laplacian + noise
14:21:58 Commencing optimization for 500 epochs, with 176544 positive edges
14:22:11 Optimization finished
[1] "129 0.15"14:22:11 UMAP embedding parameters a = 1.414 b = 0.9488
14:22:11 Read 1203 rows and found 38 numeric columns
14:22:11 Using Annoy for neighbor search, n_neighbors = 129
14:22:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:22:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876db49f03
14:22:12 Searching Annoy index using 1 thread, search_k = 12900
14:22:13 Annoy recall = 100%
14:22:22 Commencing smooth kNN distance calibration using 1 thread
14:22:40 Initializing from normalized Laplacian + noise
14:22:40 Commencing optimization for 500 epochs, with 176544 positive edges
14:22:53 Optimization finished
[1] "129 0.16"14:22:53 UMAP embedding parameters a = 1.383 b = 0.9596
14:22:53 Read 1203 rows and found 38 numeric columns
14:22:53 Using Annoy for neighbor search, n_neighbors = 129
14:22:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:22:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ae12446
14:22:54 Searching Annoy index using 1 thread, search_k = 12900
14:22:55 Annoy recall = 100%
14:23:04 Commencing smooth kNN distance calibration using 1 thread
14:23:22 Initializing from normalized Laplacian + noise
14:23:22 Commencing optimization for 500 epochs, with 176544 positive edges
14:23:35 Optimization finished
[1] "129 0.17"14:23:35 UMAP embedding parameters a = 1.352 b = 0.9704
14:23:35 Read 1203 rows and found 38 numeric columns
14:23:35 Using Annoy for neighbor search, n_neighbors = 129
14:23:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:23:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87180f679c
14:23:36 Searching Annoy index using 1 thread, search_k = 12900
14:23:37 Annoy recall = 100%
14:23:46 Commencing smooth kNN distance calibration using 1 thread
14:24:04 Initializing from normalized Laplacian + noise
14:24:04 Commencing optimization for 500 epochs, with 176544 positive edges
14:24:17 Optimization finished
[1] "129 0.18"14:24:17 UMAP embedding parameters a = 1.321 b = 0.9813
14:24:17 Read 1203 rows and found 38 numeric columns
14:24:17 Using Annoy for neighbor search, n_neighbors = 129
14:24:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:24:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741d55ad5
14:24:18 Searching Annoy index using 1 thread, search_k = 12900
14:24:19 Annoy recall = 100%
14:24:28 Commencing smooth kNN distance calibration using 1 thread
14:24:46 Initializing from normalized Laplacian + noise
14:24:46 Commencing optimization for 500 epochs, with 176544 positive edges
14:24:59 Optimization finished
[1] "129 0.19"14:24:59 UMAP embedding parameters a = 1.292 b = 0.9921
14:24:59 Read 1203 rows and found 38 numeric columns
14:24:59 Using Annoy for neighbor search, n_neighbors = 129
14:24:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:25:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872793ca9f
14:25:00 Searching Annoy index using 1 thread, search_k = 12900
14:25:01 Annoy recall = 100%
14:25:09 Commencing smooth kNN distance calibration using 1 thread
14:25:27 Initializing from normalized Laplacian + noise
14:25:28 Commencing optimization for 500 epochs, with 176544 positive edges
14:25:41 Optimization finished
[1] "129 0.2"14:25:41 UMAP embedding parameters a = 1.262 b = 1.003
14:25:41 Read 1203 rows and found 38 numeric columns
14:25:41 Using Annoy for neighbor search, n_neighbors = 129
14:25:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:25:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e82892e
14:25:42 Searching Annoy index using 1 thread, search_k = 12900
14:25:42 Annoy recall = 100%
14:25:52 Commencing smooth kNN distance calibration using 1 thread
14:26:10 Initializing from normalized Laplacian + noise
14:26:11 Commencing optimization for 500 epochs, with 176544 positive edges
14:26:25 Optimization finished
[1] "130 0"14:26:25 UMAP embedding parameters a = 1.933 b = 0.7905
14:26:25 Read 1203 rows and found 38 numeric columns
14:26:25 Using Annoy for neighbor search, n_neighbors = 130
14:26:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:26:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ed7b3eb
14:26:26 Searching Annoy index using 1 thread, search_k = 13000
14:26:27 Annoy recall = 100%
14:26:39 Commencing smooth kNN distance calibration using 1 thread
14:26:59 Initializing from normalized Laplacian + noise
14:26:59 Commencing optimization for 500 epochs, with 177778 positive edges
14:27:13 Optimization finished
[1] "130 0.01"14:27:13 UMAP embedding parameters a = 1.896 b = 0.8006
14:27:13 Read 1203 rows and found 38 numeric columns
14:27:13 Using Annoy for neighbor search, n_neighbors = 130
14:27:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:27:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cf86e69
14:27:13 Searching Annoy index using 1 thread, search_k = 13000
14:27:14 Annoy recall = 100%
14:27:24 Commencing smooth kNN distance calibration using 1 thread
14:27:42 Initializing from normalized Laplacian + noise
14:27:42 Commencing optimization for 500 epochs, with 177778 positive edges
14:27:56 Optimization finished
[1] "130 0.02"14:27:56 UMAP embedding parameters a = 1.859 b = 0.8109
14:27:56 Read 1203 rows and found 38 numeric columns
14:27:56 Using Annoy for neighbor search, n_neighbors = 130
14:27:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:27:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716e607a4
14:27:57 Searching Annoy index using 1 thread, search_k = 13000
14:27:58 Annoy recall = 100%
14:28:07 Commencing smooth kNN distance calibration using 1 thread
14:28:25 Initializing from normalized Laplacian + noise
14:28:25 Commencing optimization for 500 epochs, with 177778 positive edges
14:28:38 Optimization finished
[1] "130 0.03"14:28:38 UMAP embedding parameters a = 1.822 b = 0.8212
14:28:38 Read 1203 rows and found 38 numeric columns
14:28:38 Using Annoy for neighbor search, n_neighbors = 130
14:28:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:28:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87400a41a2
14:28:39 Searching Annoy index using 1 thread, search_k = 13000
14:28:40 Annoy recall = 100%
14:28:49 Commencing smooth kNN distance calibration using 1 thread
14:29:07 Initializing from normalized Laplacian + noise
14:29:07 Commencing optimization for 500 epochs, with 177778 positive edges
14:29:22 Optimization finished
[1] "130 0.04"14:29:22 UMAP embedding parameters a = 1.786 b = 0.8316
14:29:22 Read 1203 rows and found 38 numeric columns
14:29:22 Using Annoy for neighbor search, n_neighbors = 130
14:29:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:29:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d5001ea
14:29:23 Searching Annoy index using 1 thread, search_k = 13000
14:29:24 Annoy recall = 100%
14:29:34 Commencing smooth kNN distance calibration using 1 thread
14:29:53 Initializing from normalized Laplacian + noise
14:29:53 Commencing optimization for 500 epochs, with 177778 positive edges
14:30:07 Optimization finished
[1] "130 0.05"14:30:07 UMAP embedding parameters a = 1.75 b = 0.8421
14:30:07 Read 1203 rows and found 38 numeric columns
14:30:07 Using Annoy for neighbor search, n_neighbors = 130
14:30:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:30:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87356b7d35
14:30:08 Searching Annoy index using 1 thread, search_k = 13000
14:30:09 Annoy recall = 100%
14:30:18 Commencing smooth kNN distance calibration using 1 thread
14:30:36 Initializing from normalized Laplacian + noise
14:30:36 Commencing optimization for 500 epochs, with 177778 positive edges
14:30:49 Optimization finished
[1] "130 0.06"14:30:50 UMAP embedding parameters a = 1.715 b = 0.8526
14:30:50 Read 1203 rows and found 38 numeric columns
14:30:50 Using Annoy for neighbor search, n_neighbors = 130
14:30:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:30:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876695d319
14:30:50 Searching Annoy index using 1 thread, search_k = 13000
14:30:51 Annoy recall = 100%
14:31:00 Commencing smooth kNN distance calibration using 1 thread
14:31:19 Initializing from normalized Laplacian + noise
14:31:19 Commencing optimization for 500 epochs, with 177778 positive edges
14:31:32 Optimization finished
[1] "130 0.07"14:31:32 UMAP embedding parameters a = 1.68 b = 0.8631
14:31:32 Read 1203 rows and found 38 numeric columns
14:31:32 Using Annoy for neighbor search, n_neighbors = 130
14:31:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:31:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87180c8922
14:31:33 Searching Annoy index using 1 thread, search_k = 13000
14:31:34 Annoy recall = 100%
14:31:43 Commencing smooth kNN distance calibration using 1 thread
14:32:01 Initializing from normalized Laplacian + noise
14:32:02 Commencing optimization for 500 epochs, with 177778 positive edges
14:32:15 Optimization finished
[1] "130 0.08"14:32:15 UMAP embedding parameters a = 1.645 b = 0.8737
14:32:15 Read 1203 rows and found 38 numeric columns
14:32:15 Using Annoy for neighbor search, n_neighbors = 130
14:32:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:32:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762e4e8d3
14:32:17 Searching Annoy index using 1 thread, search_k = 13000
14:32:18 Annoy recall = 100%
14:32:28 Commencing smooth kNN distance calibration using 1 thread
14:32:51 Initializing from normalized Laplacian + noise
14:32:51 Commencing optimization for 500 epochs, with 177778 positive edges
14:33:07 Optimization finished
[1] "130 0.09"14:33:07 UMAP embedding parameters a = 1.611 b = 0.8844
14:33:07 Read 1203 rows and found 38 numeric columns
14:33:07 Using Annoy for neighbor search, n_neighbors = 130
14:33:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:33:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87141aa82b
14:33:08 Searching Annoy index using 1 thread, search_k = 13000
14:33:09 Annoy recall = 100%
14:33:21 Commencing smooth kNN distance calibration using 1 thread
14:33:42 Initializing from normalized Laplacian + noise
14:33:42 Commencing optimization for 500 epochs, with 177778 positive edges
14:33:57 Optimization finished
[1] "130 0.1"14:33:58 UMAP embedding parameters a = 1.577 b = 0.8951
14:33:58 Read 1203 rows and found 38 numeric columns
14:33:58 Using Annoy for neighbor search, n_neighbors = 130
14:33:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:33:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769e7b2f1
14:33:59 Searching Annoy index using 1 thread, search_k = 13000
14:34:01 Annoy recall = 100%
14:34:11 Commencing smooth kNN distance calibration using 1 thread
14:34:30 Initializing from normalized Laplacian + noise
14:34:31 Commencing optimization for 500 epochs, with 177778 positive edges
14:34:45 Optimization finished
[1] "130 0.11"14:34:46 UMAP embedding parameters a = 1.544 b = 0.9058
14:34:46 Read 1203 rows and found 38 numeric columns
14:34:46 Using Annoy for neighbor search, n_neighbors = 130
14:34:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:34:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742cbab52
14:34:47 Searching Annoy index using 1 thread, search_k = 13000
14:34:48 Annoy recall = 100%
14:34:57 Commencing smooth kNN distance calibration using 1 thread
14:35:17 Initializing from normalized Laplacian + noise
14:35:17 Commencing optimization for 500 epochs, with 177778 positive edges
14:35:32 Optimization finished
[1] "130 0.12"14:35:32 UMAP embedding parameters a = 1.51 b = 0.9165
14:35:32 Read 1203 rows and found 38 numeric columns
14:35:32 Using Annoy for neighbor search, n_neighbors = 130
14:35:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:35:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ada8e83
14:35:33 Searching Annoy index using 1 thread, search_k = 13000
14:35:34 Annoy recall = 100%
14:35:44 Commencing smooth kNN distance calibration using 1 thread
14:36:04 Initializing from normalized Laplacian + noise
14:36:04 Commencing optimization for 500 epochs, with 177778 positive edges
14:36:18 Optimization finished
[1] "130 0.13"14:36:18 UMAP embedding parameters a = 1.478 b = 0.9272
14:36:18 Read 1203 rows and found 38 numeric columns
14:36:18 Using Annoy for neighbor search, n_neighbors = 130
14:36:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:36:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875330081e
14:36:18 Searching Annoy index using 1 thread, search_k = 13000
14:36:19 Annoy recall = 100%
14:36:29 Commencing smooth kNN distance calibration using 1 thread
14:36:47 Initializing from normalized Laplacian + noise
14:36:47 Commencing optimization for 500 epochs, with 177778 positive edges
14:37:01 Optimization finished
[1] "130 0.14"14:37:01 UMAP embedding parameters a = 1.446 b = 0.938
14:37:01 Read 1203 rows and found 38 numeric columns
14:37:01 Using Annoy for neighbor search, n_neighbors = 130
14:37:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:37:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735205484
14:37:01 Searching Annoy index using 1 thread, search_k = 13000
14:37:02 Annoy recall = 100%
14:37:12 Commencing smooth kNN distance calibration using 1 thread
14:37:30 Initializing from normalized Laplacian + noise
14:37:30 Commencing optimization for 500 epochs, with 177778 positive edges
14:37:43 Optimization finished
[1] "130 0.15"14:37:43 UMAP embedding parameters a = 1.414 b = 0.9488
14:37:43 Read 1203 rows and found 38 numeric columns
14:37:43 Using Annoy for neighbor search, n_neighbors = 130
14:37:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:37:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87556ab92b
14:37:44 Searching Annoy index using 1 thread, search_k = 13000
14:37:45 Annoy recall = 100%
14:37:54 Commencing smooth kNN distance calibration using 1 thread
14:38:12 Initializing from normalized Laplacian + noise
14:38:12 Commencing optimization for 500 epochs, with 177778 positive edges
14:38:25 Optimization finished
[1] "130 0.16"14:38:25 UMAP embedding parameters a = 1.383 b = 0.9596
14:38:25 Read 1203 rows and found 38 numeric columns
14:38:25 Using Annoy for neighbor search, n_neighbors = 130
14:38:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:38:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87752c225b
14:38:26 Searching Annoy index using 1 thread, search_k = 13000
14:38:27 Annoy recall = 100%
14:38:36 Commencing smooth kNN distance calibration using 1 thread
14:38:54 Initializing from normalized Laplacian + noise
14:38:54 Commencing optimization for 500 epochs, with 177778 positive edges
14:39:07 Optimization finished
[1] "130 0.17"14:39:08 UMAP embedding parameters a = 1.352 b = 0.9704
14:39:08 Read 1203 rows and found 38 numeric columns
14:39:08 Using Annoy for neighbor search, n_neighbors = 130
14:39:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:39:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763d57995
14:39:08 Searching Annoy index using 1 thread, search_k = 13000
14:39:09 Annoy recall = 100%
14:39:18 Commencing smooth kNN distance calibration using 1 thread
14:39:36 Initializing from normalized Laplacian + noise
14:39:36 Commencing optimization for 500 epochs, with 177778 positive edges
14:39:49 Optimization finished
[1] "130 0.18"14:39:50 UMAP embedding parameters a = 1.321 b = 0.9813
14:39:50 Read 1203 rows and found 38 numeric columns
14:39:50 Using Annoy for neighbor search, n_neighbors = 130
14:39:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:39:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748277c29
14:39:50 Searching Annoy index using 1 thread, search_k = 13000
14:39:51 Annoy recall = 100%
14:40:00 Commencing smooth kNN distance calibration using 1 thread
14:40:19 Initializing from normalized Laplacian + noise
14:40:19 Commencing optimization for 500 epochs, with 177778 positive edges
14:40:32 Optimization finished
[1] "130 0.19"14:40:32 UMAP embedding parameters a = 1.292 b = 0.9921
14:40:32 Read 1203 rows and found 38 numeric columns
14:40:32 Using Annoy for neighbor search, n_neighbors = 130
14:40:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:40:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744a886da
14:40:32 Searching Annoy index using 1 thread, search_k = 13000
14:40:33 Annoy recall = 100%
14:40:42 Commencing smooth kNN distance calibration using 1 thread
14:41:01 Initializing from normalized Laplacian + noise
14:41:01 Commencing optimization for 500 epochs, with 177778 positive edges
14:41:14 Optimization finished
[1] "130 0.2"14:41:14 UMAP embedding parameters a = 1.262 b = 1.003
14:41:14 Read 1203 rows and found 38 numeric columns
14:41:14 Using Annoy for neighbor search, n_neighbors = 130
14:41:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:41:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87324e0d24
14:41:15 Searching Annoy index using 1 thread, search_k = 13000
14:41:16 Annoy recall = 100%
14:41:25 Commencing smooth kNN distance calibration using 1 thread
14:41:43 Initializing from normalized Laplacian + noise
14:41:43 Commencing optimization for 500 epochs, with 177778 positive edges
14:41:56 Optimization finished
[1] "131 0"14:41:56 UMAP embedding parameters a = 1.933 b = 0.7905
14:41:56 Read 1203 rows and found 38 numeric columns
14:41:56 Using Annoy for neighbor search, n_neighbors = 131
14:41:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:41:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728f8c3b4
14:41:57 Searching Annoy index using 1 thread, search_k = 13100
14:41:58 Annoy recall = 100%
14:42:07 Commencing smooth kNN distance calibration using 1 thread
14:42:25 Initializing from normalized Laplacian + noise
14:42:25 Commencing optimization for 500 epochs, with 179028 positive edges
14:42:38 Optimization finished
[1] "131 0.01"14:42:39 UMAP embedding parameters a = 1.896 b = 0.8006
14:42:39 Read 1203 rows and found 38 numeric columns
14:42:39 Using Annoy for neighbor search, n_neighbors = 131
14:42:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:42:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752a4f739
14:42:39 Searching Annoy index using 1 thread, search_k = 13100
14:42:40 Annoy recall = 100%
14:42:49 Commencing smooth kNN distance calibration using 1 thread
14:43:07 Initializing from normalized Laplacian + noise
14:43:08 Commencing optimization for 500 epochs, with 179028 positive edges
14:43:21 Optimization finished
[1] "131 0.02"14:43:21 UMAP embedding parameters a = 1.859 b = 0.8109
14:43:21 Read 1203 rows and found 38 numeric columns
14:43:21 Using Annoy for neighbor search, n_neighbors = 131
14:43:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:43:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748ca791f
14:43:22 Searching Annoy index using 1 thread, search_k = 13100
14:43:23 Annoy recall = 100%
14:43:32 Commencing smooth kNN distance calibration using 1 thread
14:43:50 Initializing from normalized Laplacian + noise
14:43:50 Commencing optimization for 500 epochs, with 179028 positive edges
14:44:03 Optimization finished
[1] "131 0.03"14:44:03 UMAP embedding parameters a = 1.822 b = 0.8212
14:44:03 Read 1203 rows and found 38 numeric columns
14:44:03 Using Annoy for neighbor search, n_neighbors = 131
14:44:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:44:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779f3f814
14:44:04 Searching Annoy index using 1 thread, search_k = 13100
14:44:05 Annoy recall = 100%
14:44:14 Commencing smooth kNN distance calibration using 1 thread
14:44:32 Initializing from normalized Laplacian + noise
14:44:32 Commencing optimization for 500 epochs, with 179028 positive edges
14:44:45 Optimization finished
[1] "131 0.04"14:44:45 UMAP embedding parameters a = 1.786 b = 0.8316
14:44:45 Read 1203 rows and found 38 numeric columns
14:44:45 Using Annoy for neighbor search, n_neighbors = 131
14:44:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:44:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874059963c
14:44:46 Searching Annoy index using 1 thread, search_k = 13100
14:44:47 Annoy recall = 100%
14:44:56 Commencing smooth kNN distance calibration using 1 thread
14:45:14 Initializing from normalized Laplacian + noise
14:45:14 Commencing optimization for 500 epochs, with 179028 positive edges
14:45:28 Optimization finished
[1] "131 0.05"14:45:28 UMAP embedding parameters a = 1.75 b = 0.8421
14:45:28 Read 1203 rows and found 38 numeric columns
14:45:28 Using Annoy for neighbor search, n_neighbors = 131
14:45:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:45:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733ab9d65
14:45:28 Searching Annoy index using 1 thread, search_k = 13100
14:45:29 Annoy recall = 100%
14:45:38 Commencing smooth kNN distance calibration using 1 thread
14:45:59 Initializing from normalized Laplacian + noise
14:45:59 Commencing optimization for 500 epochs, with 179028 positive edges
14:46:14 Optimization finished
[1] "131 0.06"14:46:14 UMAP embedding parameters a = 1.715 b = 0.8526
14:46:14 Read 1203 rows and found 38 numeric columns
14:46:14 Using Annoy for neighbor search, n_neighbors = 131
14:46:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:46:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712035fb0
14:46:15 Searching Annoy index using 1 thread, search_k = 13100
14:46:16 Annoy recall = 100%
14:46:25 Commencing smooth kNN distance calibration using 1 thread
14:46:43 Initializing from normalized Laplacian + noise
14:46:44 Commencing optimization for 500 epochs, with 179028 positive edges
14:46:57 Optimization finished
[1] "131 0.07"14:46:57 UMAP embedding parameters a = 1.68 b = 0.8631
14:46:57 Read 1203 rows and found 38 numeric columns
14:46:57 Using Annoy for neighbor search, n_neighbors = 131
14:46:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:46:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722ef111
14:46:58 Searching Annoy index using 1 thread, search_k = 13100
14:46:59 Annoy recall = 100%
14:47:07 Commencing smooth kNN distance calibration using 1 thread
14:47:25 Initializing from normalized Laplacian + noise
14:47:25 Commencing optimization for 500 epochs, with 179028 positive edges
14:47:38 Optimization finished
[1] "131 0.08"14:47:39 UMAP embedding parameters a = 1.645 b = 0.8737
14:47:39 Read 1203 rows and found 38 numeric columns
14:47:39 Using Annoy for neighbor search, n_neighbors = 131
14:47:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:47:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b3f6804
14:47:39 Searching Annoy index using 1 thread, search_k = 13100
14:47:40 Annoy recall = 100%
14:47:49 Commencing smooth kNN distance calibration using 1 thread
14:48:07 Initializing from normalized Laplacian + noise
14:48:07 Commencing optimization for 500 epochs, with 179028 positive edges
14:48:19 Optimization finished
[1] "131 0.09"14:48:20 UMAP embedding parameters a = 1.611 b = 0.8844
14:48:20 Read 1203 rows and found 38 numeric columns
14:48:20 Using Annoy for neighbor search, n_neighbors = 131
14:48:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:48:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873085e8df
14:48:20 Searching Annoy index using 1 thread, search_k = 13100
14:48:21 Annoy recall = 100%
14:48:30 Commencing smooth kNN distance calibration using 1 thread
14:48:48 Initializing from normalized Laplacian + noise
14:48:48 Commencing optimization for 500 epochs, with 179028 positive edges
14:49:00 Optimization finished
[1] "131 0.1"14:49:01 UMAP embedding parameters a = 1.577 b = 0.8951
14:49:01 Read 1203 rows and found 38 numeric columns
14:49:01 Using Annoy for neighbor search, n_neighbors = 131
14:49:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:49:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875106a4fc
14:49:01 Searching Annoy index using 1 thread, search_k = 13100
14:49:02 Annoy recall = 100%
14:49:11 Commencing smooth kNN distance calibration using 1 thread
14:49:29 Initializing from normalized Laplacian + noise
14:49:29 Commencing optimization for 500 epochs, with 179028 positive edges
14:49:42 Optimization finished
[1] "131 0.11"14:49:42 UMAP embedding parameters a = 1.544 b = 0.9058
14:49:42 Read 1203 rows and found 38 numeric columns
14:49:42 Using Annoy for neighbor search, n_neighbors = 131
14:49:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:49:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87837d66d
14:49:43 Searching Annoy index using 1 thread, search_k = 13100
14:49:43 Annoy recall = 100%
14:49:52 Commencing smooth kNN distance calibration using 1 thread
14:50:10 Initializing from normalized Laplacian + noise
14:50:10 Commencing optimization for 500 epochs, with 179028 positive edges
14:50:23 Optimization finished
[1] "131 0.12"14:50:23 UMAP embedding parameters a = 1.51 b = 0.9165
14:50:23 Read 1203 rows and found 38 numeric columns
14:50:23 Using Annoy for neighbor search, n_neighbors = 131
14:50:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:50:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87476bf083
14:50:24 Searching Annoy index using 1 thread, search_k = 13100
14:50:24 Annoy recall = 100%
14:50:33 Commencing smooth kNN distance calibration using 1 thread
14:50:51 Initializing from normalized Laplacian + noise
14:50:51 Commencing optimization for 500 epochs, with 179028 positive edges
14:51:04 Optimization finished
[1] "131 0.13"14:51:04 UMAP embedding parameters a = 1.478 b = 0.9272
14:51:04 Read 1203 rows and found 38 numeric columns
14:51:04 Using Annoy for neighbor search, n_neighbors = 131
14:51:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:51:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871110e69e
14:51:05 Searching Annoy index using 1 thread, search_k = 13100
14:51:05 Annoy recall = 100%
14:51:14 Commencing smooth kNN distance calibration using 1 thread
14:51:32 Initializing from normalized Laplacian + noise
14:51:32 Commencing optimization for 500 epochs, with 179028 positive edges
14:51:45 Optimization finished
[1] "131 0.14"14:51:45 UMAP embedding parameters a = 1.446 b = 0.938
14:51:45 Read 1203 rows and found 38 numeric columns
14:51:45 Using Annoy for neighbor search, n_neighbors = 131
14:51:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:51:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874587d857
14:51:46 Searching Annoy index using 1 thread, search_k = 13100
14:51:47 Annoy recall = 100%
14:51:55 Commencing smooth kNN distance calibration using 1 thread
14:52:13 Initializing from normalized Laplacian + noise
14:52:13 Commencing optimization for 500 epochs, with 179028 positive edges
14:52:26 Optimization finished
[1] "131 0.15"14:52:26 UMAP embedding parameters a = 1.414 b = 0.9488
14:52:26 Read 1203 rows and found 38 numeric columns
14:52:26 Using Annoy for neighbor search, n_neighbors = 131
14:52:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:52:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877cd76db8
14:52:27 Searching Annoy index using 1 thread, search_k = 13100
14:52:28 Annoy recall = 100%
14:52:37 Commencing smooth kNN distance calibration using 1 thread
14:52:54 Initializing from normalized Laplacian + noise
14:52:54 Commencing optimization for 500 epochs, with 179028 positive edges
14:53:07 Optimization finished
[1] "131 0.16"14:53:07 UMAP embedding parameters a = 1.383 b = 0.9596
14:53:07 Read 1203 rows and found 38 numeric columns
14:53:07 Using Annoy for neighbor search, n_neighbors = 131
14:53:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:53:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777a6b9b7
14:53:08 Searching Annoy index using 1 thread, search_k = 13100
14:53:09 Annoy recall = 100%
14:53:18 Commencing smooth kNN distance calibration using 1 thread
14:53:35 Initializing from normalized Laplacian + noise
14:53:35 Commencing optimization for 500 epochs, with 179028 positive edges
14:53:48 Optimization finished
[1] "131 0.17"14:53:49 UMAP embedding parameters a = 1.352 b = 0.9704
14:53:49 Read 1203 rows and found 38 numeric columns
14:53:49 Using Annoy for neighbor search, n_neighbors = 131
14:53:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:53:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d94617a
14:53:49 Searching Annoy index using 1 thread, search_k = 13100
14:53:50 Annoy recall = 100%
14:53:59 Commencing smooth kNN distance calibration using 1 thread
14:54:16 Initializing from normalized Laplacian + noise
14:54:16 Commencing optimization for 500 epochs, with 179028 positive edges
14:54:29 Optimization finished
[1] "131 0.18"14:54:30 UMAP embedding parameters a = 1.321 b = 0.9813
14:54:30 Read 1203 rows and found 38 numeric columns
14:54:30 Using Annoy for neighbor search, n_neighbors = 131
14:54:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:54:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fbc568b
14:54:30 Searching Annoy index using 1 thread, search_k = 13100
14:54:31 Annoy recall = 100%
14:54:40 Commencing smooth kNN distance calibration using 1 thread
14:54:58 Initializing from normalized Laplacian + noise
14:54:58 Commencing optimization for 500 epochs, with 179028 positive edges
14:55:11 Optimization finished
[1] "131 0.19"14:55:11 UMAP embedding parameters a = 1.292 b = 0.9921
14:55:11 Read 1203 rows and found 38 numeric columns
14:55:11 Using Annoy for neighbor search, n_neighbors = 131
14:55:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:55:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87bc161e2
14:55:11 Searching Annoy index using 1 thread, search_k = 13100
14:55:12 Annoy recall = 100%
14:55:21 Commencing smooth kNN distance calibration using 1 thread
14:55:39 Initializing from normalized Laplacian + noise
14:55:39 Commencing optimization for 500 epochs, with 179028 positive edges
14:55:52 Optimization finished
[1] "131 0.2"14:55:52 UMAP embedding parameters a = 1.262 b = 1.003
14:55:52 Read 1203 rows and found 38 numeric columns
14:55:52 Using Annoy for neighbor search, n_neighbors = 131
14:55:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:55:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87477c146b
14:55:53 Searching Annoy index using 1 thread, search_k = 13100
14:55:54 Annoy recall = 100%
14:56:02 Commencing smooth kNN distance calibration using 1 thread
14:56:20 Initializing from normalized Laplacian + noise
14:56:20 Commencing optimization for 500 epochs, with 179028 positive edges
14:56:33 Optimization finished
[1] "132 0"14:56:33 UMAP embedding parameters a = 1.933 b = 0.7905
14:56:33 Read 1203 rows and found 38 numeric columns
14:56:33 Using Annoy for neighbor search, n_neighbors = 132
14:56:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:56:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87228801de
14:56:34 Searching Annoy index using 1 thread, search_k = 13200
14:56:35 Annoy recall = 100%
14:56:44 Commencing smooth kNN distance calibration using 1 thread
14:57:03 Initializing from normalized Laplacian + noise
14:57:03 Commencing optimization for 500 epochs, with 180246 positive edges
14:57:18 Optimization finished
[1] "132 0.01"14:57:18 UMAP embedding parameters a = 1.896 b = 0.8006
14:57:18 Read 1203 rows and found 38 numeric columns
14:57:18 Using Annoy for neighbor search, n_neighbors = 132
14:57:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:57:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87369bf065
14:57:19 Searching Annoy index using 1 thread, search_k = 13200
14:57:19 Annoy recall = 100%
14:57:29 Commencing smooth kNN distance calibration using 1 thread
14:57:48 Initializing from normalized Laplacian + noise
14:57:48 Commencing optimization for 500 epochs, with 180246 positive edges
14:58:02 Optimization finished
[1] "132 0.02"14:58:02 UMAP embedding parameters a = 1.859 b = 0.8109
14:58:02 Read 1203 rows and found 38 numeric columns
14:58:02 Using Annoy for neighbor search, n_neighbors = 132
14:58:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:58:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871aac1c8a
14:58:03 Searching Annoy index using 1 thread, search_k = 13200
14:58:04 Annoy recall = 100%
14:58:13 Commencing smooth kNN distance calibration using 1 thread
14:58:32 Initializing from normalized Laplacian + noise
14:58:32 Commencing optimization for 500 epochs, with 180246 positive edges
14:58:45 Optimization finished
[1] "132 0.03"14:58:45 UMAP embedding parameters a = 1.822 b = 0.8212
14:58:45 Read 1203 rows and found 38 numeric columns
14:58:45 Using Annoy for neighbor search, n_neighbors = 132
14:58:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:58:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757a85662
14:58:46 Searching Annoy index using 1 thread, search_k = 13200
14:58:47 Annoy recall = 100%
14:58:56 Commencing smooth kNN distance calibration using 1 thread
14:59:14 Initializing from normalized Laplacian + noise
14:59:14 Commencing optimization for 500 epochs, with 180246 positive edges
14:59:28 Optimization finished
[1] "132 0.04"14:59:28 UMAP embedding parameters a = 1.786 b = 0.8316
14:59:28 Read 1203 rows and found 38 numeric columns
14:59:28 Using Annoy for neighbor search, n_neighbors = 132
14:59:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:59:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c06a990
14:59:29 Searching Annoy index using 1 thread, search_k = 13200
14:59:30 Annoy recall = 100%
14:59:39 Commencing smooth kNN distance calibration using 1 thread
14:59:57 Initializing from normalized Laplacian + noise
14:59:57 Commencing optimization for 500 epochs, with 180246 positive edges
15:00:10 Optimization finished
[1] "132 0.05"15:00:10 UMAP embedding parameters a = 1.75 b = 0.8421
15:00:11 Read 1203 rows and found 38 numeric columns
15:00:11 Using Annoy for neighbor search, n_neighbors = 132
15:00:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:00:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fd83ee5
15:00:11 Searching Annoy index using 1 thread, search_k = 13200
15:00:12 Annoy recall = 100%
15:00:21 Commencing smooth kNN distance calibration using 1 thread
15:00:40 Initializing from normalized Laplacian + noise
15:00:40 Commencing optimization for 500 epochs, with 180246 positive edges
15:00:56 Optimization finished
[1] "132 0.06"15:00:56 UMAP embedding parameters a = 1.715 b = 0.8526
15:00:56 Read 1203 rows and found 38 numeric columns
15:00:56 Using Annoy for neighbor search, n_neighbors = 132
15:00:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:00:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b7dcff7
15:00:57 Searching Annoy index using 1 thread, search_k = 13200
15:00:58 Annoy recall = 100%
15:01:07 Commencing smooth kNN distance calibration using 1 thread
15:01:25 Initializing from normalized Laplacian + noise
15:01:25 Commencing optimization for 500 epochs, with 180246 positive edges
15:01:38 Optimization finished
[1] "132 0.07"15:01:38 UMAP embedding parameters a = 1.68 b = 0.8631
15:01:38 Read 1203 rows and found 38 numeric columns
15:01:38 Using Annoy for neighbor search, n_neighbors = 132
15:01:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:01:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87542e25ba
15:01:39 Searching Annoy index using 1 thread, search_k = 13200
15:01:40 Annoy recall = 100%
15:01:49 Commencing smooth kNN distance calibration using 1 thread
15:02:07 Initializing from normalized Laplacian + noise
15:02:07 Commencing optimization for 500 epochs, with 180246 positive edges
15:02:20 Optimization finished
[1] "132 0.08"15:02:20 UMAP embedding parameters a = 1.645 b = 0.8737
15:02:20 Read 1203 rows and found 38 numeric columns
15:02:20 Using Annoy for neighbor search, n_neighbors = 132
15:02:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:02:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875480c5bf
15:02:21 Searching Annoy index using 1 thread, search_k = 13200
15:02:22 Annoy recall = 100%
15:02:31 Commencing smooth kNN distance calibration using 1 thread
15:02:49 Initializing from normalized Laplacian + noise
15:02:49 Commencing optimization for 500 epochs, with 180246 positive edges
15:03:02 Optimization finished
[1] "132 0.09"15:03:02 UMAP embedding parameters a = 1.611 b = 0.8844
15:03:02 Read 1203 rows and found 38 numeric columns
15:03:02 Using Annoy for neighbor search, n_neighbors = 132
15:03:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:03:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876dcbdd1b
15:03:03 Searching Annoy index using 1 thread, search_k = 13200
15:03:03 Annoy recall = 100%
15:03:12 Commencing smooth kNN distance calibration using 1 thread
15:03:30 Initializing from normalized Laplacian + noise
15:03:30 Commencing optimization for 500 epochs, with 180246 positive edges
15:03:43 Optimization finished
[1] "132 0.1"15:03:44 UMAP embedding parameters a = 1.577 b = 0.8951
15:03:44 Read 1203 rows and found 38 numeric columns
15:03:44 Using Annoy for neighbor search, n_neighbors = 132
15:03:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:03:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d26e96e
15:03:44 Searching Annoy index using 1 thread, search_k = 13200
15:03:45 Annoy recall = 100%
15:03:54 Commencing smooth kNN distance calibration using 1 thread
15:04:12 Initializing from normalized Laplacian + noise
15:04:12 Commencing optimization for 500 epochs, with 180246 positive edges
15:04:25 Optimization finished
[1] "132 0.11"15:04:25 UMAP embedding parameters a = 1.544 b = 0.9058
15:04:25 Read 1203 rows and found 38 numeric columns
15:04:25 Using Annoy for neighbor search, n_neighbors = 132
15:04:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:04:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872725bcf8
15:04:26 Searching Annoy index using 1 thread, search_k = 13200
15:04:27 Annoy recall = 100%
15:04:37 Commencing smooth kNN distance calibration using 1 thread
15:04:57 Initializing from normalized Laplacian + noise
15:04:57 Commencing optimization for 500 epochs, with 180246 positive edges
15:05:11 Optimization finished
[1] "132 0.12"15:05:11 UMAP embedding parameters a = 1.51 b = 0.9165
15:05:11 Read 1203 rows and found 38 numeric columns
15:05:11 Using Annoy for neighbor search, n_neighbors = 132
15:05:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:05:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873696563a
15:05:12 Searching Annoy index using 1 thread, search_k = 13200
15:05:13 Annoy recall = 100%
15:05:22 Commencing smooth kNN distance calibration using 1 thread
15:05:41 Initializing from normalized Laplacian + noise
15:05:41 Commencing optimization for 500 epochs, with 180246 positive edges
15:05:54 Optimization finished
[1] "132 0.13"15:05:54 UMAP embedding parameters a = 1.478 b = 0.9272
15:05:54 Read 1203 rows and found 38 numeric columns
15:05:54 Using Annoy for neighbor search, n_neighbors = 132
15:05:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:05:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87771ae183
15:05:55 Searching Annoy index using 1 thread, search_k = 13200
15:05:55 Annoy recall = 100%
15:06:04 Commencing smooth kNN distance calibration using 1 thread
15:06:23 Initializing from normalized Laplacian + noise
15:06:23 Commencing optimization for 500 epochs, with 180246 positive edges
15:06:36 Optimization finished
[1] "132 0.14"15:06:36 UMAP embedding parameters a = 1.446 b = 0.938
15:06:36 Read 1203 rows and found 38 numeric columns
15:06:36 Using Annoy for neighbor search, n_neighbors = 132
15:06:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:06:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87677f5334
15:06:37 Searching Annoy index using 1 thread, search_k = 13200
15:06:37 Annoy recall = 100%
15:06:46 Commencing smooth kNN distance calibration using 1 thread
15:07:04 Initializing from normalized Laplacian + noise
15:07:04 Commencing optimization for 500 epochs, with 180246 positive edges
15:07:17 Optimization finished
[1] "132 0.15"15:07:18 UMAP embedding parameters a = 1.414 b = 0.9488
15:07:18 Read 1203 rows and found 38 numeric columns
15:07:18 Using Annoy for neighbor search, n_neighbors = 132
15:07:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:07:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a41f39f
15:07:18 Searching Annoy index using 1 thread, search_k = 13200
15:07:19 Annoy recall = 100%
15:07:28 Commencing smooth kNN distance calibration using 1 thread
15:07:46 Initializing from normalized Laplacian + noise
15:07:46 Commencing optimization for 500 epochs, with 180246 positive edges
15:07:59 Optimization finished
[1] "132 0.16"15:07:59 UMAP embedding parameters a = 1.383 b = 0.9596
15:07:59 Read 1203 rows and found 38 numeric columns
15:07:59 Using Annoy for neighbor search, n_neighbors = 132
15:07:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:08:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8791e4133
15:08:00 Searching Annoy index using 1 thread, search_k = 13200
15:08:01 Annoy recall = 100%
15:08:10 Commencing smooth kNN distance calibration using 1 thread
15:08:28 Initializing from normalized Laplacian + noise
15:08:28 Commencing optimization for 500 epochs, with 180246 positive edges
15:08:41 Optimization finished
[1] "132 0.17"15:08:41 UMAP embedding parameters a = 1.352 b = 0.9704
15:08:41 Read 1203 rows and found 38 numeric columns
15:08:41 Using Annoy for neighbor search, n_neighbors = 132
15:08:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:08:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769ae4445
15:08:42 Searching Annoy index using 1 thread, search_k = 13200
15:08:43 Annoy recall = 100%
15:08:52 Commencing smooth kNN distance calibration using 1 thread
15:09:18 Initializing from normalized Laplacian + noise
15:09:18 Commencing optimization for 500 epochs, with 180246 positive edges
15:09:35 Optimization finished
[1] "132 0.18"15:09:35 UMAP embedding parameters a = 1.321 b = 0.9813
15:09:35 Read 1203 rows and found 38 numeric columns
15:09:35 Using Annoy for neighbor search, n_neighbors = 132
15:09:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:09:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745815ba3
15:09:36 Searching Annoy index using 1 thread, search_k = 13200
15:09:37 Annoy recall = 100%
15:09:49 Commencing smooth kNN distance calibration using 1 thread
15:10:14 Initializing from normalized Laplacian + noise
15:10:15 Commencing optimization for 500 epochs, with 180246 positive edges
15:10:29 Optimization finished
[1] "132 0.19"15:10:29 UMAP embedding parameters a = 1.292 b = 0.9921
15:10:29 Read 1203 rows and found 38 numeric columns
15:10:29 Using Annoy for neighbor search, n_neighbors = 132
15:10:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:10:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739a42a12
15:10:30 Searching Annoy index using 1 thread, search_k = 13200
15:10:31 Annoy recall = 100%
15:10:41 Commencing smooth kNN distance calibration using 1 thread
15:11:01 Initializing from normalized Laplacian + noise
15:11:02 Commencing optimization for 500 epochs, with 180246 positive edges
15:11:16 Optimization finished
[1] "132 0.2"15:11:16 UMAP embedding parameters a = 1.262 b = 1.003
15:11:16 Read 1203 rows and found 38 numeric columns
15:11:16 Using Annoy for neighbor search, n_neighbors = 132
15:11:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:11:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ab4e941
15:11:17 Searching Annoy index using 1 thread, search_k = 13200
15:11:18 Annoy recall = 100%
15:11:28 Commencing smooth kNN distance calibration using 1 thread
15:11:49 Initializing from normalized Laplacian + noise
15:11:49 Commencing optimization for 500 epochs, with 180246 positive edges
15:12:03 Optimization finished
[1] "133 0"15:12:03 UMAP embedding parameters a = 1.933 b = 0.7905
15:12:03 Read 1203 rows and found 38 numeric columns
15:12:03 Using Annoy for neighbor search, n_neighbors = 133
15:12:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:12:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874db93210
15:12:04 Searching Annoy index using 1 thread, search_k = 13300
15:12:04 Annoy recall = 100%
15:12:14 Commencing smooth kNN distance calibration using 1 thread
15:12:32 Initializing from normalized Laplacian + noise
15:12:32 Commencing optimization for 500 epochs, with 181516 positive edges
15:12:45 Optimization finished
[1] "133 0.01"15:12:46 UMAP embedding parameters a = 1.896 b = 0.8006
15:12:46 Read 1203 rows and found 38 numeric columns
15:12:46 Using Annoy for neighbor search, n_neighbors = 133
15:12:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:12:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871101a95
15:12:46 Searching Annoy index using 1 thread, search_k = 13300
15:12:47 Annoy recall = 100%
15:12:56 Commencing smooth kNN distance calibration using 1 thread
15:13:15 Initializing from normalized Laplacian + noise
15:13:15 Commencing optimization for 500 epochs, with 181516 positive edges
15:13:28 Optimization finished
[1] "133 0.02"15:13:29 UMAP embedding parameters a = 1.859 b = 0.8109
15:13:29 Read 1203 rows and found 38 numeric columns
15:13:29 Using Annoy for neighbor search, n_neighbors = 133
15:13:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:13:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bc5cfdf
15:13:29 Searching Annoy index using 1 thread, search_k = 13300
15:13:30 Annoy recall = 100%
15:13:39 Commencing smooth kNN distance calibration using 1 thread
15:13:58 Initializing from normalized Laplacian + noise
15:13:58 Commencing optimization for 500 epochs, with 181516 positive edges
15:14:11 Optimization finished
[1] "133 0.03"15:14:11 UMAP embedding parameters a = 1.822 b = 0.8212
15:14:11 Read 1203 rows and found 38 numeric columns
15:14:11 Using Annoy for neighbor search, n_neighbors = 133
15:14:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:14:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713410a68
15:14:12 Searching Annoy index using 1 thread, search_k = 13300
15:14:13 Annoy recall = 100%
15:14:22 Commencing smooth kNN distance calibration using 1 thread
15:14:41 Initializing from normalized Laplacian + noise
15:14:41 Commencing optimization for 500 epochs, with 181516 positive edges
15:14:54 Optimization finished
[1] "133 0.04"15:14:54 UMAP embedding parameters a = 1.786 b = 0.8316
15:14:54 Read 1203 rows and found 38 numeric columns
15:14:54 Using Annoy for neighbor search, n_neighbors = 133
15:14:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:14:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877de7884d
15:14:55 Searching Annoy index using 1 thread, search_k = 13300
15:14:56 Annoy recall = 100%
15:15:05 Commencing smooth kNN distance calibration using 1 thread
15:15:23 Initializing from normalized Laplacian + noise
15:15:24 Commencing optimization for 500 epochs, with 181516 positive edges
15:15:37 Optimization finished
[1] "133 0.05"15:15:37 UMAP embedding parameters a = 1.75 b = 0.8421
15:15:37 Read 1203 rows and found 38 numeric columns
15:15:37 Using Annoy for neighbor search, n_neighbors = 133
15:15:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:15:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87436c8996
15:15:38 Searching Annoy index using 1 thread, search_k = 13300
15:15:39 Annoy recall = 100%
15:15:48 Commencing smooth kNN distance calibration using 1 thread
15:16:06 Initializing from normalized Laplacian + noise
15:16:06 Commencing optimization for 500 epochs, with 181516 positive edges
15:16:19 Optimization finished
[1] "133 0.06"15:16:20 UMAP embedding parameters a = 1.715 b = 0.8526
15:16:20 Read 1203 rows and found 38 numeric columns
15:16:20 Using Annoy for neighbor search, n_neighbors = 133
15:16:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:16:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770d56be2
15:16:20 Searching Annoy index using 1 thread, search_k = 13300
15:16:21 Annoy recall = 100%
15:16:30 Commencing smooth kNN distance calibration using 1 thread
15:16:49 Initializing from normalized Laplacian + noise
15:16:49 Commencing optimization for 500 epochs, with 181516 positive edges
15:17:02 Optimization finished
[1] "133 0.07"15:17:02 UMAP embedding parameters a = 1.68 b = 0.8631
15:17:02 Read 1203 rows and found 38 numeric columns
15:17:02 Using Annoy for neighbor search, n_neighbors = 133
15:17:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:17:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875da3ded9
15:17:03 Searching Annoy index using 1 thread, search_k = 13300
15:17:04 Annoy recall = 100%
15:17:13 Commencing smooth kNN distance calibration using 1 thread
15:17:32 Initializing from normalized Laplacian + noise
15:17:32 Commencing optimization for 500 epochs, with 181516 positive edges
15:17:45 Optimization finished
[1] "133 0.08"15:17:45 UMAP embedding parameters a = 1.645 b = 0.8737
15:17:45 Read 1203 rows and found 38 numeric columns
15:17:45 Using Annoy for neighbor search, n_neighbors = 133
15:17:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:17:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f2deb78
15:17:46 Searching Annoy index using 1 thread, search_k = 13300
15:17:47 Annoy recall = 100%
15:17:56 Commencing smooth kNN distance calibration using 1 thread
15:18:15 Initializing from normalized Laplacian + noise
15:18:15 Commencing optimization for 500 epochs, with 181516 positive edges
15:18:30 Optimization finished
[1] "133 0.09"15:18:30 UMAP embedding parameters a = 1.611 b = 0.8844
15:18:30 Read 1203 rows and found 38 numeric columns
15:18:30 Using Annoy for neighbor search, n_neighbors = 133
15:18:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:18:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873851804d
15:18:31 Searching Annoy index using 1 thread, search_k = 13300
15:18:32 Annoy recall = 100%
15:18:42 Commencing smooth kNN distance calibration using 1 thread
15:19:01 Initializing from normalized Laplacian + noise
15:19:01 Commencing optimization for 500 epochs, with 181516 positive edges
15:19:14 Optimization finished
[1] "133 0.1"15:19:15 UMAP embedding parameters a = 1.577 b = 0.8951
15:19:15 Read 1203 rows and found 38 numeric columns
15:19:15 Using Annoy for neighbor search, n_neighbors = 133
15:19:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:19:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872be0b7
15:19:15 Searching Annoy index using 1 thread, search_k = 13300
15:19:16 Annoy recall = 100%
15:19:25 Commencing smooth kNN distance calibration using 1 thread
15:19:43 Initializing from normalized Laplacian + noise
15:19:44 Commencing optimization for 500 epochs, with 181516 positive edges
15:19:57 Optimization finished
[1] "133 0.11"15:19:57 UMAP embedding parameters a = 1.544 b = 0.9058
15:19:57 Read 1203 rows and found 38 numeric columns
15:19:57 Using Annoy for neighbor search, n_neighbors = 133
15:19:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:19:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c9dbde
15:19:58 Searching Annoy index using 1 thread, search_k = 13300
15:19:59 Annoy recall = 100%
15:20:08 Commencing smooth kNN distance calibration using 1 thread
15:20:26 Initializing from normalized Laplacian + noise
15:20:26 Commencing optimization for 500 epochs, with 181516 positive edges
15:20:39 Optimization finished
[1] "133 0.12"15:20:39 UMAP embedding parameters a = 1.51 b = 0.9165
15:20:39 Read 1203 rows and found 38 numeric columns
15:20:39 Using Annoy for neighbor search, n_neighbors = 133
15:20:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:20:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752fd9cd7
15:20:40 Searching Annoy index using 1 thread, search_k = 13300
15:20:41 Annoy recall = 100%
15:20:50 Commencing smooth kNN distance calibration using 1 thread
15:21:08 Initializing from normalized Laplacian + noise
15:21:08 Commencing optimization for 500 epochs, with 181516 positive edges
15:21:21 Optimization finished
[1] "133 0.13"15:21:21 UMAP embedding parameters a = 1.478 b = 0.9272
15:21:21 Read 1203 rows and found 38 numeric columns
15:21:21 Using Annoy for neighbor search, n_neighbors = 133
15:21:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:21:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757d43719
15:21:22 Searching Annoy index using 1 thread, search_k = 13300
15:21:23 Annoy recall = 100%
15:21:32 Commencing smooth kNN distance calibration using 1 thread
15:21:50 Initializing from normalized Laplacian + noise
15:21:50 Commencing optimization for 500 epochs, with 181516 positive edges
15:22:03 Optimization finished
[1] "133 0.14"15:22:04 UMAP embedding parameters a = 1.446 b = 0.938
15:22:04 Read 1203 rows and found 38 numeric columns
15:22:04 Using Annoy for neighbor search, n_neighbors = 133
15:22:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:22:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711d0856e
15:22:04 Searching Annoy index using 1 thread, search_k = 13300
15:22:05 Annoy recall = 100%
15:22:14 Commencing smooth kNN distance calibration using 1 thread
15:22:32 Initializing from normalized Laplacian + noise
15:22:32 Commencing optimization for 500 epochs, with 181516 positive edges
15:22:45 Optimization finished
[1] "133 0.15"15:22:46 UMAP embedding parameters a = 1.414 b = 0.9488
15:22:46 Read 1203 rows and found 38 numeric columns
15:22:46 Using Annoy for neighbor search, n_neighbors = 133
15:22:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:22:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762d5dbbc
15:22:46 Searching Annoy index using 1 thread, search_k = 13300
15:22:47 Annoy recall = 100%
15:22:56 Commencing smooth kNN distance calibration using 1 thread
15:23:14 Initializing from normalized Laplacian + noise
15:23:15 Commencing optimization for 500 epochs, with 181516 positive edges
15:23:27 Optimization finished
[1] "133 0.16"15:23:28 UMAP embedding parameters a = 1.383 b = 0.9596
15:23:28 Read 1203 rows and found 38 numeric columns
15:23:28 Using Annoy for neighbor search, n_neighbors = 133
15:23:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:23:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713520711
15:23:28 Searching Annoy index using 1 thread, search_k = 13300
15:23:29 Annoy recall = 100%
15:23:38 Commencing smooth kNN distance calibration using 1 thread
15:23:56 Initializing from normalized Laplacian + noise
15:23:57 Commencing optimization for 500 epochs, with 181516 positive edges
15:24:10 Optimization finished
[1] "133 0.17"15:24:10 UMAP embedding parameters a = 1.352 b = 0.9704
15:24:10 Read 1203 rows and found 38 numeric columns
15:24:10 Using Annoy for neighbor search, n_neighbors = 133
15:24:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:24:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765feab28
15:24:11 Searching Annoy index using 1 thread, search_k = 13300
15:24:12 Annoy recall = 100%
15:24:21 Commencing smooth kNN distance calibration using 1 thread
15:24:39 Initializing from normalized Laplacian + noise
15:24:39 Commencing optimization for 500 epochs, with 181516 positive edges
15:24:52 Optimization finished
[1] "133 0.18"15:24:52 UMAP embedding parameters a = 1.321 b = 0.9813
15:24:52 Read 1203 rows and found 38 numeric columns
15:24:52 Using Annoy for neighbor search, n_neighbors = 133
15:24:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:24:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873756a17c
15:24:53 Searching Annoy index using 1 thread, search_k = 13300
15:24:54 Annoy recall = 100%
15:25:03 Commencing smooth kNN distance calibration using 1 thread
15:25:21 Initializing from normalized Laplacian + noise
15:25:21 Commencing optimization for 500 epochs, with 181516 positive edges
15:25:34 Optimization finished
[1] "133 0.19"15:25:34 UMAP embedding parameters a = 1.292 b = 0.9921
15:25:34 Read 1203 rows and found 38 numeric columns
15:25:34 Using Annoy for neighbor search, n_neighbors = 133
15:25:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:25:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711de42c
15:25:34 Searching Annoy index using 1 thread, search_k = 13300
15:25:35 Annoy recall = 100%
15:25:44 Commencing smooth kNN distance calibration using 1 thread
15:26:02 Initializing from normalized Laplacian + noise
15:26:02 Commencing optimization for 500 epochs, with 181516 positive edges
15:26:15 Optimization finished
[1] "133 0.2"15:26:16 UMAP embedding parameters a = 1.262 b = 1.003
15:26:16 Read 1203 rows and found 38 numeric columns
15:26:16 Using Annoy for neighbor search, n_neighbors = 133
15:26:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:26:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763259497
15:26:16 Searching Annoy index using 1 thread, search_k = 13300
15:26:17 Annoy recall = 100%
15:26:26 Commencing smooth kNN distance calibration using 1 thread
15:26:44 Initializing from normalized Laplacian + noise
15:26:44 Commencing optimization for 500 epochs, with 181516 positive edges
15:26:57 Optimization finished
[1] "134 0"15:26:57 UMAP embedding parameters a = 1.933 b = 0.7905
15:26:57 Read 1203 rows and found 38 numeric columns
15:26:57 Using Annoy for neighbor search, n_neighbors = 134
15:26:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:26:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e7c5e74
15:26:58 Searching Annoy index using 1 thread, search_k = 13400
15:26:59 Annoy recall = 100%
15:27:08 Commencing smooth kNN distance calibration using 1 thread
15:27:26 Initializing from normalized Laplacian + noise
15:27:26 Commencing optimization for 500 epochs, with 182782 positive edges
15:27:39 Optimization finished
[1] "134 0.01"15:27:39 UMAP embedding parameters a = 1.896 b = 0.8006
15:27:39 Read 1203 rows and found 38 numeric columns
15:27:39 Using Annoy for neighbor search, n_neighbors = 134
15:27:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:27:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737b43a67
15:27:40 Searching Annoy index using 1 thread, search_k = 13400
15:27:41 Annoy recall = 100%
15:27:50 Commencing smooth kNN distance calibration using 1 thread
15:28:08 Initializing from normalized Laplacian + noise
15:28:08 Commencing optimization for 500 epochs, with 182782 positive edges
15:28:21 Optimization finished
[1] "134 0.02"15:28:21 UMAP embedding parameters a = 1.859 b = 0.8109
15:28:21 Read 1203 rows and found 38 numeric columns
15:28:21 Using Annoy for neighbor search, n_neighbors = 134
15:28:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:28:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a40761a
15:28:22 Searching Annoy index using 1 thread, search_k = 13400
15:28:23 Annoy recall = 100%
15:28:32 Commencing smooth kNN distance calibration using 1 thread
15:28:49 Initializing from normalized Laplacian + noise
15:28:49 Commencing optimization for 500 epochs, with 182782 positive edges
15:29:02 Optimization finished
[1] "134 0.03"15:29:03 UMAP embedding parameters a = 1.822 b = 0.8212
15:29:03 Read 1203 rows and found 38 numeric columns
15:29:03 Using Annoy for neighbor search, n_neighbors = 134
15:29:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:29:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745fbb1a9
15:29:03 Searching Annoy index using 1 thread, search_k = 13400
15:29:04 Annoy recall = 100%
15:29:13 Commencing smooth kNN distance calibration using 1 thread
15:29:31 Initializing from normalized Laplacian + noise
15:29:31 Commencing optimization for 500 epochs, with 182782 positive edges
15:29:44 Optimization finished
[1] "134 0.04"15:29:45 UMAP embedding parameters a = 1.786 b = 0.8316
15:29:45 Read 1203 rows and found 38 numeric columns
15:29:45 Using Annoy for neighbor search, n_neighbors = 134
15:29:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:29:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721f62e06
15:29:45 Searching Annoy index using 1 thread, search_k = 13400
15:29:46 Annoy recall = 100%
15:29:55 Commencing smooth kNN distance calibration using 1 thread
15:30:13 Initializing from normalized Laplacian + noise
15:30:13 Commencing optimization for 500 epochs, with 182782 positive edges
15:30:26 Optimization finished
[1] "134 0.05"15:30:26 UMAP embedding parameters a = 1.75 b = 0.8421
15:30:26 Read 1203 rows and found 38 numeric columns
15:30:26 Using Annoy for neighbor search, n_neighbors = 134
15:30:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:30:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87635eb74d
15:30:27 Searching Annoy index using 1 thread, search_k = 13400
15:30:28 Annoy recall = 100%
15:30:37 Commencing smooth kNN distance calibration using 1 thread
15:30:55 Initializing from normalized Laplacian + noise
15:30:55 Commencing optimization for 500 epochs, with 182782 positive edges
15:31:08 Optimization finished
[1] "134 0.06"15:31:08 UMAP embedding parameters a = 1.715 b = 0.8526
15:31:08 Read 1203 rows and found 38 numeric columns
15:31:08 Using Annoy for neighbor search, n_neighbors = 134
15:31:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:31:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fa9f5ee
15:31:09 Searching Annoy index using 1 thread, search_k = 13400
15:31:10 Annoy recall = 100%
15:31:19 Commencing smooth kNN distance calibration using 1 thread
15:31:37 Initializing from normalized Laplacian + noise
15:31:37 Commencing optimization for 500 epochs, with 182782 positive edges
15:31:50 Optimization finished
[1] "134 0.07"15:31:50 UMAP embedding parameters a = 1.68 b = 0.8631
15:31:50 Read 1203 rows and found 38 numeric columns
15:31:50 Using Annoy for neighbor search, n_neighbors = 134
15:31:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:31:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87677789aa
15:31:51 Searching Annoy index using 1 thread, search_k = 13400
15:31:52 Annoy recall = 100%
15:32:01 Commencing smooth kNN distance calibration using 1 thread
15:32:19 Initializing from normalized Laplacian + noise
15:32:19 Commencing optimization for 500 epochs, with 182782 positive edges
15:32:32 Optimization finished
[1] "134 0.08"15:32:32 UMAP embedding parameters a = 1.645 b = 0.8737
15:32:32 Read 1203 rows and found 38 numeric columns
15:32:32 Using Annoy for neighbor search, n_neighbors = 134
15:32:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:32:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d02e160
15:32:33 Searching Annoy index using 1 thread, search_k = 13400
15:32:34 Annoy recall = 100%
15:32:43 Commencing smooth kNN distance calibration using 1 thread
15:33:00 Initializing from normalized Laplacian + noise
15:33:01 Commencing optimization for 500 epochs, with 182782 positive edges
15:33:14 Optimization finished
[1] "134 0.09"15:33:14 UMAP embedding parameters a = 1.611 b = 0.8844
15:33:14 Read 1203 rows and found 38 numeric columns
15:33:14 Using Annoy for neighbor search, n_neighbors = 134
15:33:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:33:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a5edf30
15:33:14 Searching Annoy index using 1 thread, search_k = 13400
15:33:15 Annoy recall = 100%
15:33:24 Commencing smooth kNN distance calibration using 1 thread
15:33:43 Initializing from normalized Laplacian + noise
15:33:43 Commencing optimization for 500 epochs, with 182782 positive edges
15:33:56 Optimization finished
[1] "134 0.1"15:33:56 UMAP embedding parameters a = 1.577 b = 0.8951
15:33:56 Read 1203 rows and found 38 numeric columns
15:33:56 Using Annoy for neighbor search, n_neighbors = 134
15:33:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:33:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873530bbba
15:33:57 Searching Annoy index using 1 thread, search_k = 13400
15:33:57 Annoy recall = 100%
15:34:06 Commencing smooth kNN distance calibration using 1 thread
15:34:24 Initializing from normalized Laplacian + noise
15:34:24 Commencing optimization for 500 epochs, with 182782 positive edges
15:34:38 Optimization finished
[1] "134 0.11"15:34:38 UMAP embedding parameters a = 1.544 b = 0.9058
15:34:38 Read 1203 rows and found 38 numeric columns
15:34:38 Using Annoy for neighbor search, n_neighbors = 134
15:34:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:34:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e12fbf5
15:34:38 Searching Annoy index using 1 thread, search_k = 13400
15:34:39 Annoy recall = 100%
15:34:48 Commencing smooth kNN distance calibration using 1 thread
15:35:06 Initializing from normalized Laplacian + noise
15:35:06 Commencing optimization for 500 epochs, with 182782 positive edges
15:35:19 Optimization finished
[1] "134 0.12"15:35:20 UMAP embedding parameters a = 1.51 b = 0.9165
15:35:20 Read 1203 rows and found 38 numeric columns
15:35:20 Using Annoy for neighbor search, n_neighbors = 134
15:35:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:35:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873624af0f
15:35:20 Searching Annoy index using 1 thread, search_k = 13400
15:35:21 Annoy recall = 100%
15:35:30 Commencing smooth kNN distance calibration using 1 thread
15:35:48 Initializing from normalized Laplacian + noise
15:35:48 Commencing optimization for 500 epochs, with 182782 positive edges
15:36:02 Optimization finished
[1] "134 0.13"15:36:02 UMAP embedding parameters a = 1.478 b = 0.9272
15:36:02 Read 1203 rows and found 38 numeric columns
15:36:02 Using Annoy for neighbor search, n_neighbors = 134
15:36:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:36:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874871c622
15:36:02 Searching Annoy index using 1 thread, search_k = 13400
15:36:03 Annoy recall = 100%
15:36:12 Commencing smooth kNN distance calibration using 1 thread
15:36:30 Initializing from normalized Laplacian + noise
15:36:31 Commencing optimization for 500 epochs, with 182782 positive edges
15:36:44 Optimization finished
[1] "134 0.14"15:36:44 UMAP embedding parameters a = 1.446 b = 0.938
15:36:44 Read 1203 rows and found 38 numeric columns
15:36:44 Using Annoy for neighbor search, n_neighbors = 134
15:36:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:36:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871bfa8443
15:36:45 Searching Annoy index using 1 thread, search_k = 13400
15:36:46 Annoy recall = 100%
15:36:55 Commencing smooth kNN distance calibration using 1 thread
15:37:13 Initializing from normalized Laplacian + noise
15:37:13 Commencing optimization for 500 epochs, with 182782 positive edges
15:37:26 Optimization finished
[1] "134 0.15"15:37:26 UMAP embedding parameters a = 1.414 b = 0.9488
15:37:26 Read 1203 rows and found 38 numeric columns
15:37:26 Using Annoy for neighbor search, n_neighbors = 134
15:37:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:37:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87799138a6
15:37:27 Searching Annoy index using 1 thread, search_k = 13400
15:37:28 Annoy recall = 100%
15:37:37 Commencing smooth kNN distance calibration using 1 thread
15:37:55 Initializing from normalized Laplacian + noise
15:37:55 Commencing optimization for 500 epochs, with 182782 positive edges
15:38:08 Optimization finished
[1] "134 0.16"15:38:09 UMAP embedding parameters a = 1.383 b = 0.9596
15:38:09 Read 1203 rows and found 38 numeric columns
15:38:09 Using Annoy for neighbor search, n_neighbors = 134
15:38:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:38:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739473204
15:38:09 Searching Annoy index using 1 thread, search_k = 13400
15:38:10 Annoy recall = 100%
15:38:19 Commencing smooth kNN distance calibration using 1 thread
15:38:37 Initializing from normalized Laplacian + noise
15:38:37 Commencing optimization for 500 epochs, with 182782 positive edges
15:38:51 Optimization finished
[1] "134 0.17"15:38:51 UMAP embedding parameters a = 1.352 b = 0.9704
15:38:51 Read 1203 rows and found 38 numeric columns
15:38:51 Using Annoy for neighbor search, n_neighbors = 134
15:38:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:38:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87799e631c
15:38:52 Searching Annoy index using 1 thread, search_k = 13400
15:38:52 Annoy recall = 100%
15:39:02 Commencing smooth kNN distance calibration using 1 thread
15:39:20 Initializing from normalized Laplacian + noise
15:39:20 Commencing optimization for 500 epochs, with 182782 positive edges
15:39:33 Optimization finished
[1] "134 0.18"15:39:33 UMAP embedding parameters a = 1.321 b = 0.9813
15:39:33 Read 1203 rows and found 38 numeric columns
15:39:33 Using Annoy for neighbor search, n_neighbors = 134
15:39:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:39:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748bf241e
15:39:34 Searching Annoy index using 1 thread, search_k = 13400
15:39:35 Annoy recall = 100%
15:39:44 Commencing smooth kNN distance calibration using 1 thread
15:40:02 Initializing from normalized Laplacian + noise
15:40:02 Commencing optimization for 500 epochs, with 182782 positive edges
15:40:15 Optimization finished
[1] "134 0.19"15:40:15 UMAP embedding parameters a = 1.292 b = 0.9921
15:40:16 Read 1203 rows and found 38 numeric columns
15:40:16 Using Annoy for neighbor search, n_neighbors = 134
15:40:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:40:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877198b252
15:40:16 Searching Annoy index using 1 thread, search_k = 13400
15:40:17 Annoy recall = 100%
15:40:26 Commencing smooth kNN distance calibration using 1 thread
15:40:44 Initializing from normalized Laplacian + noise
15:40:44 Commencing optimization for 500 epochs, with 182782 positive edges
15:40:58 Optimization finished
[1] "134 0.2"15:40:58 UMAP embedding parameters a = 1.262 b = 1.003
15:40:58 Read 1203 rows and found 38 numeric columns
15:40:58 Using Annoy for neighbor search, n_neighbors = 134
15:40:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:40:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779ca43d3
15:40:59 Searching Annoy index using 1 thread, search_k = 13400
15:40:59 Annoy recall = 100%
15:41:09 Commencing smooth kNN distance calibration using 1 thread
15:41:27 Initializing from normalized Laplacian + noise
15:41:27 Commencing optimization for 500 epochs, with 182782 positive edges
15:41:40 Optimization finished
[1] "135 0"15:41:40 UMAP embedding parameters a = 1.933 b = 0.7905
15:41:40 Read 1203 rows and found 38 numeric columns
15:41:40 Using Annoy for neighbor search, n_neighbors = 135
15:41:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:41:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e88fffc
15:41:41 Searching Annoy index using 1 thread, search_k = 13500
15:41:42 Annoy recall = 100%
15:41:51 Commencing smooth kNN distance calibration using 1 thread
15:42:09 Initializing from normalized Laplacian + noise
15:42:09 Commencing optimization for 500 epochs, with 184004 positive edges
15:42:22 Optimization finished
[1] "135 0.01"15:42:23 UMAP embedding parameters a = 1.896 b = 0.8006
15:42:23 Read 1203 rows and found 38 numeric columns
15:42:23 Using Annoy for neighbor search, n_neighbors = 135
15:42:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:42:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744964f29
15:42:23 Searching Annoy index using 1 thread, search_k = 13500
15:42:24 Annoy recall = 100%
15:42:33 Commencing smooth kNN distance calibration using 1 thread
15:42:51 Initializing from normalized Laplacian + noise
15:42:51 Commencing optimization for 500 epochs, with 184004 positive edges
15:43:05 Optimization finished
[1] "135 0.02"15:43:05 UMAP embedding parameters a = 1.859 b = 0.8109
15:43:05 Read 1203 rows and found 38 numeric columns
15:43:05 Using Annoy for neighbor search, n_neighbors = 135
15:43:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:43:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87519e7aec
15:43:06 Searching Annoy index using 1 thread, search_k = 13500
15:43:07 Annoy recall = 100%
15:43:16 Commencing smooth kNN distance calibration using 1 thread
15:43:34 Initializing from normalized Laplacian + noise
15:43:34 Commencing optimization for 500 epochs, with 184004 positive edges
15:43:47 Optimization finished
[1] "135 0.03"15:43:47 UMAP embedding parameters a = 1.822 b = 0.8212
15:43:47 Read 1203 rows and found 38 numeric columns
15:43:47 Using Annoy for neighbor search, n_neighbors = 135
15:43:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:43:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876059856b
15:43:48 Searching Annoy index using 1 thread, search_k = 13500
15:43:49 Annoy recall = 100%
15:43:58 Commencing smooth kNN distance calibration using 1 thread
15:44:16 Initializing from normalized Laplacian + noise
15:44:16 Commencing optimization for 500 epochs, with 184004 positive edges
15:44:29 Optimization finished
[1] "135 0.04"15:44:30 UMAP embedding parameters a = 1.786 b = 0.8316
15:44:30 Read 1203 rows and found 38 numeric columns
15:44:30 Using Annoy for neighbor search, n_neighbors = 135
15:44:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:44:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87276c2ae6
15:44:30 Searching Annoy index using 1 thread, search_k = 13500
15:44:31 Annoy recall = 100%
15:44:40 Commencing smooth kNN distance calibration using 1 thread
15:44:59 Initializing from normalized Laplacian + noise
15:44:59 Commencing optimization for 500 epochs, with 184004 positive edges
15:45:12 Optimization finished
[1] "135 0.05"15:45:12 UMAP embedding parameters a = 1.75 b = 0.8421
15:45:12 Read 1203 rows and found 38 numeric columns
15:45:12 Using Annoy for neighbor search, n_neighbors = 135
15:45:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:45:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764f081fd
15:45:13 Searching Annoy index using 1 thread, search_k = 13500
15:45:14 Annoy recall = 100%
15:45:23 Commencing smooth kNN distance calibration using 1 thread
15:45:41 Initializing from normalized Laplacian + noise
15:45:41 Commencing optimization for 500 epochs, with 184004 positive edges
15:45:54 Optimization finished
[1] "135 0.06"15:45:55 UMAP embedding parameters a = 1.715 b = 0.8526
15:45:55 Read 1203 rows and found 38 numeric columns
15:45:55 Using Annoy for neighbor search, n_neighbors = 135
15:45:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:45:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746583093
15:45:55 Searching Annoy index using 1 thread, search_k = 13500
15:45:56 Annoy recall = 100%
15:46:05 Commencing smooth kNN distance calibration using 1 thread
15:46:24 Initializing from normalized Laplacian + noise
15:46:24 Commencing optimization for 500 epochs, with 184004 positive edges
15:46:37 Optimization finished
[1] "135 0.07"15:46:37 UMAP embedding parameters a = 1.68 b = 0.8631
15:46:37 Read 1203 rows and found 38 numeric columns
15:46:37 Using Annoy for neighbor search, n_neighbors = 135
15:46:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:46:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ec2cc62
15:46:38 Searching Annoy index using 1 thread, search_k = 13500
15:46:39 Annoy recall = 100%
15:46:48 Commencing smooth kNN distance calibration using 1 thread
15:47:06 Initializing from normalized Laplacian + noise
15:47:06 Commencing optimization for 500 epochs, with 184004 positive edges
15:47:19 Optimization finished
[1] "135 0.08"15:47:20 UMAP embedding parameters a = 1.645 b = 0.8737
15:47:20 Read 1203 rows and found 38 numeric columns
15:47:20 Using Annoy for neighbor search, n_neighbors = 135
15:47:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:47:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87660e662a
15:47:20 Searching Annoy index using 1 thread, search_k = 13500
15:47:21 Annoy recall = 100%
15:47:30 Commencing smooth kNN distance calibration using 1 thread
15:47:48 Initializing from normalized Laplacian + noise
15:47:49 Commencing optimization for 500 epochs, with 184004 positive edges
15:48:02 Optimization finished
[1] "135 0.09"15:48:02 UMAP embedding parameters a = 1.611 b = 0.8844
15:48:02 Read 1203 rows and found 38 numeric columns
15:48:02 Using Annoy for neighbor search, n_neighbors = 135
15:48:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:48:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87297dc52a
15:48:03 Searching Annoy index using 1 thread, search_k = 13500
15:48:04 Annoy recall = 100%
15:48:13 Commencing smooth kNN distance calibration using 1 thread
15:48:31 Initializing from normalized Laplacian + noise
15:48:31 Commencing optimization for 500 epochs, with 184004 positive edges
15:48:44 Optimization finished
[1] "135 0.1"15:48:44 UMAP embedding parameters a = 1.577 b = 0.8951
15:48:44 Read 1203 rows and found 38 numeric columns
15:48:44 Using Annoy for neighbor search, n_neighbors = 135
15:48:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:48:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d3f2ad6
15:48:45 Searching Annoy index using 1 thread, search_k = 13500
15:48:46 Annoy recall = 100%
15:48:55 Commencing smooth kNN distance calibration using 1 thread
15:49:13 Initializing from normalized Laplacian + noise
15:49:14 Commencing optimization for 500 epochs, with 184004 positive edges
15:49:27 Optimization finished
[1] "135 0.11"15:49:27 UMAP embedding parameters a = 1.544 b = 0.9058
15:49:27 Read 1203 rows and found 38 numeric columns
15:49:27 Using Annoy for neighbor search, n_neighbors = 135
15:49:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:49:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871dc2a091
15:49:28 Searching Annoy index using 1 thread, search_k = 13500
15:49:29 Annoy recall = 100%
15:49:38 Commencing smooth kNN distance calibration using 1 thread
15:49:56 Initializing from normalized Laplacian + noise
15:49:56 Commencing optimization for 500 epochs, with 184004 positive edges
15:50:09 Optimization finished
[1] "135 0.12"15:50:09 UMAP embedding parameters a = 1.51 b = 0.9165
15:50:09 Read 1203 rows and found 38 numeric columns
15:50:09 Using Annoy for neighbor search, n_neighbors = 135
15:50:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:50:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873be3b44
15:50:10 Searching Annoy index using 1 thread, search_k = 13500
15:50:11 Annoy recall = 100%
15:50:20 Commencing smooth kNN distance calibration using 1 thread
15:50:39 Initializing from normalized Laplacian + noise
15:50:39 Commencing optimization for 500 epochs, with 184004 positive edges
15:50:52 Optimization finished
[1] "135 0.13"15:50:52 UMAP embedding parameters a = 1.478 b = 0.9272
15:50:52 Read 1203 rows and found 38 numeric columns
15:50:52 Using Annoy for neighbor search, n_neighbors = 135
15:50:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:50:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733adc7f
15:50:53 Searching Annoy index using 1 thread, search_k = 13500
15:50:54 Annoy recall = 100%
15:51:03 Commencing smooth kNN distance calibration using 1 thread
15:51:21 Initializing from normalized Laplacian + noise
15:51:21 Commencing optimization for 500 epochs, with 184004 positive edges
15:51:34 Optimization finished
[1] "135 0.14"15:51:35 UMAP embedding parameters a = 1.446 b = 0.938
15:51:35 Read 1203 rows and found 38 numeric columns
15:51:35 Using Annoy for neighbor search, n_neighbors = 135
15:51:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:51:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fb8ce97
15:51:35 Searching Annoy index using 1 thread, search_k = 13500
15:51:36 Annoy recall = 100%
15:51:45 Commencing smooth kNN distance calibration using 1 thread
15:52:04 Initializing from normalized Laplacian + noise
15:52:04 Commencing optimization for 500 epochs, with 184004 positive edges
15:52:17 Optimization finished
[1] "135 0.15"15:52:17 UMAP embedding parameters a = 1.414 b = 0.9488
15:52:17 Read 1203 rows and found 38 numeric columns
15:52:17 Using Annoy for neighbor search, n_neighbors = 135
15:52:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:52:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87671cf292
15:52:18 Searching Annoy index using 1 thread, search_k = 13500
15:52:19 Annoy recall = 100%
15:52:28 Commencing smooth kNN distance calibration using 1 thread
15:52:46 Initializing from normalized Laplacian + noise
15:52:46 Commencing optimization for 500 epochs, with 184004 positive edges
15:52:59 Optimization finished
[1] "135 0.16"15:53:00 UMAP embedding parameters a = 1.383 b = 0.9596
15:53:00 Read 1203 rows and found 38 numeric columns
15:53:00 Using Annoy for neighbor search, n_neighbors = 135
15:53:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:53:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732e4d26e
15:53:00 Searching Annoy index using 1 thread, search_k = 13500
15:53:01 Annoy recall = 100%
15:53:10 Commencing smooth kNN distance calibration using 1 thread
15:53:29 Initializing from normalized Laplacian + noise
15:53:29 Commencing optimization for 500 epochs, with 184004 positive edges
15:53:42 Optimization finished
[1] "135 0.17"15:53:42 UMAP embedding parameters a = 1.352 b = 0.9704
15:53:42 Read 1203 rows and found 38 numeric columns
15:53:42 Using Annoy for neighbor search, n_neighbors = 135
15:53:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:53:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727305841
15:53:43 Searching Annoy index using 1 thread, search_k = 13500
15:53:44 Annoy recall = 100%
15:53:53 Commencing smooth kNN distance calibration using 1 thread
15:54:11 Initializing from normalized Laplacian + noise
15:54:11 Commencing optimization for 500 epochs, with 184004 positive edges
15:54:25 Optimization finished
[1] "135 0.18"15:54:25 UMAP embedding parameters a = 1.321 b = 0.9813
15:54:25 Read 1203 rows and found 38 numeric columns
15:54:25 Using Annoy for neighbor search, n_neighbors = 135
15:54:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:54:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741fd3f2
15:54:26 Searching Annoy index using 1 thread, search_k = 13500
15:54:27 Annoy recall = 100%
15:54:36 Commencing smooth kNN distance calibration using 1 thread
15:54:54 Initializing from normalized Laplacian + noise
15:54:54 Commencing optimization for 500 epochs, with 184004 positive edges
15:55:07 Optimization finished
[1] "135 0.19"15:55:08 UMAP embedding parameters a = 1.292 b = 0.9921
15:55:08 Read 1203 rows and found 38 numeric columns
15:55:08 Using Annoy for neighbor search, n_neighbors = 135
15:55:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:55:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d43b19e
15:55:08 Searching Annoy index using 1 thread, search_k = 13500
15:55:09 Annoy recall = 100%
15:55:18 Commencing smooth kNN distance calibration using 1 thread
15:55:37 Initializing from normalized Laplacian + noise
15:55:37 Commencing optimization for 500 epochs, with 184004 positive edges
15:55:50 Optimization finished
[1] "135 0.2"15:55:50 UMAP embedding parameters a = 1.262 b = 1.003
15:55:50 Read 1203 rows and found 38 numeric columns
15:55:50 Using Annoy for neighbor search, n_neighbors = 135
15:55:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:55:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c6113fc
15:55:51 Searching Annoy index using 1 thread, search_k = 13500
15:55:52 Annoy recall = 100%
15:56:01 Commencing smooth kNN distance calibration using 1 thread
15:56:19 Initializing from normalized Laplacian + noise
15:56:19 Commencing optimization for 500 epochs, with 184004 positive edges
15:56:33 Optimization finished
[1] "136 0"15:56:33 UMAP embedding parameters a = 1.933 b = 0.7905
15:56:33 Read 1203 rows and found 38 numeric columns
15:56:33 Using Annoy for neighbor search, n_neighbors = 136
15:56:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:56:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872232cfe7
15:56:34 Searching Annoy index using 1 thread, search_k = 13600
15:56:34 Annoy recall = 100%
15:56:44 Commencing smooth kNN distance calibration using 1 thread
15:57:02 Initializing from normalized Laplacian + noise
15:57:02 Commencing optimization for 500 epochs, with 185206 positive edges
15:57:15 Optimization finished
[1] "136 0.01"15:57:15 UMAP embedding parameters a = 1.896 b = 0.8006
15:57:15 Read 1203 rows and found 38 numeric columns
15:57:16 Using Annoy for neighbor search, n_neighbors = 136
15:57:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:57:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87536860ad
15:57:16 Searching Annoy index using 1 thread, search_k = 13600
15:57:17 Annoy recall = 100%
15:57:26 Commencing smooth kNN distance calibration using 1 thread
15:57:45 Initializing from normalized Laplacian + noise
15:57:45 Commencing optimization for 500 epochs, with 185206 positive edges
15:57:58 Optimization finished
[1] "136 0.02"15:57:58 UMAP embedding parameters a = 1.859 b = 0.8109
15:57:58 Read 1203 rows and found 38 numeric columns
15:57:58 Using Annoy for neighbor search, n_neighbors = 136
15:57:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:57:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724d2da1e
15:57:59 Searching Annoy index using 1 thread, search_k = 13600
15:58:00 Annoy recall = 100%
15:58:09 Commencing smooth kNN distance calibration using 1 thread
15:58:27 Initializing from normalized Laplacian + noise
15:58:27 Commencing optimization for 500 epochs, with 185206 positive edges
15:58:41 Optimization finished
[1] "136 0.03"15:58:41 UMAP embedding parameters a = 1.822 b = 0.8212
15:58:41 Read 1203 rows and found 38 numeric columns
15:58:41 Using Annoy for neighbor search, n_neighbors = 136
15:58:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:58:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e2d542a
15:58:42 Searching Annoy index using 1 thread, search_k = 13600
15:58:43 Annoy recall = 100%
15:58:52 Commencing smooth kNN distance calibration using 1 thread
15:59:10 Initializing from normalized Laplacian + noise
15:59:10 Commencing optimization for 500 epochs, with 185206 positive edges
15:59:23 Optimization finished
[1] "136 0.04"15:59:24 UMAP embedding parameters a = 1.786 b = 0.8316
15:59:24 Read 1203 rows and found 38 numeric columns
15:59:24 Using Annoy for neighbor search, n_neighbors = 136
15:59:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:59:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874cf99953
15:59:24 Searching Annoy index using 1 thread, search_k = 13600
15:59:25 Annoy recall = 100%
15:59:34 Commencing smooth kNN distance calibration using 1 thread
15:59:53 Initializing from normalized Laplacian + noise
15:59:53 Commencing optimization for 500 epochs, with 185206 positive edges
16:00:06 Optimization finished
[1] "136 0.05"16:00:06 UMAP embedding parameters a = 1.75 b = 0.8421
16:00:06 Read 1203 rows and found 38 numeric columns
16:00:07 Using Annoy for neighbor search, n_neighbors = 136
16:00:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:00:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e1a0c23
16:00:07 Searching Annoy index using 1 thread, search_k = 13600
16:00:08 Annoy recall = 100%
16:00:17 Commencing smooth kNN distance calibration using 1 thread
16:00:35 Initializing from normalized Laplacian + noise
16:00:36 Commencing optimization for 500 epochs, with 185206 positive edges
16:00:49 Optimization finished
[1] "136 0.06"16:00:49 UMAP embedding parameters a = 1.715 b = 0.8526
16:00:49 Read 1203 rows and found 38 numeric columns
16:00:49 Using Annoy for neighbor search, n_neighbors = 136
16:00:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:00:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737cbb746
16:00:50 Searching Annoy index using 1 thread, search_k = 13600
16:00:51 Annoy recall = 100%
16:01:00 Commencing smooth kNN distance calibration using 1 thread
16:01:18 Initializing from normalized Laplacian + noise
16:01:18 Commencing optimization for 500 epochs, with 185206 positive edges
16:01:32 Optimization finished
[1] "136 0.07"16:01:32 UMAP embedding parameters a = 1.68 b = 0.8631
16:01:32 Read 1203 rows and found 38 numeric columns
16:01:32 Using Annoy for neighbor search, n_neighbors = 136
16:01:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:01:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715b8bd72
16:01:32 Searching Annoy index using 1 thread, search_k = 13600
16:01:33 Annoy recall = 100%
16:01:43 Commencing smooth kNN distance calibration using 1 thread
16:02:01 Initializing from normalized Laplacian + noise
16:02:01 Commencing optimization for 500 epochs, with 185206 positive edges
16:02:14 Optimization finished
[1] "136 0.08"16:02:15 UMAP embedding parameters a = 1.645 b = 0.8737
16:02:15 Read 1203 rows and found 38 numeric columns
16:02:15 Using Annoy for neighbor search, n_neighbors = 136
16:02:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:02:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fb2be75
16:02:15 Searching Annoy index using 1 thread, search_k = 13600
16:02:16 Annoy recall = 100%
16:02:25 Commencing smooth kNN distance calibration using 1 thread
16:02:44 Initializing from normalized Laplacian + noise
16:02:44 Commencing optimization for 500 epochs, with 185206 positive edges
16:02:57 Optimization finished
[1] "136 0.09"16:02:57 UMAP embedding parameters a = 1.611 b = 0.8844
16:02:57 Read 1203 rows and found 38 numeric columns
16:02:57 Using Annoy for neighbor search, n_neighbors = 136
16:02:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:02:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873195fb19
16:02:58 Searching Annoy index using 1 thread, search_k = 13600
16:02:59 Annoy recall = 100%
16:03:08 Commencing smooth kNN distance calibration using 1 thread
16:03:27 Initializing from normalized Laplacian + noise
16:03:27 Commencing optimization for 500 epochs, with 185206 positive edges
16:03:40 Optimization finished
[1] "136 0.1"16:03:40 UMAP embedding parameters a = 1.577 b = 0.8951
16:03:40 Read 1203 rows and found 38 numeric columns
16:03:40 Using Annoy for neighbor search, n_neighbors = 136
16:03:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:03:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876441bd6e
16:03:41 Searching Annoy index using 1 thread, search_k = 13600
16:03:42 Annoy recall = 100%
16:03:51 Commencing smooth kNN distance calibration using 1 thread
16:04:09 Initializing from normalized Laplacian + noise
16:04:09 Commencing optimization for 500 epochs, with 185206 positive edges
16:04:23 Optimization finished
[1] "136 0.11"16:04:23 UMAP embedding parameters a = 1.544 b = 0.9058
16:04:23 Read 1203 rows and found 38 numeric columns
16:04:23 Using Annoy for neighbor search, n_neighbors = 136
16:04:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:04:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714490d9e
16:04:24 Searching Annoy index using 1 thread, search_k = 13600
16:04:25 Annoy recall = 100%
16:04:34 Commencing smooth kNN distance calibration using 1 thread
16:04:52 Initializing from normalized Laplacian + noise
16:04:52 Commencing optimization for 500 epochs, with 185206 positive edges
16:05:06 Optimization finished
[1] "136 0.12"16:05:06 UMAP embedding parameters a = 1.51 b = 0.9165
16:05:06 Read 1203 rows and found 38 numeric columns
16:05:06 Using Annoy for neighbor search, n_neighbors = 136
16:05:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:05:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873347606
16:05:06 Searching Annoy index using 1 thread, search_k = 13600
16:05:07 Annoy recall = 100%
16:05:17 Commencing smooth kNN distance calibration using 1 thread
16:05:35 Initializing from normalized Laplacian + noise
16:05:35 Commencing optimization for 500 epochs, with 185206 positive edges
16:05:48 Optimization finished
[1] "136 0.13"16:05:48 UMAP embedding parameters a = 1.478 b = 0.9272
16:05:49 Read 1203 rows and found 38 numeric columns
16:05:49 Using Annoy for neighbor search, n_neighbors = 136
16:05:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:05:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87449b42d9
16:05:49 Searching Annoy index using 1 thread, search_k = 13600
16:05:50 Annoy recall = 100%
16:05:59 Commencing smooth kNN distance calibration using 1 thread
16:06:18 Initializing from normalized Laplacian + noise
16:06:18 Commencing optimization for 500 epochs, with 185206 positive edges
16:06:31 Optimization finished
[1] "136 0.14"16:06:31 UMAP embedding parameters a = 1.446 b = 0.938
16:06:32 Read 1203 rows and found 38 numeric columns
16:06:32 Using Annoy for neighbor search, n_neighbors = 136
16:06:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:06:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bb53884
16:06:32 Searching Annoy index using 1 thread, search_k = 13600
16:06:33 Annoy recall = 100%
16:06:42 Commencing smooth kNN distance calibration using 1 thread
16:07:00 Initializing from normalized Laplacian + noise
16:07:01 Commencing optimization for 500 epochs, with 185206 positive edges
16:07:14 Optimization finished
[1] "136 0.15"16:07:14 UMAP embedding parameters a = 1.414 b = 0.9488
16:07:14 Read 1203 rows and found 38 numeric columns
16:07:14 Using Annoy for neighbor search, n_neighbors = 136
16:07:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:07:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876824f803
16:07:15 Searching Annoy index using 1 thread, search_k = 13600
16:07:16 Annoy recall = 100%
16:07:25 Commencing smooth kNN distance calibration using 1 thread
16:07:43 Initializing from normalized Laplacian + noise
16:07:43 Commencing optimization for 500 epochs, with 185206 positive edges
16:07:56 Optimization finished
[1] "136 0.16"16:07:57 UMAP embedding parameters a = 1.383 b = 0.9596
16:07:57 Read 1203 rows and found 38 numeric columns
16:07:57 Using Annoy for neighbor search, n_neighbors = 136
16:07:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:07:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87af3736d
16:07:57 Searching Annoy index using 1 thread, search_k = 13600
16:07:58 Annoy recall = 100%
16:08:08 Commencing smooth kNN distance calibration using 1 thread
16:08:26 Initializing from normalized Laplacian + noise
16:08:26 Commencing optimization for 500 epochs, with 185206 positive edges
16:08:39 Optimization finished
[1] "136 0.17"16:08:40 UMAP embedding parameters a = 1.352 b = 0.9704
16:08:40 Read 1203 rows and found 38 numeric columns
16:08:40 Using Annoy for neighbor search, n_neighbors = 136
16:08:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:08:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a7804e6
16:08:40 Searching Annoy index using 1 thread, search_k = 13600
16:08:41 Annoy recall = 100%
16:08:50 Commencing smooth kNN distance calibration using 1 thread
16:09:09 Initializing from normalized Laplacian + noise
16:09:09 Commencing optimization for 500 epochs, with 185206 positive edges
16:09:22 Optimization finished
[1] "136 0.18"16:09:22 UMAP embedding parameters a = 1.321 b = 0.9813
16:09:22 Read 1203 rows and found 38 numeric columns
16:09:22 Using Annoy for neighbor search, n_neighbors = 136
16:09:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:09:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e335e2d
16:09:23 Searching Annoy index using 1 thread, search_k = 13600
16:09:24 Annoy recall = 100%
16:09:33 Commencing smooth kNN distance calibration using 1 thread
16:09:51 Initializing from normalized Laplacian + noise
16:09:51 Commencing optimization for 500 epochs, with 185206 positive edges
16:10:05 Optimization finished
[1] "136 0.19"16:10:05 UMAP embedding parameters a = 1.292 b = 0.9921
16:10:05 Read 1203 rows and found 38 numeric columns
16:10:05 Using Annoy for neighbor search, n_neighbors = 136
16:10:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:10:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734713897
16:10:05 Searching Annoy index using 1 thread, search_k = 13600
16:10:06 Annoy recall = 100%
16:10:16 Commencing smooth kNN distance calibration using 1 thread
16:10:34 Initializing from normalized Laplacian + noise
16:10:34 Commencing optimization for 500 epochs, with 185206 positive edges
16:10:47 Optimization finished
[1] "136 0.2"16:10:48 UMAP embedding parameters a = 1.262 b = 1.003
16:10:48 Read 1203 rows and found 38 numeric columns
16:10:48 Using Annoy for neighbor search, n_neighbors = 136
16:10:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:10:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757b72fbd
16:10:48 Searching Annoy index using 1 thread, search_k = 13600
16:10:49 Annoy recall = 100%
16:10:58 Commencing smooth kNN distance calibration using 1 thread
16:11:17 Initializing from normalized Laplacian + noise
16:11:17 Commencing optimization for 500 epochs, with 185206 positive edges
16:11:30 Optimization finished
[1] "137 0"16:11:30 UMAP embedding parameters a = 1.933 b = 0.7905
16:11:30 Read 1203 rows and found 38 numeric columns
16:11:30 Using Annoy for neighbor search, n_neighbors = 137
16:11:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:11:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bf5febe
16:11:31 Searching Annoy index using 1 thread, search_k = 13700
16:11:32 Annoy recall = 100%
16:11:41 Commencing smooth kNN distance calibration using 1 thread
16:11:59 Initializing from normalized Laplacian + noise
16:12:00 Commencing optimization for 500 epochs, with 186462 positive edges
16:12:13 Optimization finished
[1] "137 0.01"16:12:13 UMAP embedding parameters a = 1.896 b = 0.8006
16:12:13 Read 1203 rows and found 38 numeric columns
16:12:13 Using Annoy for neighbor search, n_neighbors = 137
16:12:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:12:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87382f73dc
16:12:14 Searching Annoy index using 1 thread, search_k = 13700
16:12:15 Annoy recall = 100%
16:12:24 Commencing smooth kNN distance calibration using 1 thread
16:12:42 Initializing from normalized Laplacian + noise
16:12:42 Commencing optimization for 500 epochs, with 186462 positive edges
16:12:56 Optimization finished
[1] "137 0.02"16:12:56 UMAP embedding parameters a = 1.859 b = 0.8109
16:12:56 Read 1203 rows and found 38 numeric columns
16:12:56 Using Annoy for neighbor search, n_neighbors = 137
16:12:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:12:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875af20c3c
16:12:57 Searching Annoy index using 1 thread, search_k = 13700
16:12:57 Annoy recall = 100%
16:13:07 Commencing smooth kNN distance calibration using 1 thread
16:13:25 Initializing from normalized Laplacian + noise
16:13:25 Commencing optimization for 500 epochs, with 186462 positive edges
16:13:38 Optimization finished
[1] "137 0.03"16:13:39 UMAP embedding parameters a = 1.822 b = 0.8212
16:13:39 Read 1203 rows and found 38 numeric columns
16:13:39 Using Annoy for neighbor search, n_neighbors = 137
16:13:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:13:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872baecd56
16:13:39 Searching Annoy index using 1 thread, search_k = 13700
16:13:40 Annoy recall = 100%
16:13:50 Commencing smooth kNN distance calibration using 1 thread
16:14:08 Initializing from normalized Laplacian + noise
16:14:08 Commencing optimization for 500 epochs, with 186462 positive edges
16:14:21 Optimization finished
[1] "137 0.04"16:14:21 UMAP embedding parameters a = 1.786 b = 0.8316
16:14:21 Read 1203 rows and found 38 numeric columns
16:14:21 Using Annoy for neighbor search, n_neighbors = 137
16:14:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:14:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f4c666e
16:14:22 Searching Annoy index using 1 thread, search_k = 13700
16:14:23 Annoy recall = 100%
16:14:32 Commencing smooth kNN distance calibration using 1 thread
16:14:51 Initializing from normalized Laplacian + noise
16:14:51 Commencing optimization for 500 epochs, with 186462 positive edges
16:15:04 Optimization finished
[1] "137 0.05"16:15:04 UMAP embedding parameters a = 1.75 b = 0.8421
16:15:04 Read 1203 rows and found 38 numeric columns
16:15:04 Using Annoy for neighbor search, n_neighbors = 137
16:15:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:15:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dd6deaa
16:15:05 Searching Annoy index using 1 thread, search_k = 13700
16:15:06 Annoy recall = 100%
16:15:15 Commencing smooth kNN distance calibration using 1 thread
16:15:34 Initializing from normalized Laplacian + noise
16:15:34 Commencing optimization for 500 epochs, with 186462 positive edges
16:15:47 Optimization finished
[1] "137 0.06"16:15:47 UMAP embedding parameters a = 1.715 b = 0.8526
16:15:47 Read 1203 rows and found 38 numeric columns
16:15:47 Using Annoy for neighbor search, n_neighbors = 137
16:15:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:15:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752df2597
16:15:48 Searching Annoy index using 1 thread, search_k = 13700
16:15:49 Annoy recall = 100%
16:15:58 Commencing smooth kNN distance calibration using 1 thread
16:16:16 Initializing from normalized Laplacian + noise
16:16:16 Commencing optimization for 500 epochs, with 186462 positive edges
16:16:30 Optimization finished
[1] "137 0.07"16:16:30 UMAP embedding parameters a = 1.68 b = 0.8631
16:16:30 Read 1203 rows and found 38 numeric columns
16:16:30 Using Annoy for neighbor search, n_neighbors = 137
16:16:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:16:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87236c3a60
16:16:31 Searching Annoy index using 1 thread, search_k = 13700
16:16:32 Annoy recall = 100%
16:16:41 Commencing smooth kNN distance calibration using 1 thread
16:16:59 Initializing from normalized Laplacian + noise
16:17:00 Commencing optimization for 500 epochs, with 186462 positive edges
16:17:13 Optimization finished
[1] "137 0.08"16:17:13 UMAP embedding parameters a = 1.645 b = 0.8737
16:17:13 Read 1203 rows and found 38 numeric columns
16:17:13 Using Annoy for neighbor search, n_neighbors = 137
16:17:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:17:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b1a9048
16:17:14 Searching Annoy index using 1 thread, search_k = 13700
16:17:15 Annoy recall = 100%
16:17:24 Commencing smooth kNN distance calibration using 1 thread
16:17:42 Initializing from normalized Laplacian + noise
16:17:42 Commencing optimization for 500 epochs, with 186462 positive edges
16:17:56 Optimization finished
[1] "137 0.09"16:17:56 UMAP embedding parameters a = 1.611 b = 0.8844
16:17:56 Read 1203 rows and found 38 numeric columns
16:17:56 Using Annoy for neighbor search, n_neighbors = 137
16:17:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:17:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f403993
16:17:57 Searching Annoy index using 1 thread, search_k = 13700
16:17:58 Annoy recall = 100%
16:18:07 Commencing smooth kNN distance calibration using 1 thread
16:18:25 Initializing from normalized Laplacian + noise
16:18:25 Commencing optimization for 500 epochs, with 186462 positive edges
16:18:38 Optimization finished
[1] "137 0.1"16:18:39 UMAP embedding parameters a = 1.577 b = 0.8951
16:18:39 Read 1203 rows and found 38 numeric columns
16:18:39 Using Annoy for neighbor search, n_neighbors = 137
16:18:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:18:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87459f0a47
16:18:39 Searching Annoy index using 1 thread, search_k = 13700
16:18:40 Annoy recall = 100%
16:18:50 Commencing smooth kNN distance calibration using 1 thread
16:19:08 Initializing from normalized Laplacian + noise
16:19:08 Commencing optimization for 500 epochs, with 186462 positive edges
16:19:21 Optimization finished
[1] "137 0.11"16:19:21 UMAP embedding parameters a = 1.544 b = 0.9058
16:19:21 Read 1203 rows and found 38 numeric columns
16:19:22 Using Annoy for neighbor search, n_neighbors = 137
16:19:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:19:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e82f0f6
16:19:22 Searching Annoy index using 1 thread, search_k = 13700
16:19:23 Annoy recall = 100%
16:19:32 Commencing smooth kNN distance calibration using 1 thread
16:19:51 Initializing from normalized Laplacian + noise
16:19:51 Commencing optimization for 500 epochs, with 186462 positive edges
16:20:04 Optimization finished
[1] "137 0.12"16:20:05 UMAP embedding parameters a = 1.51 b = 0.9165
16:20:05 Read 1203 rows and found 38 numeric columns
16:20:05 Using Annoy for neighbor search, n_neighbors = 137
16:20:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:20:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87541313b2
16:20:05 Searching Annoy index using 1 thread, search_k = 13700
16:20:06 Annoy recall = 100%
16:20:15 Commencing smooth kNN distance calibration using 1 thread
16:20:34 Initializing from normalized Laplacian + noise
16:20:34 Commencing optimization for 500 epochs, with 186462 positive edges
16:20:47 Optimization finished
[1] "137 0.13"16:20:47 UMAP embedding parameters a = 1.478 b = 0.9272
16:20:47 Read 1203 rows and found 38 numeric columns
16:20:47 Using Annoy for neighbor search, n_neighbors = 137
16:20:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:20:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cc5e72
16:20:48 Searching Annoy index using 1 thread, search_k = 13700
16:20:49 Annoy recall = 100%
16:20:58 Commencing smooth kNN distance calibration using 1 thread
16:21:17 Initializing from normalized Laplacian + noise
16:21:17 Commencing optimization for 500 epochs, with 186462 positive edges
16:21:30 Optimization finished
[1] "137 0.14"16:21:30 UMAP embedding parameters a = 1.446 b = 0.938
16:21:30 Read 1203 rows and found 38 numeric columns
16:21:30 Using Annoy for neighbor search, n_neighbors = 137
16:21:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:21:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b7c8a49
16:21:31 Searching Annoy index using 1 thread, search_k = 13700
16:21:32 Annoy recall = 100%
16:21:41 Commencing smooth kNN distance calibration using 1 thread
16:22:00 Initializing from normalized Laplacian + noise
16:22:00 Commencing optimization for 500 epochs, with 186462 positive edges
16:22:13 Optimization finished
[1] "137 0.15"16:22:13 UMAP embedding parameters a = 1.414 b = 0.9488
16:22:13 Read 1203 rows and found 38 numeric columns
16:22:13 Using Annoy for neighbor search, n_neighbors = 137
16:22:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:22:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87322d1fd5
16:22:14 Searching Annoy index using 1 thread, search_k = 13700
16:22:15 Annoy recall = 100%
16:22:24 Commencing smooth kNN distance calibration using 1 thread
16:22:42 Initializing from normalized Laplacian + noise
16:22:42 Commencing optimization for 500 epochs, with 186462 positive edges
16:22:56 Optimization finished
[1] "137 0.16"16:22:56 UMAP embedding parameters a = 1.383 b = 0.9596
16:22:56 Read 1203 rows and found 38 numeric columns
16:22:56 Using Annoy for neighbor search, n_neighbors = 137
16:22:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:22:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b9815b8
16:22:57 Searching Annoy index using 1 thread, search_k = 13700
16:22:58 Annoy recall = 100%
16:23:07 Commencing smooth kNN distance calibration using 1 thread
16:23:25 Initializing from normalized Laplacian + noise
16:23:25 Commencing optimization for 500 epochs, with 186462 positive edges
16:23:39 Optimization finished
[1] "137 0.17"16:23:39 UMAP embedding parameters a = 1.352 b = 0.9704
16:23:39 Read 1203 rows and found 38 numeric columns
16:23:39 Using Annoy for neighbor search, n_neighbors = 137
16:23:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:23:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87613547bb
16:23:40 Searching Annoy index using 1 thread, search_k = 13700
16:23:41 Annoy recall = 100%
16:23:50 Commencing smooth kNN distance calibration using 1 thread
16:24:08 Initializing from normalized Laplacian + noise
16:24:08 Commencing optimization for 500 epochs, with 186462 positive edges
16:24:22 Optimization finished
[1] "137 0.18"16:24:22 UMAP embedding parameters a = 1.321 b = 0.9813
16:24:22 Read 1203 rows and found 38 numeric columns
16:24:22 Using Annoy for neighbor search, n_neighbors = 137
16:24:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:24:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871dfde4a
16:24:23 Searching Annoy index using 1 thread, search_k = 13700
16:24:24 Annoy recall = 100%
16:24:33 Commencing smooth kNN distance calibration using 1 thread
16:24:51 Initializing from normalized Laplacian + noise
16:24:51 Commencing optimization for 500 epochs, with 186462 positive edges
16:25:05 Optimization finished
[1] "137 0.19"16:25:05 UMAP embedding parameters a = 1.292 b = 0.9921
16:25:05 Read 1203 rows and found 38 numeric columns
16:25:05 Using Annoy for neighbor search, n_neighbors = 137
16:25:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:25:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d2e10d2
16:25:06 Searching Annoy index using 1 thread, search_k = 13700
16:25:06 Annoy recall = 100%
16:25:16 Commencing smooth kNN distance calibration using 1 thread
16:25:34 Initializing from normalized Laplacian + noise
16:25:34 Commencing optimization for 500 epochs, with 186462 positive edges
16:25:48 Optimization finished
[1] "137 0.2"16:25:48 UMAP embedding parameters a = 1.262 b = 1.003
16:25:48 Read 1203 rows and found 38 numeric columns
16:25:48 Using Annoy for neighbor search, n_neighbors = 137
16:25:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:25:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874577052a
16:25:48 Searching Annoy index using 1 thread, search_k = 13700
16:25:49 Annoy recall = 100%
16:25:59 Commencing smooth kNN distance calibration using 1 thread
16:26:17 Initializing from normalized Laplacian + noise
16:26:17 Commencing optimization for 500 epochs, with 186462 positive edges
16:26:31 Optimization finished
[1] "138 0"16:26:31 UMAP embedding parameters a = 1.933 b = 0.7905
16:26:31 Read 1203 rows and found 38 numeric columns
16:26:31 Using Annoy for neighbor search, n_neighbors = 138
16:26:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:26:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871628ebe8
16:26:32 Searching Annoy index using 1 thread, search_k = 13800
16:26:33 Annoy recall = 100%
16:26:42 Commencing smooth kNN distance calibration using 1 thread
16:27:00 Initializing from normalized Laplacian + noise
16:27:00 Commencing optimization for 500 epochs, with 187698 positive edges
16:27:14 Optimization finished
[1] "138 0.01"16:27:14 UMAP embedding parameters a = 1.896 b = 0.8006
16:27:14 Read 1203 rows and found 38 numeric columns
16:27:14 Using Annoy for neighbor search, n_neighbors = 138
16:27:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:27:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87706286d8
16:27:15 Searching Annoy index using 1 thread, search_k = 13800
16:27:16 Annoy recall = 100%
16:27:25 Commencing smooth kNN distance calibration using 1 thread
16:27:43 Initializing from normalized Laplacian + noise
16:27:43 Commencing optimization for 500 epochs, with 187698 positive edges
16:27:57 Optimization finished
[1] "138 0.02"16:27:57 UMAP embedding parameters a = 1.859 b = 0.8109
16:27:57 Read 1203 rows and found 38 numeric columns
16:27:57 Using Annoy for neighbor search, n_neighbors = 138
16:27:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:27:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a124803
16:27:58 Searching Annoy index using 1 thread, search_k = 13800
16:27:58 Annoy recall = 100%
16:28:08 Commencing smooth kNN distance calibration using 1 thread
16:28:26 Initializing from normalized Laplacian + noise
16:28:27 Commencing optimization for 500 epochs, with 187698 positive edges
16:28:40 Optimization finished
[1] "138 0.03"16:28:40 UMAP embedding parameters a = 1.822 b = 0.8212
16:28:40 Read 1203 rows and found 38 numeric columns
16:28:40 Using Annoy for neighbor search, n_neighbors = 138
16:28:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:28:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751de246d
16:28:41 Searching Annoy index using 1 thread, search_k = 13800
16:28:42 Annoy recall = 100%
16:28:51 Commencing smooth kNN distance calibration using 1 thread
16:29:09 Initializing from normalized Laplacian + noise
16:29:09 Commencing optimization for 500 epochs, with 187698 positive edges
16:29:23 Optimization finished
[1] "138 0.04"16:29:23 UMAP embedding parameters a = 1.786 b = 0.8316
16:29:23 Read 1203 rows and found 38 numeric columns
16:29:23 Using Annoy for neighbor search, n_neighbors = 138
16:29:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:29:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758877edb
16:29:24 Searching Annoy index using 1 thread, search_k = 13800
16:29:25 Annoy recall = 100%
16:29:34 Commencing smooth kNN distance calibration using 1 thread
16:29:52 Initializing from normalized Laplacian + noise
16:29:53 Commencing optimization for 500 epochs, with 187698 positive edges
16:30:06 Optimization finished
[1] "138 0.05"16:30:06 UMAP embedding parameters a = 1.75 b = 0.8421
16:30:06 Read 1203 rows and found 38 numeric columns
16:30:06 Using Annoy for neighbor search, n_neighbors = 138
16:30:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:30:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871505bb70
16:30:07 Searching Annoy index using 1 thread, search_k = 13800
16:30:08 Annoy recall = 100%
16:30:17 Commencing smooth kNN distance calibration using 1 thread
16:30:36 Initializing from normalized Laplacian + noise
16:30:36 Commencing optimization for 500 epochs, with 187698 positive edges
16:30:49 Optimization finished
[1] "138 0.06"16:30:49 UMAP embedding parameters a = 1.715 b = 0.8526
16:30:49 Read 1203 rows and found 38 numeric columns
16:30:49 Using Annoy for neighbor search, n_neighbors = 138
16:30:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:30:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c562953
16:30:50 Searching Annoy index using 1 thread, search_k = 13800
16:30:51 Annoy recall = 100%
16:31:00 Commencing smooth kNN distance calibration using 1 thread
16:31:19 Initializing from normalized Laplacian + noise
16:31:19 Commencing optimization for 500 epochs, with 187698 positive edges
16:31:32 Optimization finished
[1] "138 0.07"16:31:32 UMAP embedding parameters a = 1.68 b = 0.8631
16:31:32 Read 1203 rows and found 38 numeric columns
16:31:32 Using Annoy for neighbor search, n_neighbors = 138
16:31:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:31:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726badd09
16:31:33 Searching Annoy index using 1 thread, search_k = 13800
16:31:34 Annoy recall = 100%
16:31:43 Commencing smooth kNN distance calibration using 1 thread
16:32:02 Initializing from normalized Laplacian + noise
16:32:02 Commencing optimization for 500 epochs, with 187698 positive edges
16:32:15 Optimization finished
[1] "138 0.08"16:32:15 UMAP embedding parameters a = 1.645 b = 0.8737
16:32:15 Read 1203 rows and found 38 numeric columns
16:32:15 Using Annoy for neighbor search, n_neighbors = 138
16:32:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:32:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874976f408
16:32:16 Searching Annoy index using 1 thread, search_k = 13800
16:32:17 Annoy recall = 100%
16:32:26 Commencing smooth kNN distance calibration using 1 thread
16:32:45 Initializing from normalized Laplacian + noise
16:32:45 Commencing optimization for 500 epochs, with 187698 positive edges
16:32:58 Optimization finished
[1] "138 0.09"16:32:59 UMAP embedding parameters a = 1.611 b = 0.8844
16:32:59 Read 1203 rows and found 38 numeric columns
16:32:59 Using Annoy for neighbor search, n_neighbors = 138
16:32:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:32:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87440d5910
16:32:59 Searching Annoy index using 1 thread, search_k = 13800
16:33:00 Annoy recall = 100%
16:33:09 Commencing smooth kNN distance calibration using 1 thread
16:33:28 Initializing from normalized Laplacian + noise
16:33:28 Commencing optimization for 500 epochs, with 187698 positive edges
16:33:41 Optimization finished
[1] "138 0.1"16:33:42 UMAP embedding parameters a = 1.577 b = 0.8951
16:33:42 Read 1203 rows and found 38 numeric columns
16:33:42 Using Annoy for neighbor search, n_neighbors = 138
16:33:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:33:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712b0dbc7
16:33:42 Searching Annoy index using 1 thread, search_k = 13800
16:33:43 Annoy recall = 100%
16:33:53 Commencing smooth kNN distance calibration using 1 thread
16:34:11 Initializing from normalized Laplacian + noise
16:34:11 Commencing optimization for 500 epochs, with 187698 positive edges
16:34:25 Optimization finished
[1] "138 0.11"16:34:25 UMAP embedding parameters a = 1.544 b = 0.9058
16:34:25 Read 1203 rows and found 38 numeric columns
16:34:25 Using Annoy for neighbor search, n_neighbors = 138
16:34:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:34:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a667e4
16:34:25 Searching Annoy index using 1 thread, search_k = 13800
16:34:26 Annoy recall = 100%
16:34:36 Commencing smooth kNN distance calibration using 1 thread
16:34:54 Initializing from normalized Laplacian + noise
16:34:54 Commencing optimization for 500 epochs, with 187698 positive edges
16:35:08 Optimization finished
[1] "138 0.12"16:35:08 UMAP embedding parameters a = 1.51 b = 0.9165
16:35:08 Read 1203 rows and found 38 numeric columns
16:35:08 Using Annoy for neighbor search, n_neighbors = 138
16:35:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:35:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871eff654d
16:35:09 Searching Annoy index using 1 thread, search_k = 13800
16:35:10 Annoy recall = 100%
16:35:19 Commencing smooth kNN distance calibration using 1 thread
16:35:37 Initializing from normalized Laplacian + noise
16:35:38 Commencing optimization for 500 epochs, with 187698 positive edges
16:35:51 Optimization finished
[1] "138 0.13"16:35:51 UMAP embedding parameters a = 1.478 b = 0.9272
16:35:51 Read 1203 rows and found 38 numeric columns
16:35:51 Using Annoy for neighbor search, n_neighbors = 138
16:35:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:35:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e5fa91d
16:35:52 Searching Annoy index using 1 thread, search_k = 13800
16:35:53 Annoy recall = 100%
16:36:02 Commencing smooth kNN distance calibration using 1 thread
16:36:21 Initializing from normalized Laplacian + noise
16:36:21 Commencing optimization for 500 epochs, with 187698 positive edges
16:36:34 Optimization finished
[1] "138 0.14"16:36:34 UMAP embedding parameters a = 1.446 b = 0.938
16:36:34 Read 1203 rows and found 38 numeric columns
16:36:34 Using Annoy for neighbor search, n_neighbors = 138
16:36:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:36:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720f2ce52
16:36:35 Searching Annoy index using 1 thread, search_k = 13800
16:36:36 Annoy recall = 100%
16:36:45 Commencing smooth kNN distance calibration using 1 thread
16:37:04 Initializing from normalized Laplacian + noise
16:37:04 Commencing optimization for 500 epochs, with 187698 positive edges
16:37:17 Optimization finished
[1] "138 0.15"16:37:18 UMAP embedding parameters a = 1.414 b = 0.9488
16:37:18 Read 1203 rows and found 38 numeric columns
16:37:18 Using Annoy for neighbor search, n_neighbors = 138
16:37:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:37:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cd643f7
16:37:18 Searching Annoy index using 1 thread, search_k = 13800
16:37:19 Annoy recall = 100%
16:37:28 Commencing smooth kNN distance calibration using 1 thread
16:37:47 Initializing from normalized Laplacian + noise
16:37:47 Commencing optimization for 500 epochs, with 187698 positive edges
16:38:01 Optimization finished
[1] "138 0.16"16:38:01 UMAP embedding parameters a = 1.383 b = 0.9596
16:38:01 Read 1203 rows and found 38 numeric columns
16:38:01 Using Annoy for neighbor search, n_neighbors = 138
16:38:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:38:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87113eceb5
16:38:01 Searching Annoy index using 1 thread, search_k = 13800
16:38:02 Annoy recall = 100%
16:38:12 Commencing smooth kNN distance calibration using 1 thread
16:38:30 Initializing from normalized Laplacian + noise
16:38:30 Commencing optimization for 500 epochs, with 187698 positive edges
16:38:44 Optimization finished
[1] "138 0.17"16:38:44 UMAP embedding parameters a = 1.352 b = 0.9704
16:38:44 Read 1203 rows and found 38 numeric columns
16:38:44 Using Annoy for neighbor search, n_neighbors = 138
16:38:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:38:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87445f08b2
16:38:45 Searching Annoy index using 1 thread, search_k = 13800
16:38:46 Annoy recall = 100%
16:38:55 Commencing smooth kNN distance calibration using 1 thread
16:39:14 Initializing from normalized Laplacian + noise
16:39:14 Commencing optimization for 500 epochs, with 187698 positive edges
16:39:27 Optimization finished
[1] "138 0.18"16:39:27 UMAP embedding parameters a = 1.321 b = 0.9813
16:39:27 Read 1203 rows and found 38 numeric columns
16:39:27 Using Annoy for neighbor search, n_neighbors = 138
16:39:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:39:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757f0d440
16:39:28 Searching Annoy index using 1 thread, search_k = 13800
16:39:29 Annoy recall = 100%
16:39:38 Commencing smooth kNN distance calibration using 1 thread
16:39:57 Initializing from normalized Laplacian + noise
16:39:57 Commencing optimization for 500 epochs, with 187698 positive edges
16:40:10 Optimization finished
[1] "138 0.19"16:40:11 UMAP embedding parameters a = 1.292 b = 0.9921
16:40:11 Read 1203 rows and found 38 numeric columns
16:40:11 Using Annoy for neighbor search, n_neighbors = 138
16:40:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:40:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87407f0848
16:40:11 Searching Annoy index using 1 thread, search_k = 13800
16:40:12 Annoy recall = 100%
16:40:22 Commencing smooth kNN distance calibration using 1 thread
16:40:40 Initializing from normalized Laplacian + noise
16:40:40 Commencing optimization for 500 epochs, with 187698 positive edges
16:40:53 Optimization finished
[1] "138 0.2"16:40:54 UMAP embedding parameters a = 1.262 b = 1.003
16:40:54 Read 1203 rows and found 38 numeric columns
16:40:54 Using Annoy for neighbor search, n_neighbors = 138
16:40:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:40:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879fe12f9
16:40:54 Searching Annoy index using 1 thread, search_k = 13800
16:40:55 Annoy recall = 100%
16:41:05 Commencing smooth kNN distance calibration using 1 thread
16:41:23 Initializing from normalized Laplacian + noise
16:41:24 Commencing optimization for 500 epochs, with 187698 positive edges
16:41:37 Optimization finished
[1] "139 0"16:41:37 UMAP embedding parameters a = 1.933 b = 0.7905
16:41:37 Read 1203 rows and found 38 numeric columns
16:41:37 Using Annoy for neighbor search, n_neighbors = 139
16:41:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:41:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875673c536
16:41:38 Searching Annoy index using 1 thread, search_k = 13900
16:41:39 Annoy recall = 100%
16:41:48 Commencing smooth kNN distance calibration using 1 thread
16:42:07 Initializing from normalized Laplacian + noise
16:42:07 Commencing optimization for 500 epochs, with 188918 positive edges
16:42:20 Optimization finished
[1] "139 0.01"16:42:20 UMAP embedding parameters a = 1.896 b = 0.8006
16:42:20 Read 1203 rows and found 38 numeric columns
16:42:20 Using Annoy for neighbor search, n_neighbors = 139
16:42:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:42:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714921bfa
16:42:21 Searching Annoy index using 1 thread, search_k = 13900
16:42:22 Annoy recall = 100%
16:42:31 Commencing smooth kNN distance calibration using 1 thread
16:42:50 Initializing from normalized Laplacian + noise
16:42:50 Commencing optimization for 500 epochs, with 188918 positive edges
16:43:04 Optimization finished
[1] "139 0.02"16:43:04 UMAP embedding parameters a = 1.859 b = 0.8109
16:43:04 Read 1203 rows and found 38 numeric columns
16:43:04 Using Annoy for neighbor search, n_neighbors = 139
16:43:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:43:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dca716b
16:43:05 Searching Annoy index using 1 thread, search_k = 13900
16:43:06 Annoy recall = 100%
16:43:15 Commencing smooth kNN distance calibration using 1 thread
16:43:34 Initializing from normalized Laplacian + noise
16:43:34 Commencing optimization for 500 epochs, with 188918 positive edges
16:43:47 Optimization finished
[1] "139 0.03"16:43:48 UMAP embedding parameters a = 1.822 b = 0.8212
16:43:48 Read 1203 rows and found 38 numeric columns
16:43:48 Using Annoy for neighbor search, n_neighbors = 139
16:43:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:43:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721f04f7f
16:43:48 Searching Annoy index using 1 thread, search_k = 13900
16:43:49 Annoy recall = 100%
16:43:58 Commencing smooth kNN distance calibration using 1 thread
16:44:17 Initializing from normalized Laplacian + noise
16:44:17 Commencing optimization for 500 epochs, with 188918 positive edges
16:44:31 Optimization finished
[1] "139 0.04"16:44:31 UMAP embedding parameters a = 1.786 b = 0.8316
16:44:31 Read 1203 rows and found 38 numeric columns
16:44:31 Using Annoy for neighbor search, n_neighbors = 139
16:44:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:44:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746bf3bcf
16:44:32 Searching Annoy index using 1 thread, search_k = 13900
16:44:33 Annoy recall = 100%
16:44:42 Commencing smooth kNN distance calibration using 1 thread
16:45:01 Initializing from normalized Laplacian + noise
16:45:01 Commencing optimization for 500 epochs, with 188918 positive edges
16:45:14 Optimization finished
[1] "139 0.05"16:45:15 UMAP embedding parameters a = 1.75 b = 0.8421
16:45:15 Read 1203 rows and found 38 numeric columns
16:45:15 Using Annoy for neighbor search, n_neighbors = 139
16:45:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:45:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749628724
16:45:15 Searching Annoy index using 1 thread, search_k = 13900
16:45:16 Annoy recall = 100%
16:45:26 Commencing smooth kNN distance calibration using 1 thread
16:45:45 Initializing from normalized Laplacian + noise
16:45:45 Commencing optimization for 500 epochs, with 188918 positive edges
16:45:58 Optimization finished
[1] "139 0.06"16:45:58 UMAP embedding parameters a = 1.715 b = 0.8526
16:45:58 Read 1203 rows and found 38 numeric columns
16:45:58 Using Annoy for neighbor search, n_neighbors = 139
16:45:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:45:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87325973b
16:45:59 Searching Annoy index using 1 thread, search_k = 13900
16:46:00 Annoy recall = 100%
16:46:09 Commencing smooth kNN distance calibration using 1 thread
16:46:28 Initializing from normalized Laplacian + noise
16:46:28 Commencing optimization for 500 epochs, with 188918 positive edges
16:46:42 Optimization finished
[1] "139 0.07"16:46:42 UMAP embedding parameters a = 1.68 b = 0.8631
16:46:42 Read 1203 rows and found 38 numeric columns
16:46:42 Using Annoy for neighbor search, n_neighbors = 139
16:46:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:46:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87489f1a19
16:46:43 Searching Annoy index using 1 thread, search_k = 13900
16:46:44 Annoy recall = 100%
16:46:53 Commencing smooth kNN distance calibration using 1 thread
16:47:12 Initializing from normalized Laplacian + noise
16:47:12 Commencing optimization for 500 epochs, with 188918 positive edges
16:47:25 Optimization finished
[1] "139 0.08"16:47:26 UMAP embedding parameters a = 1.645 b = 0.8737
16:47:26 Read 1203 rows and found 38 numeric columns
16:47:26 Using Annoy for neighbor search, n_neighbors = 139
16:47:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:47:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87369097f6
16:47:26 Searching Annoy index using 1 thread, search_k = 13900
16:47:27 Annoy recall = 100%
16:47:37 Commencing smooth kNN distance calibration using 1 thread
16:47:56 Initializing from normalized Laplacian + noise
16:47:56 Commencing optimization for 500 epochs, with 188918 positive edges
16:48:09 Optimization finished
[1] "139 0.09"16:48:09 UMAP embedding parameters a = 1.611 b = 0.8844
16:48:09 Read 1203 rows and found 38 numeric columns
16:48:09 Using Annoy for neighbor search, n_neighbors = 139
16:48:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:48:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87489c9c65
16:48:10 Searching Annoy index using 1 thread, search_k = 13900
16:48:11 Annoy recall = 100%
16:48:20 Commencing smooth kNN distance calibration using 1 thread
16:48:39 Initializing from normalized Laplacian + noise
16:48:39 Commencing optimization for 500 epochs, with 188918 positive edges
16:48:53 Optimization finished
[1] "139 0.1"16:48:53 UMAP embedding parameters a = 1.577 b = 0.8951
16:48:53 Read 1203 rows and found 38 numeric columns
16:48:53 Using Annoy for neighbor search, n_neighbors = 139
16:48:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:48:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ec80602
16:48:54 Searching Annoy index using 1 thread, search_k = 13900
16:48:55 Annoy recall = 100%
16:49:04 Commencing smooth kNN distance calibration using 1 thread
16:49:23 Initializing from normalized Laplacian + noise
16:49:23 Commencing optimization for 500 epochs, with 188918 positive edges
16:49:36 Optimization finished
[1] "139 0.11"16:49:37 UMAP embedding parameters a = 1.544 b = 0.9058
16:49:37 Read 1203 rows and found 38 numeric columns
16:49:37 Using Annoy for neighbor search, n_neighbors = 139
16:49:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:49:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726f31ece
16:49:37 Searching Annoy index using 1 thread, search_k = 13900
16:49:38 Annoy recall = 100%
16:49:48 Commencing smooth kNN distance calibration using 1 thread
16:50:07 Initializing from normalized Laplacian + noise
16:50:07 Commencing optimization for 500 epochs, with 188918 positive edges
16:50:20 Optimization finished
[1] "139 0.12"16:50:20 UMAP embedding parameters a = 1.51 b = 0.9165
16:50:20 Read 1203 rows and found 38 numeric columns
16:50:20 Using Annoy for neighbor search, n_neighbors = 139
16:50:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:50:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752aee468
16:50:21 Searching Annoy index using 1 thread, search_k = 13900
16:50:22 Annoy recall = 100%
16:50:31 Commencing smooth kNN distance calibration using 1 thread
16:50:50 Initializing from normalized Laplacian + noise
16:50:50 Commencing optimization for 500 epochs, with 188918 positive edges
16:51:04 Optimization finished
[1] "139 0.13"16:51:04 UMAP embedding parameters a = 1.478 b = 0.9272
16:51:04 Read 1203 rows and found 38 numeric columns
16:51:04 Using Annoy for neighbor search, n_neighbors = 139
16:51:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:51:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730a62a6f
16:51:05 Searching Annoy index using 1 thread, search_k = 13900
16:51:06 Annoy recall = 100%
16:51:15 Commencing smooth kNN distance calibration using 1 thread
16:51:34 Initializing from normalized Laplacian + noise
16:51:34 Commencing optimization for 500 epochs, with 188918 positive edges
16:51:48 Optimization finished
[1] "139 0.14"16:51:48 UMAP embedding parameters a = 1.446 b = 0.938
16:51:48 Read 1203 rows and found 38 numeric columns
16:51:48 Using Annoy for neighbor search, n_neighbors = 139
16:51:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:51:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f7a9da9
16:51:49 Searching Annoy index using 1 thread, search_k = 13900
16:51:50 Annoy recall = 100%
16:51:59 Commencing smooth kNN distance calibration using 1 thread
16:52:18 Initializing from normalized Laplacian + noise
16:52:18 Commencing optimization for 500 epochs, with 188918 positive edges
16:52:31 Optimization finished
[1] "139 0.15"16:52:32 UMAP embedding parameters a = 1.414 b = 0.9488
16:52:32 Read 1203 rows and found 38 numeric columns
16:52:32 Using Annoy for neighbor search, n_neighbors = 139
16:52:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:52:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767b49fd9
16:52:32 Searching Annoy index using 1 thread, search_k = 13900
16:52:33 Annoy recall = 100%
16:52:43 Commencing smooth kNN distance calibration using 1 thread
16:53:02 Initializing from normalized Laplacian + noise
16:53:02 Commencing optimization for 500 epochs, with 188918 positive edges
16:53:15 Optimization finished
[1] "139 0.16"16:53:15 UMAP embedding parameters a = 1.383 b = 0.9596
16:53:15 Read 1203 rows and found 38 numeric columns
16:53:15 Using Annoy for neighbor search, n_neighbors = 139
16:53:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:53:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871cfc53c2
16:53:16 Searching Annoy index using 1 thread, search_k = 13900
16:53:17 Annoy recall = 100%
16:53:26 Commencing smooth kNN distance calibration using 1 thread
16:53:45 Initializing from normalized Laplacian + noise
16:53:45 Commencing optimization for 500 epochs, with 188918 positive edges
16:53:59 Optimization finished
[1] "139 0.17"16:53:59 UMAP embedding parameters a = 1.352 b = 0.9704
16:53:59 Read 1203 rows and found 38 numeric columns
16:53:59 Using Annoy for neighbor search, n_neighbors = 139
16:53:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:54:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726357ab2
16:54:00 Searching Annoy index using 1 thread, search_k = 13900
16:54:01 Annoy recall = 100%
16:54:10 Commencing smooth kNN distance calibration using 1 thread
16:54:29 Initializing from normalized Laplacian + noise
16:54:29 Commencing optimization for 500 epochs, with 188918 positive edges
16:54:43 Optimization finished
[1] "139 0.18"16:54:43 UMAP embedding parameters a = 1.321 b = 0.9813
16:54:43 Read 1203 rows and found 38 numeric columns
16:54:43 Using Annoy for neighbor search, n_neighbors = 139
16:54:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:54:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87312b93e1
16:54:44 Searching Annoy index using 1 thread, search_k = 13900
16:54:45 Annoy recall = 100%
16:54:54 Commencing smooth kNN distance calibration using 1 thread
16:55:13 Initializing from normalized Laplacian + noise
16:55:13 Commencing optimization for 500 epochs, with 188918 positive edges
16:55:26 Optimization finished
[1] "139 0.19"16:55:27 UMAP embedding parameters a = 1.292 b = 0.9921
16:55:27 Read 1203 rows and found 38 numeric columns
16:55:27 Using Annoy for neighbor search, n_neighbors = 139
16:55:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:55:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876109acd3
16:55:27 Searching Annoy index using 1 thread, search_k = 13900
16:55:28 Annoy recall = 100%
16:55:38 Commencing smooth kNN distance calibration using 1 thread
16:55:57 Initializing from normalized Laplacian + noise
16:55:57 Commencing optimization for 500 epochs, with 188918 positive edges
16:56:10 Optimization finished
[1] "139 0.2"16:56:11 UMAP embedding parameters a = 1.262 b = 1.003
16:56:11 Read 1203 rows and found 38 numeric columns
16:56:11 Using Annoy for neighbor search, n_neighbors = 139
16:56:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:56:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738e6567a
16:56:11 Searching Annoy index using 1 thread, search_k = 13900
16:56:12 Annoy recall = 100%
16:56:22 Commencing smooth kNN distance calibration using 1 thread
16:56:40 Initializing from normalized Laplacian + noise
16:56:41 Commencing optimization for 500 epochs, with 188918 positive edges
16:56:54 Optimization finished
[1] "140 0"16:56:54 UMAP embedding parameters a = 1.933 b = 0.7905
16:56:54 Read 1203 rows and found 38 numeric columns
16:56:54 Using Annoy for neighbor search, n_neighbors = 140
16:56:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:56:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732d1fbc5
16:56:55 Searching Annoy index using 1 thread, search_k = 14000
16:56:56 Annoy recall = 100%
16:57:05 Commencing smooth kNN distance calibration using 1 thread
16:57:24 Initializing from normalized Laplacian + noise
16:57:25 Commencing optimization for 500 epochs, with 190148 positive edges
16:57:38 Optimization finished
[1] "140 0.01"16:57:38 UMAP embedding parameters a = 1.896 b = 0.8006
16:57:38 Read 1203 rows and found 38 numeric columns
16:57:38 Using Annoy for neighbor search, n_neighbors = 140
16:57:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:57:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8791220
16:57:39 Searching Annoy index using 1 thread, search_k = 14000
16:57:40 Annoy recall = 100%
16:57:49 Commencing smooth kNN distance calibration using 1 thread
16:58:08 Initializing from normalized Laplacian + noise
16:58:08 Commencing optimization for 500 epochs, with 190148 positive edges
16:58:22 Optimization finished
[1] "140 0.02"16:58:22 UMAP embedding parameters a = 1.859 b = 0.8109
16:58:22 Read 1203 rows and found 38 numeric columns
16:58:22 Using Annoy for neighbor search, n_neighbors = 140
16:58:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:58:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877745ff97
16:58:23 Searching Annoy index using 1 thread, search_k = 14000
16:58:24 Annoy recall = 100%
16:58:33 Commencing smooth kNN distance calibration using 1 thread
16:58:52 Initializing from normalized Laplacian + noise
16:58:52 Commencing optimization for 500 epochs, with 190148 positive edges
16:59:06 Optimization finished
[1] "140 0.03"16:59:06 UMAP embedding parameters a = 1.822 b = 0.8212
16:59:06 Read 1203 rows and found 38 numeric columns
16:59:06 Using Annoy for neighbor search, n_neighbors = 140
16:59:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:59:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753c4ca17
16:59:07 Searching Annoy index using 1 thread, search_k = 14000
16:59:08 Annoy recall = 100%
16:59:17 Commencing smooth kNN distance calibration using 1 thread
16:59:36 Initializing from normalized Laplacian + noise
16:59:36 Commencing optimization for 500 epochs, with 190148 positive edges
16:59:50 Optimization finished
[1] "140 0.04"16:59:50 UMAP embedding parameters a = 1.786 b = 0.8316
16:59:50 Read 1203 rows and found 38 numeric columns
16:59:50 Using Annoy for neighbor search, n_neighbors = 140
16:59:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:59:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cdf5617
16:59:51 Searching Annoy index using 1 thread, search_k = 14000
16:59:51 Annoy recall = 100%
17:00:01 Commencing smooth kNN distance calibration using 1 thread
17:00:20 Initializing from normalized Laplacian + noise
17:00:20 Commencing optimization for 500 epochs, with 190148 positive edges
17:00:34 Optimization finished
[1] "140 0.05"17:00:34 UMAP embedding parameters a = 1.75 b = 0.8421
17:00:34 Read 1203 rows and found 38 numeric columns
17:00:34 Using Annoy for neighbor search, n_neighbors = 140
17:00:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:00:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87884ce4c
17:00:35 Searching Annoy index using 1 thread, search_k = 14000
17:00:36 Annoy recall = 100%
17:00:45 Commencing smooth kNN distance calibration using 1 thread
17:01:04 Initializing from normalized Laplacian + noise
17:01:04 Commencing optimization for 500 epochs, with 190148 positive edges
17:01:17 Optimization finished
[1] "140 0.06"17:01:18 UMAP embedding parameters a = 1.715 b = 0.8526
17:01:18 Read 1203 rows and found 38 numeric columns
17:01:18 Using Annoy for neighbor search, n_neighbors = 140
17:01:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:01:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871823d2c9
17:01:18 Searching Annoy index using 1 thread, search_k = 14000
17:01:19 Annoy recall = 100%
17:01:29 Commencing smooth kNN distance calibration using 1 thread
17:01:48 Initializing from normalized Laplacian + noise
17:01:48 Commencing optimization for 500 epochs, with 190148 positive edges
17:02:01 Optimization finished
[1] "140 0.07"17:02:02 UMAP embedding parameters a = 1.68 b = 0.8631
17:02:02 Read 1203 rows and found 38 numeric columns
17:02:02 Using Annoy for neighbor search, n_neighbors = 140
17:02:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:02:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d02a57
17:02:02 Searching Annoy index using 1 thread, search_k = 14000
17:02:03 Annoy recall = 100%
17:02:13 Commencing smooth kNN distance calibration using 1 thread
17:02:32 Initializing from normalized Laplacian + noise
17:02:32 Commencing optimization for 500 epochs, with 190148 positive edges
17:02:45 Optimization finished
[1] "140 0.08"17:02:46 UMAP embedding parameters a = 1.645 b = 0.8737
17:02:46 Read 1203 rows and found 38 numeric columns
17:02:46 Using Annoy for neighbor search, n_neighbors = 140
17:02:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:02:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874903d695
17:02:46 Searching Annoy index using 1 thread, search_k = 14000
17:02:47 Annoy recall = 100%
17:02:57 Commencing smooth kNN distance calibration using 1 thread
17:03:16 Initializing from normalized Laplacian + noise
17:03:16 Commencing optimization for 500 epochs, with 190148 positive edges
17:03:29 Optimization finished
[1] "140 0.09"17:03:30 UMAP embedding parameters a = 1.611 b = 0.8844
17:03:30 Read 1203 rows and found 38 numeric columns
17:03:30 Using Annoy for neighbor search, n_neighbors = 140
17:03:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:03:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872221e5c2
17:03:30 Searching Annoy index using 1 thread, search_k = 14000
17:03:31 Annoy recall = 100%
17:03:41 Commencing smooth kNN distance calibration using 1 thread
17:04:00 Initializing from normalized Laplacian + noise
17:04:00 Commencing optimization for 500 epochs, with 190148 positive edges
17:04:13 Optimization finished
[1] "140 0.1"17:04:14 UMAP embedding parameters a = 1.577 b = 0.8951
17:04:14 Read 1203 rows and found 38 numeric columns
17:04:14 Using Annoy for neighbor search, n_neighbors = 140
17:04:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:04:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b43ef8d
17:04:14 Searching Annoy index using 1 thread, search_k = 14000
17:04:15 Annoy recall = 100%
17:04:25 Commencing smooth kNN distance calibration using 1 thread
17:04:44 Initializing from normalized Laplacian + noise
17:04:44 Commencing optimization for 500 epochs, with 190148 positive edges
17:04:57 Optimization finished
[1] "140 0.11"17:04:58 UMAP embedding parameters a = 1.544 b = 0.9058
17:04:58 Read 1203 rows and found 38 numeric columns
17:04:58 Using Annoy for neighbor search, n_neighbors = 140
17:04:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:04:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d95f28f
17:04:58 Searching Annoy index using 1 thread, search_k = 14000
17:04:59 Annoy recall = 100%
17:05:09 Commencing smooth kNN distance calibration using 1 thread
17:05:28 Initializing from normalized Laplacian + noise
17:05:28 Commencing optimization for 500 epochs, with 190148 positive edges
17:05:41 Optimization finished
[1] "140 0.12"17:05:42 UMAP embedding parameters a = 1.51 b = 0.9165
17:05:42 Read 1203 rows and found 38 numeric columns
17:05:42 Using Annoy for neighbor search, n_neighbors = 140
17:05:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:05:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fec572e
17:05:42 Searching Annoy index using 1 thread, search_k = 14000
17:05:43 Annoy recall = 100%
17:05:53 Commencing smooth kNN distance calibration using 1 thread
17:06:12 Initializing from normalized Laplacian + noise
17:06:12 Commencing optimization for 500 epochs, with 190148 positive edges
17:06:25 Optimization finished
[1] "140 0.13"17:06:26 UMAP embedding parameters a = 1.478 b = 0.9272
17:06:26 Read 1203 rows and found 38 numeric columns
17:06:26 Using Annoy for neighbor search, n_neighbors = 140
17:06:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:06:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d343f0d
17:06:26 Searching Annoy index using 1 thread, search_k = 14000
17:06:27 Annoy recall = 100%
17:06:37 Commencing smooth kNN distance calibration using 1 thread
17:06:56 Initializing from normalized Laplacian + noise
17:06:56 Commencing optimization for 500 epochs, with 190148 positive edges
17:07:09 Optimization finished
[1] "140 0.14"17:07:10 UMAP embedding parameters a = 1.446 b = 0.938
17:07:10 Read 1203 rows and found 38 numeric columns
17:07:10 Using Annoy for neighbor search, n_neighbors = 140
17:07:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:07:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724552e5f
17:07:10 Searching Annoy index using 1 thread, search_k = 14000
17:07:11 Annoy recall = 100%
17:07:21 Commencing smooth kNN distance calibration using 1 thread
17:07:40 Initializing from normalized Laplacian + noise
17:07:40 Commencing optimization for 500 epochs, with 190148 positive edges
17:07:54 Optimization finished
[1] "140 0.15"17:07:54 UMAP embedding parameters a = 1.414 b = 0.9488
17:07:54 Read 1203 rows and found 38 numeric columns
17:07:54 Using Annoy for neighbor search, n_neighbors = 140
17:07:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:07:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87794ede52
17:07:55 Searching Annoy index using 1 thread, search_k = 14000
17:07:56 Annoy recall = 100%
17:08:05 Commencing smooth kNN distance calibration using 1 thread
17:08:24 Initializing from normalized Laplacian + noise
17:08:24 Commencing optimization for 500 epochs, with 190148 positive edges
17:08:38 Optimization finished
[1] "140 0.16"17:08:38 UMAP embedding parameters a = 1.383 b = 0.9596
17:08:38 Read 1203 rows and found 38 numeric columns
17:08:38 Using Annoy for neighbor search, n_neighbors = 140
17:08:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:08:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759d648
17:08:39 Searching Annoy index using 1 thread, search_k = 14000
17:08:40 Annoy recall = 100%
17:08:49 Commencing smooth kNN distance calibration using 1 thread
17:09:08 Initializing from normalized Laplacian + noise
17:09:08 Commencing optimization for 500 epochs, with 190148 positive edges
17:09:22 Optimization finished
[1] "140 0.17"17:09:22 UMAP embedding parameters a = 1.352 b = 0.9704
17:09:22 Read 1203 rows and found 38 numeric columns
17:09:22 Using Annoy for neighbor search, n_neighbors = 140
17:09:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:09:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cf44878
17:09:23 Searching Annoy index using 1 thread, search_k = 14000
17:09:24 Annoy recall = 100%
17:09:33 Commencing smooth kNN distance calibration using 1 thread
17:09:52 Initializing from normalized Laplacian + noise
17:09:53 Commencing optimization for 500 epochs, with 190148 positive edges
17:10:06 Optimization finished
[1] "140 0.18"17:10:06 UMAP embedding parameters a = 1.321 b = 0.9813
17:10:06 Read 1203 rows and found 38 numeric columns
17:10:06 Using Annoy for neighbor search, n_neighbors = 140
17:10:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:10:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fdf7648
17:10:07 Searching Annoy index using 1 thread, search_k = 14000
17:10:08 Annoy recall = 100%
17:10:17 Commencing smooth kNN distance calibration using 1 thread
17:10:37 Initializing from normalized Laplacian + noise
17:10:37 Commencing optimization for 500 epochs, with 190148 positive edges
17:10:50 Optimization finished
[1] "140 0.19"17:10:51 UMAP embedding parameters a = 1.292 b = 0.9921
17:10:51 Read 1203 rows and found 38 numeric columns
17:10:51 Using Annoy for neighbor search, n_neighbors = 140
17:10:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:10:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748f672ad
17:10:51 Searching Annoy index using 1 thread, search_k = 14000
17:10:52 Annoy recall = 100%
17:11:02 Commencing smooth kNN distance calibration using 1 thread
17:11:21 Initializing from normalized Laplacian + noise
17:11:21 Commencing optimization for 500 epochs, with 190148 positive edges
17:11:34 Optimization finished
[1] "140 0.2"17:11:35 UMAP embedding parameters a = 1.262 b = 1.003
17:11:35 Read 1203 rows and found 38 numeric columns
17:11:35 Using Annoy for neighbor search, n_neighbors = 140
17:11:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:11:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bbc4e7a
17:11:35 Searching Annoy index using 1 thread, search_k = 14000
17:11:36 Annoy recall = 100%
17:11:46 Commencing smooth kNN distance calibration using 1 thread
17:12:05 Initializing from normalized Laplacian + noise
17:12:05 Commencing optimization for 500 epochs, with 190148 positive edges
17:12:19 Optimization finished
[1] "141 0"17:12:19 UMAP embedding parameters a = 1.933 b = 0.7905
17:12:19 Read 1203 rows and found 38 numeric columns
17:12:19 Using Annoy for neighbor search, n_neighbors = 141
17:12:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:12:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756d29516
17:12:20 Searching Annoy index using 1 thread, search_k = 14100
17:12:21 Annoy recall = 100%
17:12:30 Commencing smooth kNN distance calibration using 1 thread
17:12:49 Initializing from normalized Laplacian + noise
17:12:49 Commencing optimization for 500 epochs, with 191366 positive edges
17:13:03 Optimization finished
[1] "141 0.01"17:13:03 UMAP embedding parameters a = 1.896 b = 0.8006
17:13:03 Read 1203 rows and found 38 numeric columns
17:13:03 Using Annoy for neighbor search, n_neighbors = 141
17:13:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:13:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ba55715
17:13:04 Searching Annoy index using 1 thread, search_k = 14100
17:13:05 Annoy recall = 100%
17:13:15 Commencing smooth kNN distance calibration using 1 thread
17:13:33 Initializing from normalized Laplacian + noise
17:13:34 Commencing optimization for 500 epochs, with 191366 positive edges
17:13:47 Optimization finished
[1] "141 0.02"17:13:47 UMAP embedding parameters a = 1.859 b = 0.8109
17:13:47 Read 1203 rows and found 38 numeric columns
17:13:47 Using Annoy for neighbor search, n_neighbors = 141
17:13:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:13:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c6278e9
17:13:48 Searching Annoy index using 1 thread, search_k = 14100
17:13:49 Annoy recall = 100%
17:13:59 Commencing smooth kNN distance calibration using 1 thread
17:14:18 Initializing from normalized Laplacian + noise
17:14:18 Commencing optimization for 500 epochs, with 191366 positive edges
17:14:31 Optimization finished
[1] "141 0.03"17:14:32 UMAP embedding parameters a = 1.822 b = 0.8212
17:14:32 Read 1203 rows and found 38 numeric columns
17:14:32 Using Annoy for neighbor search, n_neighbors = 141
17:14:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:14:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87564d32bf
17:14:32 Searching Annoy index using 1 thread, search_k = 14100
17:14:33 Annoy recall = 100%
17:14:43 Commencing smooth kNN distance calibration using 1 thread
17:15:02 Initializing from normalized Laplacian + noise
17:15:02 Commencing optimization for 500 epochs, with 191366 positive edges
17:15:16 Optimization finished
[1] "141 0.04"17:15:16 UMAP embedding parameters a = 1.786 b = 0.8316
17:15:16 Read 1203 rows and found 38 numeric columns
17:15:16 Using Annoy for neighbor search, n_neighbors = 141
17:15:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:15:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87359f6ee
17:15:17 Searching Annoy index using 1 thread, search_k = 14100
17:15:18 Annoy recall = 100%
17:15:27 Commencing smooth kNN distance calibration using 1 thread
17:15:46 Initializing from normalized Laplacian + noise
17:15:46 Commencing optimization for 500 epochs, with 191366 positive edges
17:16:00 Optimization finished
[1] "141 0.05"17:16:00 UMAP embedding parameters a = 1.75 b = 0.8421
17:16:00 Read 1203 rows and found 38 numeric columns
17:16:00 Using Annoy for neighbor search, n_neighbors = 141
17:16:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:16:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87195eccac
17:16:01 Searching Annoy index using 1 thread, search_k = 14100
17:16:02 Annoy recall = 100%
17:16:12 Commencing smooth kNN distance calibration using 1 thread
17:16:31 Initializing from normalized Laplacian + noise
17:16:31 Commencing optimization for 500 epochs, with 191366 positive edges
17:16:44 Optimization finished
[1] "141 0.06"17:16:45 UMAP embedding parameters a = 1.715 b = 0.8526
17:16:45 Read 1203 rows and found 38 numeric columns
17:16:45 Using Annoy for neighbor search, n_neighbors = 141
17:16:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:16:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c82ad72
17:16:45 Searching Annoy index using 1 thread, search_k = 14100
17:16:46 Annoy recall = 100%
17:16:56 Commencing smooth kNN distance calibration using 1 thread
17:17:15 Initializing from normalized Laplacian + noise
17:17:15 Commencing optimization for 500 epochs, with 191366 positive edges
17:17:29 Optimization finished
[1] "141 0.07"17:17:29 UMAP embedding parameters a = 1.68 b = 0.8631
17:17:29 Read 1203 rows and found 38 numeric columns
17:17:29 Using Annoy for neighbor search, n_neighbors = 141
17:17:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:17:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734858acf
17:17:30 Searching Annoy index using 1 thread, search_k = 14100
17:17:31 Annoy recall = 100%
17:17:40 Commencing smooth kNN distance calibration using 1 thread
17:17:59 Initializing from normalized Laplacian + noise
17:17:59 Commencing optimization for 500 epochs, with 191366 positive edges
17:18:13 Optimization finished
[1] "141 0.08"17:18:13 UMAP embedding parameters a = 1.645 b = 0.8737
17:18:13 Read 1203 rows and found 38 numeric columns
17:18:13 Using Annoy for neighbor search, n_neighbors = 141
17:18:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:18:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a68797f
17:18:14 Searching Annoy index using 1 thread, search_k = 14100
17:18:15 Annoy recall = 100%
17:18:25 Commencing smooth kNN distance calibration using 1 thread
17:18:44 Initializing from normalized Laplacian + noise
17:18:44 Commencing optimization for 500 epochs, with 191366 positive edges
17:18:57 Optimization finished
[1] "141 0.09"17:18:58 UMAP embedding parameters a = 1.611 b = 0.8844
17:18:58 Read 1203 rows and found 38 numeric columns
17:18:58 Using Annoy for neighbor search, n_neighbors = 141
17:18:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:18:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87356903ec
17:18:58 Searching Annoy index using 1 thread, search_k = 14100
17:18:59 Annoy recall = 100%
17:19:09 Commencing smooth kNN distance calibration using 1 thread
17:19:28 Initializing from normalized Laplacian + noise
17:19:28 Commencing optimization for 500 epochs, with 191366 positive edges
17:19:42 Optimization finished
[1] "141 0.1"17:19:42 UMAP embedding parameters a = 1.577 b = 0.8951
17:19:42 Read 1203 rows and found 38 numeric columns
17:19:42 Using Annoy for neighbor search, n_neighbors = 141
17:19:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:19:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767578694
17:19:43 Searching Annoy index using 1 thread, search_k = 14100
17:19:44 Annoy recall = 100%
17:19:53 Commencing smooth kNN distance calibration using 1 thread
17:20:12 Initializing from normalized Laplacian + noise
17:20:13 Commencing optimization for 500 epochs, with 191366 positive edges
17:20:26 Optimization finished
[1] "141 0.11"17:20:27 UMAP embedding parameters a = 1.544 b = 0.9058
17:20:27 Read 1203 rows and found 38 numeric columns
17:20:27 Using Annoy for neighbor search, n_neighbors = 141
17:20:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:20:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a718b9f
17:20:27 Searching Annoy index using 1 thread, search_k = 14100
17:20:28 Annoy recall = 100%
17:20:38 Commencing smooth kNN distance calibration using 1 thread
17:20:57 Initializing from normalized Laplacian + noise
17:20:57 Commencing optimization for 500 epochs, with 191366 positive edges
17:21:11 Optimization finished
[1] "141 0.12"17:21:11 UMAP embedding parameters a = 1.51 b = 0.9165
17:21:11 Read 1203 rows and found 38 numeric columns
17:21:11 Using Annoy for neighbor search, n_neighbors = 141
17:21:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:21:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872caf0383
17:21:12 Searching Annoy index using 1 thread, search_k = 14100
17:21:13 Annoy recall = 100%
17:21:22 Commencing smooth kNN distance calibration using 1 thread
17:21:41 Initializing from normalized Laplacian + noise
17:21:41 Commencing optimization for 500 epochs, with 191366 positive edges
17:21:55 Optimization finished
[1] "141 0.13"17:21:55 UMAP embedding parameters a = 1.478 b = 0.9272
17:21:55 Read 1203 rows and found 38 numeric columns
17:21:55 Using Annoy for neighbor search, n_neighbors = 141
17:21:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:21:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b1c50ab
17:21:56 Searching Annoy index using 1 thread, search_k = 14100
17:21:57 Annoy recall = 100%
17:22:06 Commencing smooth kNN distance calibration using 1 thread
17:22:26 Initializing from normalized Laplacian + noise
17:22:26 Commencing optimization for 500 epochs, with 191366 positive edges
17:22:40 Optimization finished
[1] "141 0.14"17:22:40 UMAP embedding parameters a = 1.446 b = 0.938
17:22:40 Read 1203 rows and found 38 numeric columns
17:22:40 Using Annoy for neighbor search, n_neighbors = 141
17:22:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:22:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872750e1b6
17:22:41 Searching Annoy index using 1 thread, search_k = 14100
17:22:42 Annoy recall = 100%
17:22:51 Commencing smooth kNN distance calibration using 1 thread
17:23:10 Initializing from normalized Laplacian + noise
17:23:10 Commencing optimization for 500 epochs, with 191366 positive edges
17:23:24 Optimization finished
[1] "141 0.15"17:23:24 UMAP embedding parameters a = 1.414 b = 0.9488
17:23:24 Read 1203 rows and found 38 numeric columns
17:23:24 Using Annoy for neighbor search, n_neighbors = 141
17:23:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:23:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873533d1d0
17:23:25 Searching Annoy index using 1 thread, search_k = 14100
17:23:26 Annoy recall = 100%
17:23:35 Commencing smooth kNN distance calibration using 1 thread
17:23:55 Initializing from normalized Laplacian + noise
17:23:55 Commencing optimization for 500 epochs, with 191366 positive edges
17:24:08 Optimization finished
[1] "141 0.16"17:24:08 UMAP embedding parameters a = 1.383 b = 0.9596
17:24:09 Read 1203 rows and found 38 numeric columns
17:24:09 Using Annoy for neighbor search, n_neighbors = 141
17:24:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:24:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753402374
17:24:09 Searching Annoy index using 1 thread, search_k = 14100
17:24:10 Annoy recall = 100%
17:24:20 Commencing smooth kNN distance calibration using 1 thread
17:24:39 Initializing from normalized Laplacian + noise
17:24:39 Commencing optimization for 500 epochs, with 191366 positive edges
17:24:53 Optimization finished
[1] "141 0.17"17:24:53 UMAP embedding parameters a = 1.352 b = 0.9704
17:24:53 Read 1203 rows and found 38 numeric columns
17:24:53 Using Annoy for neighbor search, n_neighbors = 141
17:24:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:24:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c210c0e
17:24:54 Searching Annoy index using 1 thread, search_k = 14100
17:24:55 Annoy recall = 100%
17:25:04 Commencing smooth kNN distance calibration using 1 thread
17:25:23 Initializing from normalized Laplacian + noise
17:25:24 Commencing optimization for 500 epochs, with 191366 positive edges
17:25:37 Optimization finished
[1] "141 0.18"17:25:38 UMAP embedding parameters a = 1.321 b = 0.9813
17:25:38 Read 1203 rows and found 38 numeric columns
17:25:38 Using Annoy for neighbor search, n_neighbors = 141
17:25:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:25:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e37a865
17:25:38 Searching Annoy index using 1 thread, search_k = 14100
17:25:39 Annoy recall = 100%
17:25:49 Commencing smooth kNN distance calibration using 1 thread
17:26:09 Initializing from normalized Laplacian + noise
17:26:09 Commencing optimization for 500 epochs, with 191366 positive edges
17:26:23 Optimization finished
[1] "141 0.19"17:26:23 UMAP embedding parameters a = 1.292 b = 0.9921
17:26:23 Read 1203 rows and found 38 numeric columns
17:26:23 Using Annoy for neighbor search, n_neighbors = 141
17:26:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:26:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775620937
17:26:24 Searching Annoy index using 1 thread, search_k = 14100
17:26:25 Annoy recall = 100%
17:26:34 Commencing smooth kNN distance calibration using 1 thread
17:26:54 Initializing from normalized Laplacian + noise
17:26:54 Commencing optimization for 500 epochs, with 191366 positive edges
17:27:08 Optimization finished
[1] "141 0.2"17:27:08 UMAP embedding parameters a = 1.262 b = 1.003
17:27:08 Read 1203 rows and found 38 numeric columns
17:27:08 Using Annoy for neighbor search, n_neighbors = 141
17:27:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:27:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87764fb9b
17:27:09 Searching Annoy index using 1 thread, search_k = 14100
17:27:10 Annoy recall = 100%
17:27:20 Commencing smooth kNN distance calibration using 1 thread
17:27:40 Initializing from normalized Laplacian + noise
17:27:40 Commencing optimization for 500 epochs, with 191366 positive edges
17:27:54 Optimization finished
[1] "142 0"17:27:54 UMAP embedding parameters a = 1.933 b = 0.7905
17:27:54 Read 1203 rows and found 38 numeric columns
17:27:54 Using Annoy for neighbor search, n_neighbors = 142
17:27:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:27:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bcd9af4
17:27:55 Searching Annoy index using 1 thread, search_k = 14200
17:27:56 Annoy recall = 100%
17:28:05 Commencing smooth kNN distance calibration using 1 thread
17:28:25 Initializing from normalized Laplacian + noise
17:28:25 Commencing optimization for 500 epochs, with 192600 positive edges
17:28:39 Optimization finished
[1] "142 0.01"17:28:39 UMAP embedding parameters a = 1.896 b = 0.8006
17:28:40 Read 1203 rows and found 38 numeric columns
17:28:40 Using Annoy for neighbor search, n_neighbors = 142
17:28:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:28:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87254e6065
17:28:40 Searching Annoy index using 1 thread, search_k = 14200
17:28:41 Annoy recall = 100%
17:28:51 Commencing smooth kNN distance calibration using 1 thread
17:29:11 Initializing from normalized Laplacian + noise
17:29:11 Commencing optimization for 500 epochs, with 192600 positive edges
17:29:25 Optimization finished
[1] "142 0.02"17:29:25 UMAP embedding parameters a = 1.859 b = 0.8109
17:29:25 Read 1203 rows and found 38 numeric columns
17:29:25 Using Annoy for neighbor search, n_neighbors = 142
17:29:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:29:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874993aa8
17:29:26 Searching Annoy index using 1 thread, search_k = 14200
17:29:27 Annoy recall = 100%
17:29:37 Commencing smooth kNN distance calibration using 1 thread
17:29:56 Initializing from normalized Laplacian + noise
17:29:56 Commencing optimization for 500 epochs, with 192600 positive edges
17:30:10 Optimization finished
[1] "142 0.03"17:30:11 UMAP embedding parameters a = 1.822 b = 0.8212
17:30:11 Read 1203 rows and found 38 numeric columns
17:30:11 Using Annoy for neighbor search, n_neighbors = 142
17:30:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:30:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722c953
17:30:11 Searching Annoy index using 1 thread, search_k = 14200
17:30:12 Annoy recall = 100%
17:30:22 Commencing smooth kNN distance calibration using 1 thread
17:30:42 Initializing from normalized Laplacian + noise
17:30:42 Commencing optimization for 500 epochs, with 192600 positive edges
17:30:56 Optimization finished
[1] "142 0.04"17:30:56 UMAP embedding parameters a = 1.786 b = 0.8316
17:30:56 Read 1203 rows and found 38 numeric columns
17:30:56 Using Annoy for neighbor search, n_neighbors = 142
17:30:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:30:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e9d3eb7
17:30:57 Searching Annoy index using 1 thread, search_k = 14200
17:30:58 Annoy recall = 100%
17:31:08 Commencing smooth kNN distance calibration using 1 thread
17:31:27 Initializing from normalized Laplacian + noise
17:31:28 Commencing optimization for 500 epochs, with 192600 positive edges
17:31:42 Optimization finished
[1] "142 0.05"17:31:42 UMAP embedding parameters a = 1.75 b = 0.8421
17:31:42 Read 1203 rows and found 38 numeric columns
17:31:42 Using Annoy for neighbor search, n_neighbors = 142
17:31:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:31:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f310f0
17:31:43 Searching Annoy index using 1 thread, search_k = 14200
17:31:44 Annoy recall = 100%
17:31:53 Commencing smooth kNN distance calibration using 1 thread
17:32:13 Initializing from normalized Laplacian + noise
17:32:13 Commencing optimization for 500 epochs, with 192600 positive edges
17:32:27 Optimization finished
[1] "142 0.06"17:32:27 UMAP embedding parameters a = 1.715 b = 0.8526
17:32:27 Read 1203 rows and found 38 numeric columns
17:32:27 Using Annoy for neighbor search, n_neighbors = 142
17:32:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:32:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d1711cc
17:32:28 Searching Annoy index using 1 thread, search_k = 14200
17:32:29 Annoy recall = 100%
17:32:39 Commencing smooth kNN distance calibration using 1 thread
17:32:59 Initializing from normalized Laplacian + noise
17:32:59 Commencing optimization for 500 epochs, with 192600 positive edges
17:33:13 Optimization finished
[1] "142 0.07"17:33:13 UMAP embedding parameters a = 1.68 b = 0.8631
17:33:13 Read 1203 rows and found 38 numeric columns
17:33:13 Using Annoy for neighbor search, n_neighbors = 142
17:33:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:33:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e7cb4ff
17:33:14 Searching Annoy index using 1 thread, search_k = 14200
17:33:15 Annoy recall = 100%
17:33:25 Commencing smooth kNN distance calibration using 1 thread
17:33:44 Initializing from normalized Laplacian + noise
17:33:44 Commencing optimization for 500 epochs, with 192600 positive edges
17:33:58 Optimization finished
[1] "142 0.08"17:33:59 UMAP embedding parameters a = 1.645 b = 0.8737
17:33:59 Read 1203 rows and found 38 numeric columns
17:33:59 Using Annoy for neighbor search, n_neighbors = 142
17:33:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:33:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874de9839d
17:33:59 Searching Annoy index using 1 thread, search_k = 14200
17:34:00 Annoy recall = 100%
17:34:10 Commencing smooth kNN distance calibration using 1 thread
17:34:30 Initializing from normalized Laplacian + noise
17:34:30 Commencing optimization for 500 epochs, with 192600 positive edges
17:34:44 Optimization finished
[1] "142 0.09"17:34:44 UMAP embedding parameters a = 1.611 b = 0.8844
17:34:44 Read 1203 rows and found 38 numeric columns
17:34:44 Using Annoy for neighbor search, n_neighbors = 142
17:34:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:34:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738d36046
17:34:45 Searching Annoy index using 1 thread, search_k = 14200
17:34:46 Annoy recall = 100%
17:34:56 Commencing smooth kNN distance calibration using 1 thread
17:35:16 Initializing from normalized Laplacian + noise
17:35:16 Commencing optimization for 500 epochs, with 192600 positive edges
17:35:30 Optimization finished
[1] "142 0.1"17:35:30 UMAP embedding parameters a = 1.577 b = 0.8951
17:35:30 Read 1203 rows and found 38 numeric columns
17:35:30 Using Annoy for neighbor search, n_neighbors = 142
17:35:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:35:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87254f4a15
17:35:31 Searching Annoy index using 1 thread, search_k = 14200
17:35:32 Annoy recall = 100%
17:35:41 Commencing smooth kNN distance calibration using 1 thread
17:36:01 Initializing from normalized Laplacian + noise
17:36:01 Commencing optimization for 500 epochs, with 192600 positive edges
17:36:16 Optimization finished
[1] "142 0.11"17:36:16 UMAP embedding parameters a = 1.544 b = 0.9058
17:36:16 Read 1203 rows and found 38 numeric columns
17:36:16 Using Annoy for neighbor search, n_neighbors = 142
17:36:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:36:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87698edab3
17:36:16 Searching Annoy index using 1 thread, search_k = 14200
17:36:17 Annoy recall = 100%
17:36:27 Commencing smooth kNN distance calibration using 1 thread
17:36:47 Initializing from normalized Laplacian + noise
17:36:47 Commencing optimization for 500 epochs, with 192600 positive edges
17:37:01 Optimization finished
[1] "142 0.12"17:37:01 UMAP embedding parameters a = 1.51 b = 0.9165
17:37:01 Read 1203 rows and found 38 numeric columns
17:37:01 Using Annoy for neighbor search, n_neighbors = 142
17:37:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:37:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873535d930
17:37:02 Searching Annoy index using 1 thread, search_k = 14200
17:37:03 Annoy recall = 100%
17:37:13 Commencing smooth kNN distance calibration using 1 thread
17:37:33 Initializing from normalized Laplacian + noise
17:37:33 Commencing optimization for 500 epochs, with 192600 positive edges
17:37:47 Optimization finished
[1] "142 0.13"17:37:47 UMAP embedding parameters a = 1.478 b = 0.9272
17:37:47 Read 1203 rows and found 38 numeric columns
17:37:47 Using Annoy for neighbor search, n_neighbors = 142
17:37:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:37:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b9c7cd4
17:37:48 Searching Annoy index using 1 thread, search_k = 14200
17:37:49 Annoy recall = 100%
17:37:59 Commencing smooth kNN distance calibration using 1 thread
17:38:19 Initializing from normalized Laplacian + noise
17:38:19 Commencing optimization for 500 epochs, with 192600 positive edges
17:38:33 Optimization finished
[1] "142 0.14"17:38:33 UMAP embedding parameters a = 1.446 b = 0.938
17:38:33 Read 1203 rows and found 38 numeric columns
17:38:33 Using Annoy for neighbor search, n_neighbors = 142
17:38:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:38:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ce8d1a1
17:38:34 Searching Annoy index using 1 thread, search_k = 14200
17:38:35 Annoy recall = 100%
17:38:44 Commencing smooth kNN distance calibration using 1 thread
17:39:04 Initializing from normalized Laplacian + noise
17:39:04 Commencing optimization for 500 epochs, with 192600 positive edges
17:39:18 Optimization finished
[1] "142 0.15"17:39:19 UMAP embedding parameters a = 1.414 b = 0.9488
17:39:19 Read 1203 rows and found 38 numeric columns
17:39:19 Using Annoy for neighbor search, n_neighbors = 142
17:39:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:39:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e94a5dc
17:39:19 Searching Annoy index using 1 thread, search_k = 14200
17:39:20 Annoy recall = 100%
17:39:30 Commencing smooth kNN distance calibration using 1 thread
17:39:50 Initializing from normalized Laplacian + noise
17:39:50 Commencing optimization for 500 epochs, with 192600 positive edges
17:40:04 Optimization finished
[1] "142 0.16"17:40:04 UMAP embedding parameters a = 1.383 b = 0.9596
17:40:04 Read 1203 rows and found 38 numeric columns
17:40:04 Using Annoy for neighbor search, n_neighbors = 142
17:40:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:40:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87781f2a46
17:40:05 Searching Annoy index using 1 thread, search_k = 14200
17:40:06 Annoy recall = 100%
17:40:16 Commencing smooth kNN distance calibration using 1 thread
17:40:36 Initializing from normalized Laplacian + noise
17:40:36 Commencing optimization for 500 epochs, with 192600 positive edges
17:40:50 Optimization finished
[1] "142 0.17"17:40:50 UMAP embedding parameters a = 1.352 b = 0.9704
17:40:50 Read 1203 rows and found 38 numeric columns
17:40:50 Using Annoy for neighbor search, n_neighbors = 142
17:40:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:40:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87216e5c71
17:40:51 Searching Annoy index using 1 thread, search_k = 14200
17:40:52 Annoy recall = 100%
17:41:01 Commencing smooth kNN distance calibration using 1 thread
17:41:21 Initializing from normalized Laplacian + noise
17:41:22 Commencing optimization for 500 epochs, with 192600 positive edges
17:41:36 Optimization finished
[1] "142 0.18"17:41:36 UMAP embedding parameters a = 1.321 b = 0.9813
17:41:36 Read 1203 rows and found 38 numeric columns
17:41:36 Using Annoy for neighbor search, n_neighbors = 142
17:41:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748fd1f5b
17:41:37 Searching Annoy index using 1 thread, search_k = 14200
17:41:38 Annoy recall = 100%
17:41:47 Commencing smooth kNN distance calibration using 1 thread
17:42:07 Initializing from normalized Laplacian + noise
17:42:07 Commencing optimization for 500 epochs, with 192600 positive edges
17:42:21 Optimization finished
[1] "142 0.19"17:42:22 UMAP embedding parameters a = 1.292 b = 0.9921
17:42:22 Read 1203 rows and found 38 numeric columns
17:42:22 Using Annoy for neighbor search, n_neighbors = 142
17:42:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d882e32
17:42:22 Searching Annoy index using 1 thread, search_k = 14200
17:42:23 Annoy recall = 100%
17:42:33 Commencing smooth kNN distance calibration using 1 thread
17:42:53 Initializing from normalized Laplacian + noise
17:42:53 Commencing optimization for 500 epochs, with 192600 positive edges
17:43:07 Optimization finished
[1] "142 0.2"17:43:07 UMAP embedding parameters a = 1.262 b = 1.003
17:43:07 Read 1203 rows and found 38 numeric columns
17:43:07 Using Annoy for neighbor search, n_neighbors = 142
17:43:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:43:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878c5e305
17:43:08 Searching Annoy index using 1 thread, search_k = 14200
17:43:09 Annoy recall = 100%
17:43:19 Commencing smooth kNN distance calibration using 1 thread
17:43:39 Initializing from normalized Laplacian + noise
17:43:39 Commencing optimization for 500 epochs, with 192600 positive edges
17:43:53 Optimization finished
[1] "143 0"17:43:53 UMAP embedding parameters a = 1.933 b = 0.7905
17:43:53 Read 1203 rows and found 38 numeric columns
17:43:53 Using Annoy for neighbor search, n_neighbors = 143
17:43:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:43:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87436eaafa
17:43:54 Searching Annoy index using 1 thread, search_k = 14300
17:43:55 Annoy recall = 100%
17:44:05 Commencing smooth kNN distance calibration using 1 thread
17:44:25 Initializing from normalized Laplacian + noise
17:44:25 Commencing optimization for 500 epochs, with 193796 positive edges
17:44:39 Optimization finished
[1] "143 0.01"17:44:39 UMAP embedding parameters a = 1.896 b = 0.8006
17:44:39 Read 1203 rows and found 38 numeric columns
17:44:39 Using Annoy for neighbor search, n_neighbors = 143
17:44:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:44:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a3731b6
17:44:40 Searching Annoy index using 1 thread, search_k = 14300
17:44:41 Annoy recall = 100%
17:44:51 Commencing smooth kNN distance calibration using 1 thread
17:45:10 Initializing from normalized Laplacian + noise
17:45:10 Commencing optimization for 500 epochs, with 193796 positive edges
17:45:24 Optimization finished
[1] "143 0.02"17:45:25 UMAP embedding parameters a = 1.859 b = 0.8109
17:45:25 Read 1203 rows and found 38 numeric columns
17:45:25 Using Annoy for neighbor search, n_neighbors = 143
17:45:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:45:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743e233b1
17:45:25 Searching Annoy index using 1 thread, search_k = 14300
17:45:26 Annoy recall = 100%
17:45:36 Commencing smooth kNN distance calibration using 1 thread
17:45:56 Initializing from normalized Laplacian + noise
17:45:56 Commencing optimization for 500 epochs, with 193796 positive edges
17:46:10 Optimization finished
[1] "143 0.03"17:46:11 UMAP embedding parameters a = 1.822 b = 0.8212
17:46:11 Read 1203 rows and found 38 numeric columns
17:46:11 Using Annoy for neighbor search, n_neighbors = 143
17:46:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:46:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876abf8cb0
17:46:11 Searching Annoy index using 1 thread, search_k = 14300
17:46:12 Annoy recall = 100%
17:46:22 Commencing smooth kNN distance calibration using 1 thread
17:46:42 Initializing from normalized Laplacian + noise
17:46:42 Commencing optimization for 500 epochs, with 193796 positive edges
17:46:56 Optimization finished
[1] "143 0.04"17:46:57 UMAP embedding parameters a = 1.786 b = 0.8316
17:46:57 Read 1203 rows and found 38 numeric columns
17:46:57 Using Annoy for neighbor search, n_neighbors = 143
17:46:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:46:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f6b0386
17:46:57 Searching Annoy index using 1 thread, search_k = 14300
17:46:58 Annoy recall = 100%
17:47:08 Commencing smooth kNN distance calibration using 1 thread
17:47:28 Initializing from normalized Laplacian + noise
17:47:28 Commencing optimization for 500 epochs, with 193796 positive edges
17:47:42 Optimization finished
[1] "143 0.05"17:47:42 UMAP embedding parameters a = 1.75 b = 0.8421
17:47:42 Read 1203 rows and found 38 numeric columns
17:47:42 Using Annoy for neighbor search, n_neighbors = 143
17:47:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:47:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717225725
17:47:43 Searching Annoy index using 1 thread, search_k = 14300
17:47:44 Annoy recall = 100%
17:47:54 Commencing smooth kNN distance calibration using 1 thread
17:48:13 Initializing from normalized Laplacian + noise
17:48:14 Commencing optimization for 500 epochs, with 193796 positive edges
17:48:27 Optimization finished
[1] "143 0.06"17:48:28 UMAP embedding parameters a = 1.715 b = 0.8526
17:48:28 Read 1203 rows and found 38 numeric columns
17:48:28 Using Annoy for neighbor search, n_neighbors = 143
17:48:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:48:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716e098be
17:48:28 Searching Annoy index using 1 thread, search_k = 14300
17:48:29 Annoy recall = 100%
17:48:39 Commencing smooth kNN distance calibration using 1 thread
17:48:59 Initializing from normalized Laplacian + noise
17:48:59 Commencing optimization for 500 epochs, with 193796 positive edges
17:49:13 Optimization finished
[1] "143 0.07"17:49:14 UMAP embedding parameters a = 1.68 b = 0.8631
17:49:14 Read 1203 rows and found 38 numeric columns
17:49:14 Using Annoy for neighbor search, n_neighbors = 143
17:49:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:49:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87da2abeb
17:49:14 Searching Annoy index using 1 thread, search_k = 14300
17:49:15 Annoy recall = 100%
17:49:25 Commencing smooth kNN distance calibration using 1 thread
17:49:45 Initializing from normalized Laplacian + noise
17:49:45 Commencing optimization for 500 epochs, with 193796 positive edges
17:49:59 Optimization finished
[1] "143 0.08"17:49:59 UMAP embedding parameters a = 1.645 b = 0.8737
17:49:59 Read 1203 rows and found 38 numeric columns
17:49:59 Using Annoy for neighbor search, n_neighbors = 143
17:49:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:50:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c84605c
17:50:00 Searching Annoy index using 1 thread, search_k = 14300
17:50:01 Annoy recall = 100%
17:50:11 Commencing smooth kNN distance calibration using 1 thread
17:50:30 Initializing from normalized Laplacian + noise
17:50:31 Commencing optimization for 500 epochs, with 193796 positive edges
17:50:45 Optimization finished
[1] "143 0.09"17:50:45 UMAP embedding parameters a = 1.611 b = 0.8844
17:50:45 Read 1203 rows and found 38 numeric columns
17:50:45 Using Annoy for neighbor search, n_neighbors = 143
17:50:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:50:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e45945a
17:50:46 Searching Annoy index using 1 thread, search_k = 14300
17:50:47 Annoy recall = 100%
17:50:56 Commencing smooth kNN distance calibration using 1 thread
17:51:16 Initializing from normalized Laplacian + noise
17:51:17 Commencing optimization for 500 epochs, with 193796 positive edges
17:51:30 Optimization finished
[1] "143 0.1"17:51:31 UMAP embedding parameters a = 1.577 b = 0.8951
17:51:31 Read 1203 rows and found 38 numeric columns
17:51:31 Using Annoy for neighbor search, n_neighbors = 143
17:51:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:51:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87697046df
17:51:31 Searching Annoy index using 1 thread, search_k = 14300
17:51:32 Annoy recall = 100%
17:51:42 Commencing smooth kNN distance calibration using 1 thread
17:52:02 Initializing from normalized Laplacian + noise
17:52:02 Commencing optimization for 500 epochs, with 193796 positive edges
17:52:16 Optimization finished
[1] "143 0.11"17:52:17 UMAP embedding parameters a = 1.544 b = 0.9058
17:52:17 Read 1203 rows and found 38 numeric columns
17:52:17 Using Annoy for neighbor search, n_neighbors = 143
17:52:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:52:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731d2c0c1
17:52:17 Searching Annoy index using 1 thread, search_k = 14300
17:52:18 Annoy recall = 100%
17:52:28 Commencing smooth kNN distance calibration using 1 thread
17:52:48 Initializing from normalized Laplacian + noise
17:52:48 Commencing optimization for 500 epochs, with 193796 positive edges
17:53:02 Optimization finished
[1] "143 0.12"17:53:02 UMAP embedding parameters a = 1.51 b = 0.9165
17:53:02 Read 1203 rows and found 38 numeric columns
17:53:02 Using Annoy for neighbor search, n_neighbors = 143
17:53:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:53:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722decf02
17:53:03 Searching Annoy index using 1 thread, search_k = 14300
17:53:04 Annoy recall = 100%
17:53:14 Commencing smooth kNN distance calibration using 1 thread
17:53:34 Initializing from normalized Laplacian + noise
17:53:34 Commencing optimization for 500 epochs, with 193796 positive edges
17:53:48 Optimization finished
[1] "143 0.13"17:53:48 UMAP embedding parameters a = 1.478 b = 0.9272
17:53:48 Read 1203 rows and found 38 numeric columns
17:53:48 Using Annoy for neighbor search, n_neighbors = 143
17:53:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:53:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769931033
17:53:49 Searching Annoy index using 1 thread, search_k = 14300
17:53:50 Annoy recall = 100%
17:53:59 Commencing smooth kNN distance calibration using 1 thread
17:54:19 Initializing from normalized Laplacian + noise
17:54:20 Commencing optimization for 500 epochs, with 193796 positive edges
17:54:33 Optimization finished
[1] "143 0.14"17:54:34 UMAP embedding parameters a = 1.446 b = 0.938
17:54:34 Read 1203 rows and found 38 numeric columns
17:54:34 Using Annoy for neighbor search, n_neighbors = 143
17:54:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:54:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87506fff78
17:54:34 Searching Annoy index using 1 thread, search_k = 14300
17:54:35 Annoy recall = 100%
17:54:45 Commencing smooth kNN distance calibration using 1 thread
17:55:05 Initializing from normalized Laplacian + noise
17:55:05 Commencing optimization for 500 epochs, with 193796 positive edges
17:55:19 Optimization finished
[1] "143 0.15"17:55:20 UMAP embedding parameters a = 1.414 b = 0.9488
17:55:20 Read 1203 rows and found 38 numeric columns
17:55:20 Using Annoy for neighbor search, n_neighbors = 143
17:55:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:55:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727d1dff3
17:55:20 Searching Annoy index using 1 thread, search_k = 14300
17:55:21 Annoy recall = 100%
17:55:31 Commencing smooth kNN distance calibration using 1 thread
17:55:51 Initializing from normalized Laplacian + noise
17:55:51 Commencing optimization for 500 epochs, with 193796 positive edges
17:56:05 Optimization finished
[1] "143 0.16"17:56:05 UMAP embedding parameters a = 1.383 b = 0.9596
17:56:05 Read 1203 rows and found 38 numeric columns
17:56:05 Using Annoy for neighbor search, n_neighbors = 143
17:56:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:56:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756aa21ff
17:56:06 Searching Annoy index using 1 thread, search_k = 14300
17:56:07 Annoy recall = 100%
17:56:17 Commencing smooth kNN distance calibration using 1 thread
17:56:37 Initializing from normalized Laplacian + noise
17:56:37 Commencing optimization for 500 epochs, with 193796 positive edges
17:56:51 Optimization finished
[1] "143 0.17"17:56:51 UMAP embedding parameters a = 1.352 b = 0.9704
17:56:51 Read 1203 rows and found 38 numeric columns
17:56:51 Using Annoy for neighbor search, n_neighbors = 143
17:56:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:56:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871eecb477
17:56:52 Searching Annoy index using 1 thread, search_k = 14300
17:56:53 Annoy recall = 100%
17:57:03 Commencing smooth kNN distance calibration using 1 thread
17:57:23 Initializing from normalized Laplacian + noise
17:57:23 Commencing optimization for 500 epochs, with 193796 positive edges
17:57:37 Optimization finished
[1] "143 0.18"17:57:37 UMAP embedding parameters a = 1.321 b = 0.9813
17:57:37 Read 1203 rows and found 38 numeric columns
17:57:37 Using Annoy for neighbor search, n_neighbors = 143
17:57:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:57:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775bb6390
17:57:38 Searching Annoy index using 1 thread, search_k = 14300
17:57:39 Annoy recall = 100%
17:57:48 Commencing smooth kNN distance calibration using 1 thread
17:58:08 Initializing from normalized Laplacian + noise
17:58:08 Commencing optimization for 500 epochs, with 193796 positive edges
17:58:23 Optimization finished
[1] "143 0.19"17:58:23 UMAP embedding parameters a = 1.292 b = 0.9921
17:58:23 Read 1203 rows and found 38 numeric columns
17:58:23 Using Annoy for neighbor search, n_neighbors = 143
17:58:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:58:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f7d8245
17:58:23 Searching Annoy index using 1 thread, search_k = 14300
17:58:25 Annoy recall = 100%
17:58:34 Commencing smooth kNN distance calibration using 1 thread
17:58:54 Initializing from normalized Laplacian + noise
17:58:54 Commencing optimization for 500 epochs, with 193796 positive edges
17:59:08 Optimization finished
[1] "143 0.2"17:59:09 UMAP embedding parameters a = 1.262 b = 1.003
17:59:09 Read 1203 rows and found 38 numeric columns
17:59:09 Using Annoy for neighbor search, n_neighbors = 143
17:59:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:59:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87443bfe8c
17:59:09 Searching Annoy index using 1 thread, search_k = 14300
17:59:10 Annoy recall = 100%
17:59:20 Commencing smooth kNN distance calibration using 1 thread
17:59:40 Initializing from normalized Laplacian + noise
17:59:40 Commencing optimization for 500 epochs, with 193796 positive edges
17:59:54 Optimization finished
[1] "144 0"17:59:55 UMAP embedding parameters a = 1.933 b = 0.7905
17:59:55 Read 1203 rows and found 38 numeric columns
17:59:55 Using Annoy for neighbor search, n_neighbors = 144
17:59:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:59:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f4a3e43
17:59:55 Searching Annoy index using 1 thread, search_k = 14400
17:59:56 Annoy recall = 100%
18:00:06 Commencing smooth kNN distance calibration using 1 thread
18:00:26 Initializing from normalized Laplacian + noise
18:00:26 Commencing optimization for 500 epochs, with 195022 positive edges
18:00:40 Optimization finished
[1] "144 0.01"18:00:41 UMAP embedding parameters a = 1.896 b = 0.8006
18:00:41 Read 1203 rows and found 38 numeric columns
18:00:41 Using Annoy for neighbor search, n_neighbors = 144
18:00:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:00:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744b35b75
18:00:41 Searching Annoy index using 1 thread, search_k = 14400
18:00:42 Annoy recall = 100%
18:00:52 Commencing smooth kNN distance calibration using 1 thread
18:01:12 Initializing from normalized Laplacian + noise
18:01:12 Commencing optimization for 500 epochs, with 195022 positive edges
18:01:26 Optimization finished
[1] "144 0.02"18:01:26 UMAP embedding parameters a = 1.859 b = 0.8109
18:01:26 Read 1203 rows and found 38 numeric columns
18:01:26 Using Annoy for neighbor search, n_neighbors = 144
18:01:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:01:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fd87b61
18:01:27 Searching Annoy index using 1 thread, search_k = 14400
18:01:28 Annoy recall = 100%
18:01:38 Commencing smooth kNN distance calibration using 1 thread
18:01:58 Initializing from normalized Laplacian + noise
18:01:58 Commencing optimization for 500 epochs, with 195022 positive edges
18:02:12 Optimization finished
[1] "144 0.03"18:02:12 UMAP embedding parameters a = 1.822 b = 0.8212
18:02:12 Read 1203 rows and found 38 numeric columns
18:02:12 Using Annoy for neighbor search, n_neighbors = 144
18:02:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:02:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c330fe5
18:02:13 Searching Annoy index using 1 thread, search_k = 14400
18:02:14 Annoy recall = 100%
18:02:24 Commencing smooth kNN distance calibration using 1 thread
18:02:44 Initializing from normalized Laplacian + noise
18:02:44 Commencing optimization for 500 epochs, with 195022 positive edges
18:02:58 Optimization finished
[1] "144 0.04"18:02:58 UMAP embedding parameters a = 1.786 b = 0.8316
18:02:58 Read 1203 rows and found 38 numeric columns
18:02:58 Using Annoy for neighbor search, n_neighbors = 144
18:02:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:02:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713480151
18:02:59 Searching Annoy index using 1 thread, search_k = 14400
18:03:00 Annoy recall = 100%
18:03:10 Commencing smooth kNN distance calibration using 1 thread
18:03:30 Initializing from normalized Laplacian + noise
18:03:30 Commencing optimization for 500 epochs, with 195022 positive edges
18:03:44 Optimization finished
[1] "144 0.05"18:03:44 UMAP embedding parameters a = 1.75 b = 0.8421
18:03:44 Read 1203 rows and found 38 numeric columns
18:03:44 Using Annoy for neighbor search, n_neighbors = 144
18:03:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:03:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737f7a5a7
18:03:45 Searching Annoy index using 1 thread, search_k = 14400
18:03:46 Annoy recall = 100%
18:03:56 Commencing smooth kNN distance calibration using 1 thread
18:04:16 Initializing from normalized Laplacian + noise
18:04:16 Commencing optimization for 500 epochs, with 195022 positive edges
18:04:30 Optimization finished
[1] "144 0.06"18:04:30 UMAP embedding parameters a = 1.715 b = 0.8526
18:04:30 Read 1203 rows and found 38 numeric columns
18:04:30 Using Annoy for neighbor search, n_neighbors = 144
18:04:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:04:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876da16c56
18:04:31 Searching Annoy index using 1 thread, search_k = 14400
18:04:32 Annoy recall = 100%
18:04:42 Commencing smooth kNN distance calibration using 1 thread
18:05:02 Initializing from normalized Laplacian + noise
18:05:02 Commencing optimization for 500 epochs, with 195022 positive edges
18:05:16 Optimization finished
[1] "144 0.07"18:05:16 UMAP embedding parameters a = 1.68 b = 0.8631
18:05:16 Read 1203 rows and found 38 numeric columns
18:05:16 Using Annoy for neighbor search, n_neighbors = 144
18:05:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:05:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c4520ac
18:05:17 Searching Annoy index using 1 thread, search_k = 14400
18:05:18 Annoy recall = 100%
18:05:28 Commencing smooth kNN distance calibration using 1 thread
18:05:48 Initializing from normalized Laplacian + noise
18:05:48 Commencing optimization for 500 epochs, with 195022 positive edges
18:06:02 Optimization finished
[1] "144 0.08"18:06:02 UMAP embedding parameters a = 1.645 b = 0.8737
18:06:02 Read 1203 rows and found 38 numeric columns
18:06:02 Using Annoy for neighbor search, n_neighbors = 144
18:06:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:06:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87657fd3da
18:06:03 Searching Annoy index using 1 thread, search_k = 14400
18:06:04 Annoy recall = 100%
18:06:14 Commencing smooth kNN distance calibration using 1 thread
18:06:34 Initializing from normalized Laplacian + noise
18:06:34 Commencing optimization for 500 epochs, with 195022 positive edges
18:06:48 Optimization finished
[1] "144 0.09"18:06:48 UMAP embedding parameters a = 1.611 b = 0.8844
18:06:48 Read 1203 rows and found 38 numeric columns
18:06:48 Using Annoy for neighbor search, n_neighbors = 144
18:06:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:06:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776674f5b
18:06:49 Searching Annoy index using 1 thread, search_k = 14400
18:06:50 Annoy recall = 100%
18:07:00 Commencing smooth kNN distance calibration using 1 thread
18:07:20 Initializing from normalized Laplacian + noise
18:07:20 Commencing optimization for 500 epochs, with 195022 positive edges
18:07:34 Optimization finished
[1] "144 0.1"18:07:34 UMAP embedding parameters a = 1.577 b = 0.8951
18:07:34 Read 1203 rows and found 38 numeric columns
18:07:34 Using Annoy for neighbor search, n_neighbors = 144
18:07:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:07:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871fb3cba6
18:07:35 Searching Annoy index using 1 thread, search_k = 14400
18:07:36 Annoy recall = 100%
18:07:46 Commencing smooth kNN distance calibration using 1 thread
18:08:06 Initializing from normalized Laplacian + noise
18:08:06 Commencing optimization for 500 epochs, with 195022 positive edges
18:08:20 Optimization finished
[1] "144 0.11"18:08:20 UMAP embedding parameters a = 1.544 b = 0.9058
18:08:20 Read 1203 rows and found 38 numeric columns
18:08:20 Using Annoy for neighbor search, n_neighbors = 144
18:08:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:08:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fb70590
18:08:21 Searching Annoy index using 1 thread, search_k = 14400
18:08:22 Annoy recall = 100%
18:08:32 Commencing smooth kNN distance calibration using 1 thread
18:08:52 Initializing from normalized Laplacian + noise
18:08:52 Commencing optimization for 500 epochs, with 195022 positive edges
18:09:06 Optimization finished
[1] "144 0.12"18:09:06 UMAP embedding parameters a = 1.51 b = 0.9165
18:09:06 Read 1203 rows and found 38 numeric columns
18:09:06 Using Annoy for neighbor search, n_neighbors = 144
18:09:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:09:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a49830c
18:09:07 Searching Annoy index using 1 thread, search_k = 14400
18:09:08 Annoy recall = 100%
18:09:18 Commencing smooth kNN distance calibration using 1 thread
18:09:38 Initializing from normalized Laplacian + noise
18:09:38 Commencing optimization for 500 epochs, with 195022 positive edges
18:09:52 Optimization finished
[1] "144 0.13"18:09:52 UMAP embedding parameters a = 1.478 b = 0.9272
18:09:52 Read 1203 rows and found 38 numeric columns
18:09:52 Using Annoy for neighbor search, n_neighbors = 144
18:09:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:09:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a735857
18:09:53 Searching Annoy index using 1 thread, search_k = 14400
18:09:54 Annoy recall = 100%
18:10:04 Commencing smooth kNN distance calibration using 1 thread
18:10:24 Initializing from normalized Laplacian + noise
18:10:24 Commencing optimization for 500 epochs, with 195022 positive edges
18:10:38 Optimization finished
[1] "144 0.14"18:10:38 UMAP embedding parameters a = 1.446 b = 0.938
18:10:38 Read 1203 rows and found 38 numeric columns
18:10:38 Using Annoy for neighbor search, n_neighbors = 144
18:10:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:10:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f220916
18:10:39 Searching Annoy index using 1 thread, search_k = 14400
18:10:40 Annoy recall = 100%
18:10:50 Commencing smooth kNN distance calibration using 1 thread
18:11:10 Initializing from normalized Laplacian + noise
18:11:10 Commencing optimization for 500 epochs, with 195022 positive edges
18:11:24 Optimization finished
[1] "144 0.15"18:11:25 UMAP embedding parameters a = 1.414 b = 0.9488
18:11:25 Read 1203 rows and found 38 numeric columns
18:11:25 Using Annoy for neighbor search, n_neighbors = 144
18:11:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:11:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87516bda32
18:11:25 Searching Annoy index using 1 thread, search_k = 14400
18:11:26 Annoy recall = 100%
18:11:36 Commencing smooth kNN distance calibration using 1 thread
18:11:56 Initializing from normalized Laplacian + noise
18:11:56 Commencing optimization for 500 epochs, with 195022 positive edges
18:12:10 Optimization finished
[1] "144 0.16"18:12:10 UMAP embedding parameters a = 1.383 b = 0.9596
18:12:10 Read 1203 rows and found 38 numeric columns
18:12:11 Using Annoy for neighbor search, n_neighbors = 144
18:12:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:12:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872153f115
18:12:11 Searching Annoy index using 1 thread, search_k = 14400
18:12:12 Annoy recall = 100%
18:12:22 Commencing smooth kNN distance calibration using 1 thread
18:12:42 Initializing from normalized Laplacian + noise
18:12:42 Commencing optimization for 500 epochs, with 195022 positive edges
18:12:56 Optimization finished
[1] "144 0.17"18:12:57 UMAP embedding parameters a = 1.352 b = 0.9704
18:12:57 Read 1203 rows and found 38 numeric columns
18:12:57 Using Annoy for neighbor search, n_neighbors = 144
18:12:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:12:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cc4b501
18:12:57 Searching Annoy index using 1 thread, search_k = 14400
18:12:58 Annoy recall = 100%
18:13:08 Commencing smooth kNN distance calibration using 1 thread
18:13:28 Initializing from normalized Laplacian + noise
18:13:28 Commencing optimization for 500 epochs, with 195022 positive edges
18:13:43 Optimization finished
[1] "144 0.18"18:13:43 UMAP embedding parameters a = 1.321 b = 0.9813
18:13:43 Read 1203 rows and found 38 numeric columns
18:13:43 Using Annoy for neighbor search, n_neighbors = 144
18:13:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:13:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875df03a8e
18:13:44 Searching Annoy index using 1 thread, search_k = 14400
18:13:45 Annoy recall = 100%
18:13:54 Commencing smooth kNN distance calibration using 1 thread
18:14:14 Initializing from normalized Laplacian + noise
18:14:15 Commencing optimization for 500 epochs, with 195022 positive edges
18:14:29 Optimization finished
[1] "144 0.19"18:14:29 UMAP embedding parameters a = 1.292 b = 0.9921
18:14:29 Read 1203 rows and found 38 numeric columns
18:14:29 Using Annoy for neighbor search, n_neighbors = 144
18:14:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:14:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f99856f
18:14:30 Searching Annoy index using 1 thread, search_k = 14400
18:14:31 Annoy recall = 100%
18:14:41 Commencing smooth kNN distance calibration using 1 thread
18:15:01 Initializing from normalized Laplacian + noise
18:15:01 Commencing optimization for 500 epochs, with 195022 positive edges
18:15:15 Optimization finished
[1] "144 0.2"18:15:15 UMAP embedding parameters a = 1.262 b = 1.003
18:15:15 Read 1203 rows and found 38 numeric columns
18:15:15 Using Annoy for neighbor search, n_neighbors = 144
18:15:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:15:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874634fbe0
18:15:16 Searching Annoy index using 1 thread, search_k = 14400
18:15:17 Annoy recall = 100%
18:15:27 Commencing smooth kNN distance calibration using 1 thread
18:15:47 Initializing from normalized Laplacian + noise
18:15:47 Commencing optimization for 500 epochs, with 195022 positive edges
18:16:01 Optimization finished
[1] "145 0"18:16:01 UMAP embedding parameters a = 1.933 b = 0.7905
18:16:01 Read 1203 rows and found 38 numeric columns
18:16:01 Using Annoy for neighbor search, n_neighbors = 145
18:16:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:16:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fc2fb50
18:16:02 Searching Annoy index using 1 thread, search_k = 14500
18:16:03 Annoy recall = 100%
18:16:13 Commencing smooth kNN distance calibration using 1 thread
18:16:32 Initializing from normalized Laplacian + noise
18:16:33 Commencing optimization for 500 epochs, with 196270 positive edges
18:16:47 Optimization finished
[1] "145 0.01"18:16:47 UMAP embedding parameters a = 1.896 b = 0.8006
18:16:47 Read 1203 rows and found 38 numeric columns
18:16:47 Using Annoy for neighbor search, n_neighbors = 145
18:16:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:16:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762785472
18:16:48 Searching Annoy index using 1 thread, search_k = 14500
18:16:49 Annoy recall = 100%
18:16:58 Commencing smooth kNN distance calibration using 1 thread
18:17:18 Initializing from normalized Laplacian + noise
18:17:18 Commencing optimization for 500 epochs, with 196270 positive edges
18:17:32 Optimization finished
[1] "145 0.02"18:17:32 UMAP embedding parameters a = 1.859 b = 0.8109
18:17:32 Read 1203 rows and found 38 numeric columns
18:17:32 Using Annoy for neighbor search, n_neighbors = 145
18:17:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:17:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fc80c13
18:17:33 Searching Annoy index using 1 thread, search_k = 14500
18:17:34 Annoy recall = 100%
18:17:44 Commencing smooth kNN distance calibration using 1 thread
18:18:03 Initializing from normalized Laplacian + noise
18:18:03 Commencing optimization for 500 epochs, with 196270 positive edges
18:18:17 Optimization finished
[1] "145 0.03"18:18:18 UMAP embedding parameters a = 1.822 b = 0.8212
18:18:18 Read 1203 rows and found 38 numeric columns
18:18:18 Using Annoy for neighbor search, n_neighbors = 145
18:18:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:18:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876032fac8
18:18:18 Searching Annoy index using 1 thread, search_k = 14500
18:18:19 Annoy recall = 100%
18:18:29 Commencing smooth kNN distance calibration using 1 thread
18:18:49 Initializing from normalized Laplacian + noise
18:18:49 Commencing optimization for 500 epochs, with 196270 positive edges
18:19:03 Optimization finished
[1] "145 0.04"18:19:03 UMAP embedding parameters a = 1.786 b = 0.8316
18:19:03 Read 1203 rows and found 38 numeric columns
18:19:03 Using Annoy for neighbor search, n_neighbors = 145
18:19:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:19:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a4a3465
18:19:04 Searching Annoy index using 1 thread, search_k = 14500
18:19:05 Annoy recall = 100%
18:19:15 Commencing smooth kNN distance calibration using 1 thread
18:19:35 Initializing from normalized Laplacian + noise
18:19:35 Commencing optimization for 500 epochs, with 196270 positive edges
18:19:49 Optimization finished
[1] "145 0.05"18:19:49 UMAP embedding parameters a = 1.75 b = 0.8421
18:19:49 Read 1203 rows and found 38 numeric columns
18:19:49 Using Annoy for neighbor search, n_neighbors = 145
18:19:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:19:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876722e12
18:19:50 Searching Annoy index using 1 thread, search_k = 14500
18:19:51 Annoy recall = 100%
18:20:01 Commencing smooth kNN distance calibration using 1 thread
18:20:20 Initializing from normalized Laplacian + noise
18:20:20 Commencing optimization for 500 epochs, with 196270 positive edges
18:20:34 Optimization finished
[1] "145 0.06"18:20:34 UMAP embedding parameters a = 1.715 b = 0.8526
18:20:34 Read 1203 rows and found 38 numeric columns
18:20:34 Using Annoy for neighbor search, n_neighbors = 145
18:20:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:20:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f1faf40
18:20:35 Searching Annoy index using 1 thread, search_k = 14500
18:20:36 Annoy recall = 100%
18:20:46 Commencing smooth kNN distance calibration using 1 thread
18:21:06 Initializing from normalized Laplacian + noise
18:21:06 Commencing optimization for 500 epochs, with 196270 positive edges
18:21:20 Optimization finished
[1] "145 0.07"18:21:20 UMAP embedding parameters a = 1.68 b = 0.8631
18:21:20 Read 1203 rows and found 38 numeric columns
18:21:20 Using Annoy for neighbor search, n_neighbors = 145
18:21:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:21:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87597f5
18:21:21 Searching Annoy index using 1 thread, search_k = 14500
18:21:22 Annoy recall = 100%
18:21:31 Commencing smooth kNN distance calibration using 1 thread
18:21:51 Initializing from normalized Laplacian + noise
18:21:51 Commencing optimization for 500 epochs, with 196270 positive edges
18:22:05 Optimization finished
[1] "145 0.08"18:22:06 UMAP embedding parameters a = 1.645 b = 0.8737
18:22:06 Read 1203 rows and found 38 numeric columns
18:22:06 Using Annoy for neighbor search, n_neighbors = 145
18:22:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:22:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715efb058
18:22:06 Searching Annoy index using 1 thread, search_k = 14500
18:22:07 Annoy recall = 100%
18:22:17 Commencing smooth kNN distance calibration using 1 thread
18:22:37 Initializing from normalized Laplacian + noise
18:22:37 Commencing optimization for 500 epochs, with 196270 positive edges
18:22:51 Optimization finished
[1] "145 0.09"18:22:51 UMAP embedding parameters a = 1.611 b = 0.8844
18:22:51 Read 1203 rows and found 38 numeric columns
18:22:51 Using Annoy for neighbor search, n_neighbors = 145
18:22:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:22:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87435badcc
18:22:52 Searching Annoy index using 1 thread, search_k = 14500
18:22:53 Annoy recall = 100%
18:23:03 Commencing smooth kNN distance calibration using 1 thread
18:23:22 Initializing from normalized Laplacian + noise
18:23:23 Commencing optimization for 500 epochs, with 196270 positive edges
18:23:37 Optimization finished
[1] "145 0.1"18:23:37 UMAP embedding parameters a = 1.577 b = 0.8951
18:23:37 Read 1203 rows and found 38 numeric columns
18:23:37 Using Annoy for neighbor search, n_neighbors = 145
18:23:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:23:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f4fd639
18:23:37 Searching Annoy index using 1 thread, search_k = 14500
18:23:39 Annoy recall = 100%
18:23:48 Commencing smooth kNN distance calibration using 1 thread
18:24:08 Initializing from normalized Laplacian + noise
18:24:08 Commencing optimization for 500 epochs, with 196270 positive edges
18:24:22 Optimization finished
[1] "145 0.11"18:24:23 UMAP embedding parameters a = 1.544 b = 0.9058
18:24:23 Read 1203 rows and found 38 numeric columns
18:24:23 Using Annoy for neighbor search, n_neighbors = 145
18:24:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:24:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875aa30bcd
18:24:23 Searching Annoy index using 1 thread, search_k = 14500
18:24:24 Annoy recall = 100%
18:24:34 Commencing smooth kNN distance calibration using 1 thread
18:24:54 Initializing from normalized Laplacian + noise
18:24:54 Commencing optimization for 500 epochs, with 196270 positive edges
18:25:08 Optimization finished
[1] "145 0.12"18:25:08 UMAP embedding parameters a = 1.51 b = 0.9165
18:25:08 Read 1203 rows and found 38 numeric columns
18:25:08 Using Annoy for neighbor search, n_neighbors = 145
18:25:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:25:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87334292d
18:25:09 Searching Annoy index using 1 thread, search_k = 14500
18:25:10 Annoy recall = 100%
18:25:20 Commencing smooth kNN distance calibration using 1 thread
18:25:39 Initializing from normalized Laplacian + noise
18:25:39 Commencing optimization for 500 epochs, with 196270 positive edges
18:25:53 Optimization finished
[1] "145 0.13"18:25:54 UMAP embedding parameters a = 1.478 b = 0.9272
18:25:54 Read 1203 rows and found 38 numeric columns
18:25:54 Using Annoy for neighbor search, n_neighbors = 145
18:25:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:25:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b82e61e
18:25:54 Searching Annoy index using 1 thread, search_k = 14500
18:25:55 Annoy recall = 100%
18:26:05 Commencing smooth kNN distance calibration using 1 thread
18:26:25 Initializing from normalized Laplacian + noise
18:26:25 Commencing optimization for 500 epochs, with 196270 positive edges
18:26:39 Optimization finished
[1] "145 0.14"18:26:39 UMAP embedding parameters a = 1.446 b = 0.938
18:26:39 Read 1203 rows and found 38 numeric columns
18:26:39 Using Annoy for neighbor search, n_neighbors = 145
18:26:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:26:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876deb0d1f
18:26:40 Searching Annoy index using 1 thread, search_k = 14500
18:26:41 Annoy recall = 100%
18:26:51 Commencing smooth kNN distance calibration using 1 thread
18:27:11 Initializing from normalized Laplacian + noise
18:27:11 Commencing optimization for 500 epochs, with 196270 positive edges
18:27:25 Optimization finished
[1] "145 0.15"18:27:25 UMAP embedding parameters a = 1.414 b = 0.9488
18:27:25 Read 1203 rows and found 38 numeric columns
18:27:25 Using Annoy for neighbor search, n_neighbors = 145
18:27:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:27:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b2bced5
18:27:26 Searching Annoy index using 1 thread, search_k = 14500
18:27:27 Annoy recall = 100%
18:27:37 Commencing smooth kNN distance calibration using 1 thread
18:27:56 Initializing from normalized Laplacian + noise
18:27:57 Commencing optimization for 500 epochs, with 196270 positive edges
18:28:11 Optimization finished
[1] "145 0.16"18:28:11 UMAP embedding parameters a = 1.383 b = 0.9596
18:28:11 Read 1203 rows and found 38 numeric columns
18:28:11 Using Annoy for neighbor search, n_neighbors = 145
18:28:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:28:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719245274
18:28:12 Searching Annoy index using 1 thread, search_k = 14500
18:28:13 Annoy recall = 100%
18:28:22 Commencing smooth kNN distance calibration using 1 thread
18:28:42 Initializing from normalized Laplacian + noise
18:28:42 Commencing optimization for 500 epochs, with 196270 positive edges
18:28:56 Optimization finished
[1] "145 0.17"18:28:57 UMAP embedding parameters a = 1.352 b = 0.9704
18:28:57 Read 1203 rows and found 38 numeric columns
18:28:57 Using Annoy for neighbor search, n_neighbors = 145
18:28:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:28:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a302dcb
18:28:57 Searching Annoy index using 1 thread, search_k = 14500
18:28:58 Annoy recall = 100%
18:29:08 Commencing smooth kNN distance calibration using 1 thread
18:29:28 Initializing from normalized Laplacian + noise
18:29:28 Commencing optimization for 500 epochs, with 196270 positive edges
18:29:42 Optimization finished
[1] "145 0.18"18:29:42 UMAP embedding parameters a = 1.321 b = 0.9813
18:29:42 Read 1203 rows and found 38 numeric columns
18:29:42 Using Annoy for neighbor search, n_neighbors = 145
18:29:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:29:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720aba2af
18:29:43 Searching Annoy index using 1 thread, search_k = 14500
18:29:44 Annoy recall = 100%
18:29:54 Commencing smooth kNN distance calibration using 1 thread
18:30:14 Initializing from normalized Laplacian + noise
18:30:14 Commencing optimization for 500 epochs, with 196270 positive edges
18:30:28 Optimization finished
[1] "145 0.19"18:30:28 UMAP embedding parameters a = 1.292 b = 0.9921
18:30:28 Read 1203 rows and found 38 numeric columns
18:30:28 Using Annoy for neighbor search, n_neighbors = 145
18:30:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:30:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f8ba1cf
18:30:29 Searching Annoy index using 1 thread, search_k = 14500
18:30:30 Annoy recall = 100%
18:30:40 Commencing smooth kNN distance calibration using 1 thread
18:30:59 Initializing from normalized Laplacian + noise
18:31:00 Commencing optimization for 500 epochs, with 196270 positive edges
18:31:14 Optimization finished
[1] "145 0.2"18:31:14 UMAP embedding parameters a = 1.262 b = 1.003
18:31:14 Read 1203 rows and found 38 numeric columns
18:31:14 Using Annoy for neighbor search, n_neighbors = 145
18:31:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:31:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769e3f972
18:31:15 Searching Annoy index using 1 thread, search_k = 14500
18:31:16 Annoy recall = 100%
18:31:26 Commencing smooth kNN distance calibration using 1 thread
18:31:45 Initializing from normalized Laplacian + noise
18:31:45 Commencing optimization for 500 epochs, with 196270 positive edges
18:31:59 Optimization finished
[1] "146 0"18:32:00 UMAP embedding parameters a = 1.933 b = 0.7905
18:32:00 Read 1203 rows and found 38 numeric columns
18:32:00 Using Annoy for neighbor search, n_neighbors = 146
18:32:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:32:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876062a83f
18:32:00 Searching Annoy index using 1 thread, search_k = 14600
18:32:01 Annoy recall = 100%
18:32:11 Commencing smooth kNN distance calibration using 1 thread
18:32:31 Initializing from normalized Laplacian + noise
18:32:31 Commencing optimization for 500 epochs, with 197528 positive edges
18:32:45 Optimization finished
[1] "146 0.01"18:32:46 UMAP embedding parameters a = 1.896 b = 0.8006
18:32:46 Read 1203 rows and found 38 numeric columns
18:32:46 Using Annoy for neighbor search, n_neighbors = 146
18:32:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:32:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749d524dc
18:32:46 Searching Annoy index using 1 thread, search_k = 14600
18:32:47 Annoy recall = 100%
18:32:57 Commencing smooth kNN distance calibration using 1 thread
18:33:17 Initializing from normalized Laplacian + noise
18:33:17 Commencing optimization for 500 epochs, with 197528 positive edges
18:33:31 Optimization finished
[1] "146 0.02"18:33:31 UMAP embedding parameters a = 1.859 b = 0.8109
18:33:31 Read 1203 rows and found 38 numeric columns
18:33:31 Using Annoy for neighbor search, n_neighbors = 146
18:33:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:33:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87745751c9
18:33:32 Searching Annoy index using 1 thread, search_k = 14600
18:33:33 Annoy recall = 100%
18:33:43 Commencing smooth kNN distance calibration using 1 thread
18:34:03 Initializing from normalized Laplacian + noise
18:34:03 Commencing optimization for 500 epochs, with 197528 positive edges
18:34:17 Optimization finished
[1] "146 0.03"18:34:17 UMAP embedding parameters a = 1.822 b = 0.8212
18:34:17 Read 1203 rows and found 38 numeric columns
18:34:17 Using Annoy for neighbor search, n_neighbors = 146
18:34:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:34:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f84b155
18:34:18 Searching Annoy index using 1 thread, search_k = 14600
18:34:19 Annoy recall = 100%
18:34:29 Commencing smooth kNN distance calibration using 1 thread
18:34:49 Initializing from normalized Laplacian + noise
18:34:49 Commencing optimization for 500 epochs, with 197528 positive edges
18:35:03 Optimization finished
[1] "146 0.04"18:35:03 UMAP embedding parameters a = 1.786 b = 0.8316
18:35:03 Read 1203 rows and found 38 numeric columns
18:35:03 Using Annoy for neighbor search, n_neighbors = 146
18:35:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:35:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b40ff0e
18:35:04 Searching Annoy index using 1 thread, search_k = 14600
18:35:05 Annoy recall = 100%
18:35:15 Commencing smooth kNN distance calibration using 1 thread
18:35:35 Initializing from normalized Laplacian + noise
18:35:35 Commencing optimization for 500 epochs, with 197528 positive edges
18:35:49 Optimization finished
[1] "146 0.05"18:35:49 UMAP embedding parameters a = 1.75 b = 0.8421
18:35:49 Read 1203 rows and found 38 numeric columns
18:35:49 Using Annoy for neighbor search, n_neighbors = 146
18:35:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:35:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715ab42de
18:35:50 Searching Annoy index using 1 thread, search_k = 14600
18:35:51 Annoy recall = 100%
18:36:00 Commencing smooth kNN distance calibration using 1 thread
18:36:20 Initializing from normalized Laplacian + noise
18:36:20 Commencing optimization for 500 epochs, with 197528 positive edges
18:36:35 Optimization finished
[1] "146 0.06"18:36:35 UMAP embedding parameters a = 1.715 b = 0.8526
18:36:35 Read 1203 rows and found 38 numeric columns
18:36:35 Using Annoy for neighbor search, n_neighbors = 146
18:36:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:36:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c496656
18:36:36 Searching Annoy index using 1 thread, search_k = 14600
18:36:37 Annoy recall = 100%
18:36:47 Commencing smooth kNN distance calibration using 1 thread
18:37:06 Initializing from normalized Laplacian + noise
18:37:06 Commencing optimization for 500 epochs, with 197528 positive edges
18:37:20 Optimization finished
[1] "146 0.07"18:37:21 UMAP embedding parameters a = 1.68 b = 0.8631
18:37:21 Read 1203 rows and found 38 numeric columns
18:37:21 Using Annoy for neighbor search, n_neighbors = 146
18:37:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:37:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877931399c
18:37:21 Searching Annoy index using 1 thread, search_k = 14600
18:37:22 Annoy recall = 100%
18:37:32 Commencing smooth kNN distance calibration using 1 thread
18:37:52 Initializing from normalized Laplacian + noise
18:37:52 Commencing optimization for 500 epochs, with 197528 positive edges
18:38:06 Optimization finished
[1] "146 0.08"18:38:07 UMAP embedding parameters a = 1.645 b = 0.8737
18:38:07 Read 1203 rows and found 38 numeric columns
18:38:07 Using Annoy for neighbor search, n_neighbors = 146
18:38:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:38:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875544c84e
18:38:07 Searching Annoy index using 1 thread, search_k = 14600
18:38:08 Annoy recall = 100%
18:38:18 Commencing smooth kNN distance calibration using 1 thread
18:38:38 Initializing from normalized Laplacian + noise
18:38:38 Commencing optimization for 500 epochs, with 197528 positive edges
18:38:52 Optimization finished
[1] "146 0.09"18:38:53 UMAP embedding parameters a = 1.611 b = 0.8844
18:38:53 Read 1203 rows and found 38 numeric columns
18:38:53 Using Annoy for neighbor search, n_neighbors = 146
18:38:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:38:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87527e6236
18:38:53 Searching Annoy index using 1 thread, search_k = 14600
18:38:54 Annoy recall = 100%
18:39:04 Commencing smooth kNN distance calibration using 1 thread
18:39:24 Initializing from normalized Laplacian + noise
18:39:24 Commencing optimization for 500 epochs, with 197528 positive edges
18:39:38 Optimization finished
[1] "146 0.1"18:39:39 UMAP embedding parameters a = 1.577 b = 0.8951
18:39:39 Read 1203 rows and found 38 numeric columns
18:39:39 Using Annoy for neighbor search, n_neighbors = 146
18:39:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:39:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878f434ec
18:39:39 Searching Annoy index using 1 thread, search_k = 14600
18:39:40 Annoy recall = 100%
18:39:50 Commencing smooth kNN distance calibration using 1 thread
18:40:10 Initializing from normalized Laplacian + noise
18:40:10 Commencing optimization for 500 epochs, with 197528 positive edges
18:40:24 Optimization finished
[1] "146 0.11"18:40:24 UMAP embedding parameters a = 1.544 b = 0.9058
18:40:24 Read 1203 rows and found 38 numeric columns
18:40:24 Using Annoy for neighbor search, n_neighbors = 146
18:40:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:40:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737bd1cc0
18:40:25 Searching Annoy index using 1 thread, search_k = 14600
18:40:26 Annoy recall = 100%
18:40:36 Commencing smooth kNN distance calibration using 1 thread
18:40:56 Initializing from normalized Laplacian + noise
18:40:56 Commencing optimization for 500 epochs, with 197528 positive edges
18:41:10 Optimization finished
[1] "146 0.12"18:41:11 UMAP embedding parameters a = 1.51 b = 0.9165
18:41:11 Read 1203 rows and found 38 numeric columns
18:41:11 Using Annoy for neighbor search, n_neighbors = 146
18:41:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:41:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872466e4a
18:41:11 Searching Annoy index using 1 thread, search_k = 14600
18:41:12 Annoy recall = 100%
18:41:22 Commencing smooth kNN distance calibration using 1 thread
18:41:42 Initializing from normalized Laplacian + noise
18:41:42 Commencing optimization for 500 epochs, with 197528 positive edges
18:41:56 Optimization finished
[1] "146 0.13"18:41:57 UMAP embedding parameters a = 1.478 b = 0.9272
18:41:57 Read 1203 rows and found 38 numeric columns
18:41:57 Using Annoy for neighbor search, n_neighbors = 146
18:41:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:41:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769272fb5
18:41:57 Searching Annoy index using 1 thread, search_k = 14600
18:41:58 Annoy recall = 100%
18:42:08 Commencing smooth kNN distance calibration using 1 thread
18:42:28 Initializing from normalized Laplacian + noise
18:42:28 Commencing optimization for 500 epochs, with 197528 positive edges
18:42:42 Optimization finished
[1] "146 0.14"18:42:42 UMAP embedding parameters a = 1.446 b = 0.938
18:42:42 Read 1203 rows and found 38 numeric columns
18:42:42 Using Annoy for neighbor search, n_neighbors = 146
18:42:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:42:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742075125
18:42:43 Searching Annoy index using 1 thread, search_k = 14600
18:42:44 Annoy recall = 100%
18:42:54 Commencing smooth kNN distance calibration using 1 thread
18:43:14 Initializing from normalized Laplacian + noise
18:43:14 Commencing optimization for 500 epochs, with 197528 positive edges
18:43:28 Optimization finished
[1] "146 0.15"18:43:28 UMAP embedding parameters a = 1.414 b = 0.9488
18:43:28 Read 1203 rows and found 38 numeric columns
18:43:28 Using Annoy for neighbor search, n_neighbors = 146
18:43:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:43:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878b89c5c
18:43:29 Searching Annoy index using 1 thread, search_k = 14600
18:43:30 Annoy recall = 100%
18:43:40 Commencing smooth kNN distance calibration using 1 thread
18:44:00 Initializing from normalized Laplacian + noise
18:44:00 Commencing optimization for 500 epochs, with 197528 positive edges
18:44:15 Optimization finished
[1] "146 0.16"18:44:15 UMAP embedding parameters a = 1.383 b = 0.9596
18:44:15 Read 1203 rows and found 38 numeric columns
18:44:15 Using Annoy for neighbor search, n_neighbors = 146
18:44:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:44:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876846def5
18:44:16 Searching Annoy index using 1 thread, search_k = 14600
18:44:17 Annoy recall = 100%
18:44:26 Commencing smooth kNN distance calibration using 1 thread
18:44:46 Initializing from normalized Laplacian + noise
18:44:46 Commencing optimization for 500 epochs, with 197528 positive edges
18:45:01 Optimization finished
[1] "146 0.17"18:45:01 UMAP embedding parameters a = 1.352 b = 0.9704
18:45:01 Read 1203 rows and found 38 numeric columns
18:45:01 Using Annoy for neighbor search, n_neighbors = 146
18:45:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:45:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87420ce91a
18:45:02 Searching Annoy index using 1 thread, search_k = 14600
18:45:03 Annoy recall = 100%
18:45:12 Commencing smooth kNN distance calibration using 1 thread
18:45:32 Initializing from normalized Laplacian + noise
18:45:32 Commencing optimization for 500 epochs, with 197528 positive edges
18:45:46 Optimization finished
[1] "146 0.18"18:45:47 UMAP embedding parameters a = 1.321 b = 0.9813
18:45:47 Read 1203 rows and found 38 numeric columns
18:45:47 Using Annoy for neighbor search, n_neighbors = 146
18:45:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:45:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ea84cb4
18:45:47 Searching Annoy index using 1 thread, search_k = 14600
18:45:48 Annoy recall = 100%
18:45:58 Commencing smooth kNN distance calibration using 1 thread
18:46:18 Initializing from normalized Laplacian + noise
18:46:19 Commencing optimization for 500 epochs, with 197528 positive edges
18:46:33 Optimization finished
[1] "146 0.19"18:46:33 UMAP embedding parameters a = 1.292 b = 0.9921
18:46:33 Read 1203 rows and found 38 numeric columns
18:46:33 Using Annoy for neighbor search, n_neighbors = 146
18:46:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:46:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ba28cc1
18:46:33 Searching Annoy index using 1 thread, search_k = 14600
18:46:34 Annoy recall = 100%
18:46:44 Commencing smooth kNN distance calibration using 1 thread
18:47:04 Initializing from normalized Laplacian + noise
18:47:05 Commencing optimization for 500 epochs, with 197528 positive edges
18:47:19 Optimization finished
[1] "146 0.2"18:47:19 UMAP embedding parameters a = 1.262 b = 1.003
18:47:19 Read 1203 rows and found 38 numeric columns
18:47:19 Using Annoy for neighbor search, n_neighbors = 146
18:47:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:47:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87215cbf53
18:47:20 Searching Annoy index using 1 thread, search_k = 14600
18:47:21 Annoy recall = 100%
18:47:31 Commencing smooth kNN distance calibration using 1 thread
18:47:50 Initializing from normalized Laplacian + noise
18:47:50 Commencing optimization for 500 epochs, with 197528 positive edges
18:48:05 Optimization finished
[1] "147 0"18:48:05 UMAP embedding parameters a = 1.933 b = 0.7905
18:48:05 Read 1203 rows and found 38 numeric columns
18:48:05 Using Annoy for neighbor search, n_neighbors = 147
18:48:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:48:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87794b5882
18:48:06 Searching Annoy index using 1 thread, search_k = 14700
18:48:07 Annoy recall = 100%
18:48:17 Commencing smooth kNN distance calibration using 1 thread
18:48:37 Initializing from normalized Laplacian + noise
18:48:37 Commencing optimization for 500 epochs, with 198760 positive edges
18:48:51 Optimization finished
[1] "147 0.01"18:48:51 UMAP embedding parameters a = 1.896 b = 0.8006
18:48:51 Read 1203 rows and found 38 numeric columns
18:48:51 Using Annoy for neighbor search, n_neighbors = 147
18:48:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:48:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ed6b5ef
18:48:52 Searching Annoy index using 1 thread, search_k = 14700
18:48:53 Annoy recall = 100%
18:49:03 Commencing smooth kNN distance calibration using 1 thread
18:49:23 Initializing from normalized Laplacian + noise
18:49:23 Commencing optimization for 500 epochs, with 198760 positive edges
18:49:37 Optimization finished
[1] "147 0.02"18:49:37 UMAP embedding parameters a = 1.859 b = 0.8109
18:49:37 Read 1203 rows and found 38 numeric columns
18:49:37 Using Annoy for neighbor search, n_neighbors = 147
18:49:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:49:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874cdfa571
18:49:38 Searching Annoy index using 1 thread, search_k = 14700
18:49:39 Annoy recall = 100%
18:49:49 Commencing smooth kNN distance calibration using 1 thread
18:50:09 Initializing from normalized Laplacian + noise
18:50:09 Commencing optimization for 500 epochs, with 198760 positive edges
18:50:23 Optimization finished
[1] "147 0.03"18:50:23 UMAP embedding parameters a = 1.822 b = 0.8212
18:50:23 Read 1203 rows and found 38 numeric columns
18:50:23 Using Annoy for neighbor search, n_neighbors = 147
18:50:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:50:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87673665a1
18:50:24 Searching Annoy index using 1 thread, search_k = 14700
18:50:25 Annoy recall = 100%
18:50:35 Commencing smooth kNN distance calibration using 1 thread
18:50:55 Initializing from normalized Laplacian + noise
18:50:55 Commencing optimization for 500 epochs, with 198760 positive edges
18:51:09 Optimization finished
[1] "147 0.04"18:51:09 UMAP embedding parameters a = 1.786 b = 0.8316
18:51:09 Read 1203 rows and found 38 numeric columns
18:51:09 Using Annoy for neighbor search, n_neighbors = 147
18:51:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:51:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a0284c4
18:51:10 Searching Annoy index using 1 thread, search_k = 14700
18:51:11 Annoy recall = 100%
18:51:21 Commencing smooth kNN distance calibration using 1 thread
18:51:41 Initializing from normalized Laplacian + noise
18:51:41 Commencing optimization for 500 epochs, with 198760 positive edges
18:51:55 Optimization finished
[1] "147 0.05"18:51:55 UMAP embedding parameters a = 1.75 b = 0.8421
18:51:55 Read 1203 rows and found 38 numeric columns
18:51:55 Using Annoy for neighbor search, n_neighbors = 147
18:51:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:51:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876603f7e5
18:51:56 Searching Annoy index using 1 thread, search_k = 14700
18:51:57 Annoy recall = 100%
18:52:07 Commencing smooth kNN distance calibration using 1 thread
18:52:27 Initializing from normalized Laplacian + noise
18:52:27 Commencing optimization for 500 epochs, with 198760 positive edges
18:52:42 Optimization finished
[1] "147 0.06"18:52:42 UMAP embedding parameters a = 1.715 b = 0.8526
18:52:42 Read 1203 rows and found 38 numeric columns
18:52:42 Using Annoy for neighbor search, n_neighbors = 147
18:52:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:52:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873166936c
18:52:43 Searching Annoy index using 1 thread, search_k = 14700
18:52:44 Annoy recall = 100%
18:52:53 Commencing smooth kNN distance calibration using 1 thread
18:53:13 Initializing from normalized Laplacian + noise
18:53:13 Commencing optimization for 500 epochs, with 198760 positive edges
18:53:28 Optimization finished
[1] "147 0.07"18:53:28 UMAP embedding parameters a = 1.68 b = 0.8631
18:53:28 Read 1203 rows and found 38 numeric columns
18:53:28 Using Annoy for neighbor search, n_neighbors = 147
18:53:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:53:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87aae2773
18:53:29 Searching Annoy index using 1 thread, search_k = 14700
18:53:30 Annoy recall = 100%
18:53:40 Commencing smooth kNN distance calibration using 1 thread
18:54:00 Initializing from normalized Laplacian + noise
18:54:00 Commencing optimization for 500 epochs, with 198760 positive edges
18:54:14 Optimization finished
[1] "147 0.08"18:54:15 UMAP embedding parameters a = 1.645 b = 0.8737
18:54:15 Read 1203 rows and found 38 numeric columns
18:54:15 Using Annoy for neighbor search, n_neighbors = 147
18:54:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:54:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87758f99b5
18:54:15 Searching Annoy index using 1 thread, search_k = 14700
18:54:16 Annoy recall = 100%
18:54:26 Commencing smooth kNN distance calibration using 1 thread
18:54:46 Initializing from normalized Laplacian + noise
18:54:47 Commencing optimization for 500 epochs, with 198760 positive edges
18:55:01 Optimization finished
[1] "147 0.09"18:55:01 UMAP embedding parameters a = 1.611 b = 0.8844
18:55:01 Read 1203 rows and found 38 numeric columns
18:55:01 Using Annoy for neighbor search, n_neighbors = 147
18:55:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:55:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b4a8cde
18:55:01 Searching Annoy index using 1 thread, search_k = 14700
18:55:03 Annoy recall = 100%
18:55:12 Commencing smooth kNN distance calibration using 1 thread
18:55:33 Initializing from normalized Laplacian + noise
18:55:33 Commencing optimization for 500 epochs, with 198760 positive edges
18:55:47 Optimization finished
[1] "147 0.1"18:55:47 UMAP embedding parameters a = 1.577 b = 0.8951
18:55:47 Read 1203 rows and found 38 numeric columns
18:55:47 Using Annoy for neighbor search, n_neighbors = 147
18:55:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:55:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b10cfb2
18:55:48 Searching Annoy index using 1 thread, search_k = 14700
18:55:49 Annoy recall = 100%
18:55:59 Commencing smooth kNN distance calibration using 1 thread
18:56:19 Initializing from normalized Laplacian + noise
18:56:19 Commencing optimization for 500 epochs, with 198760 positive edges
18:56:33 Optimization finished
[1] "147 0.11"18:56:33 UMAP embedding parameters a = 1.544 b = 0.9058
18:56:33 Read 1203 rows and found 38 numeric columns
18:56:33 Using Annoy for neighbor search, n_neighbors = 147
18:56:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:56:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f64be91
18:56:34 Searching Annoy index using 1 thread, search_k = 14700
18:56:35 Annoy recall = 100%
18:56:45 Commencing smooth kNN distance calibration using 1 thread
18:57:05 Initializing from normalized Laplacian + noise
18:57:05 Commencing optimization for 500 epochs, with 198760 positive edges
18:57:19 Optimization finished
[1] "147 0.12"18:57:19 UMAP embedding parameters a = 1.51 b = 0.9165
18:57:20 Read 1203 rows and found 38 numeric columns
18:57:20 Using Annoy for neighbor search, n_neighbors = 147
18:57:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:57:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fa1dea7
18:57:20 Searching Annoy index using 1 thread, search_k = 14700
18:57:21 Annoy recall = 100%
18:57:31 Commencing smooth kNN distance calibration using 1 thread
18:57:51 Initializing from normalized Laplacian + noise
18:57:52 Commencing optimization for 500 epochs, with 198760 positive edges
18:58:06 Optimization finished
[1] "147 0.13"18:58:06 UMAP embedding parameters a = 1.478 b = 0.9272
18:58:06 Read 1203 rows and found 38 numeric columns
18:58:06 Using Annoy for neighbor search, n_neighbors = 147
18:58:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:58:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a958107
18:58:07 Searching Annoy index using 1 thread, search_k = 14700
18:58:08 Annoy recall = 100%
18:58:17 Commencing smooth kNN distance calibration using 1 thread
18:58:38 Initializing from normalized Laplacian + noise
18:58:38 Commencing optimization for 500 epochs, with 198760 positive edges
18:58:52 Optimization finished
[1] "147 0.14"18:58:52 UMAP embedding parameters a = 1.446 b = 0.938
18:58:52 Read 1203 rows and found 38 numeric columns
18:58:52 Using Annoy for neighbor search, n_neighbors = 147
18:58:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:58:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875aa5bd9f
18:58:53 Searching Annoy index using 1 thread, search_k = 14700
18:58:54 Annoy recall = 100%
18:59:04 Commencing smooth kNN distance calibration using 1 thread
18:59:24 Initializing from normalized Laplacian + noise
18:59:24 Commencing optimization for 500 epochs, with 198760 positive edges
18:59:38 Optimization finished
[1] "147 0.15"18:59:38 UMAP embedding parameters a = 1.414 b = 0.9488
18:59:38 Read 1203 rows and found 38 numeric columns
18:59:38 Using Annoy for neighbor search, n_neighbors = 147
18:59:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:59:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87254d2186
18:59:39 Searching Annoy index using 1 thread, search_k = 14700
18:59:40 Annoy recall = 100%
18:59:50 Commencing smooth kNN distance calibration using 1 thread
19:00:10 Initializing from normalized Laplacian + noise
19:00:10 Commencing optimization for 500 epochs, with 198760 positive edges
19:00:24 Optimization finished
[1] "147 0.16"19:00:25 UMAP embedding parameters a = 1.383 b = 0.9596
19:00:25 Read 1203 rows and found 38 numeric columns
19:00:25 Using Annoy for neighbor search, n_neighbors = 147
19:00:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:00:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726dee75d
19:00:25 Searching Annoy index using 1 thread, search_k = 14700
19:00:26 Annoy recall = 100%
19:00:36 Commencing smooth kNN distance calibration using 1 thread
19:00:57 Initializing from normalized Laplacian + noise
19:00:57 Commencing optimization for 500 epochs, with 198760 positive edges
19:01:11 Optimization finished
[1] "147 0.17"19:01:11 UMAP embedding parameters a = 1.352 b = 0.9704
19:01:11 Read 1203 rows and found 38 numeric columns
19:01:11 Using Annoy for neighbor search, n_neighbors = 147
19:01:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:01:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753d6f73b
19:01:12 Searching Annoy index using 1 thread, search_k = 14700
19:01:13 Annoy recall = 100%
19:01:23 Commencing smooth kNN distance calibration using 1 thread
19:01:43 Initializing from normalized Laplacian + noise
19:01:43 Commencing optimization for 500 epochs, with 198760 positive edges
19:01:57 Optimization finished
[1] "147 0.18"19:01:57 UMAP embedding parameters a = 1.321 b = 0.9813
19:01:57 Read 1203 rows and found 38 numeric columns
19:01:57 Using Annoy for neighbor search, n_neighbors = 147
19:01:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:01:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a91e9d4
19:01:58 Searching Annoy index using 1 thread, search_k = 14700
19:01:59 Annoy recall = 100%
19:02:09 Commencing smooth kNN distance calibration using 1 thread
19:02:29 Initializing from normalized Laplacian + noise
19:02:29 Commencing optimization for 500 epochs, with 198760 positive edges
19:02:43 Optimization finished
[1] "147 0.19"19:02:44 UMAP embedding parameters a = 1.292 b = 0.9921
19:02:44 Read 1203 rows and found 38 numeric columns
19:02:44 Using Annoy for neighbor search, n_neighbors = 147
19:02:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:02:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87795d4993
19:02:44 Searching Annoy index using 1 thread, search_k = 14700
19:02:45 Annoy recall = 100%
19:02:55 Commencing smooth kNN distance calibration using 1 thread
19:03:16 Initializing from normalized Laplacian + noise
19:03:16 Commencing optimization for 500 epochs, with 198760 positive edges
19:03:30 Optimization finished
[1] "147 0.2"19:03:30 UMAP embedding parameters a = 1.262 b = 1.003
19:03:30 Read 1203 rows and found 38 numeric columns
19:03:30 Using Annoy for neighbor search, n_neighbors = 147
19:03:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:03:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ccb2c28
19:03:31 Searching Annoy index using 1 thread, search_k = 14700
19:03:32 Annoy recall = 100%
19:03:42 Commencing smooth kNN distance calibration using 1 thread
19:04:02 Initializing from normalized Laplacian + noise
19:04:02 Commencing optimization for 500 epochs, with 198760 positive edges
19:04:16 Optimization finished
[1] "148 0"19:04:17 UMAP embedding parameters a = 1.933 b = 0.7905
19:04:17 Read 1203 rows and found 38 numeric columns
19:04:17 Using Annoy for neighbor search, n_neighbors = 148
19:04:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:04:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87324f0694
19:04:17 Searching Annoy index using 1 thread, search_k = 14800
19:04:18 Annoy recall = 100%
19:04:28 Commencing smooth kNN distance calibration using 1 thread
19:04:48 Initializing from normalized Laplacian + noise
19:04:48 Commencing optimization for 500 epochs, with 199978 positive edges
19:05:03 Optimization finished
[1] "148 0.01"19:05:03 UMAP embedding parameters a = 1.896 b = 0.8006
19:05:03 Read 1203 rows and found 38 numeric columns
19:05:03 Using Annoy for neighbor search, n_neighbors = 148
19:05:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:05:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ba3b7dd
19:05:03 Searching Annoy index using 1 thread, search_k = 14800
19:05:05 Annoy recall = 100%
19:05:15 Commencing smooth kNN distance calibration using 1 thread
19:05:35 Initializing from normalized Laplacian + noise
19:05:35 Commencing optimization for 500 epochs, with 199978 positive edges
19:05:49 Optimization finished
[1] "148 0.02"19:05:49 UMAP embedding parameters a = 1.859 b = 0.8109
19:05:49 Read 1203 rows and found 38 numeric columns
19:05:49 Using Annoy for neighbor search, n_neighbors = 148
19:05:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:05:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745f25bdd
19:05:50 Searching Annoy index using 1 thread, search_k = 14800
19:05:51 Annoy recall = 100%
19:06:01 Commencing smooth kNN distance calibration using 1 thread
19:06:21 Initializing from normalized Laplacian + noise
19:06:21 Commencing optimization for 500 epochs, with 199978 positive edges
19:06:35 Optimization finished
[1] "148 0.03"19:06:36 UMAP embedding parameters a = 1.822 b = 0.8212
19:06:36 Read 1203 rows and found 38 numeric columns
19:06:36 Using Annoy for neighbor search, n_neighbors = 148
19:06:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:06:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87745657b9
19:06:36 Searching Annoy index using 1 thread, search_k = 14800
19:06:37 Annoy recall = 100%
19:06:47 Commencing smooth kNN distance calibration using 1 thread
19:07:07 Initializing from normalized Laplacian + noise
19:07:08 Commencing optimization for 500 epochs, with 199978 positive edges
19:07:22 Optimization finished
[1] "148 0.04"19:07:22 UMAP embedding parameters a = 1.786 b = 0.8316
19:07:22 Read 1203 rows and found 38 numeric columns
19:07:22 Using Annoy for neighbor search, n_neighbors = 148
19:07:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:07:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745c543a
19:07:23 Searching Annoy index using 1 thread, search_k = 14800
19:07:24 Annoy recall = 100%
19:07:34 Commencing smooth kNN distance calibration using 1 thread
19:07:54 Initializing from normalized Laplacian + noise
19:07:54 Commencing optimization for 500 epochs, with 199978 positive edges
19:08:08 Optimization finished
[1] "148 0.05"19:08:08 UMAP embedding parameters a = 1.75 b = 0.8421
19:08:08 Read 1203 rows and found 38 numeric columns
19:08:08 Using Annoy for neighbor search, n_neighbors = 148
19:08:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:08:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e393ad2
19:08:09 Searching Annoy index using 1 thread, search_k = 14800
19:08:10 Annoy recall = 100%
19:08:20 Commencing smooth kNN distance calibration using 1 thread
19:08:40 Initializing from normalized Laplacian + noise
19:08:41 Commencing optimization for 500 epochs, with 199978 positive edges
19:08:55 Optimization finished
[1] "148 0.06"19:08:55 UMAP embedding parameters a = 1.715 b = 0.8526
19:08:55 Read 1203 rows and found 38 numeric columns
19:08:55 Using Annoy for neighbor search, n_neighbors = 148
19:08:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:08:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87366340d3
19:08:56 Searching Annoy index using 1 thread, search_k = 14800
19:08:57 Annoy recall = 100%
19:09:07 Commencing smooth kNN distance calibration using 1 thread
19:09:27 Initializing from normalized Laplacian + noise
19:09:27 Commencing optimization for 500 epochs, with 199978 positive edges
19:09:41 Optimization finished
[1] "148 0.07"19:09:42 UMAP embedding parameters a = 1.68 b = 0.8631
19:09:42 Read 1203 rows and found 38 numeric columns
19:09:42 Using Annoy for neighbor search, n_neighbors = 148
19:09:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:09:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872304a0ee
19:09:42 Searching Annoy index using 1 thread, search_k = 14800
19:09:43 Annoy recall = 100%
19:09:53 Commencing smooth kNN distance calibration using 1 thread
19:10:13 Initializing from normalized Laplacian + noise
19:10:13 Commencing optimization for 500 epochs, with 199978 positive edges
19:10:28 Optimization finished
[1] "148 0.08"19:10:28 UMAP embedding parameters a = 1.645 b = 0.8737
19:10:28 Read 1203 rows and found 38 numeric columns
19:10:28 Using Annoy for neighbor search, n_neighbors = 148
19:10:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:10:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759dbc793
19:10:29 Searching Annoy index using 1 thread, search_k = 14800
19:10:30 Annoy recall = 100%
19:10:40 Commencing smooth kNN distance calibration using 1 thread
19:11:00 Initializing from normalized Laplacian + noise
19:11:00 Commencing optimization for 500 epochs, with 199978 positive edges
19:11:14 Optimization finished
[1] "148 0.09"19:11:14 UMAP embedding parameters a = 1.611 b = 0.8844
19:11:14 Read 1203 rows and found 38 numeric columns
19:11:14 Using Annoy for neighbor search, n_neighbors = 148
19:11:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:11:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757c00027
19:11:15 Searching Annoy index using 1 thread, search_k = 14800
19:11:16 Annoy recall = 100%
19:11:26 Commencing smooth kNN distance calibration using 1 thread
19:11:47 Initializing from normalized Laplacian + noise
19:11:47 Commencing optimization for 500 epochs, with 199978 positive edges
19:12:01 Optimization finished
[1] "148 0.1"19:12:01 UMAP embedding parameters a = 1.577 b = 0.8951
19:12:01 Read 1203 rows and found 38 numeric columns
19:12:01 Using Annoy for neighbor search, n_neighbors = 148
19:12:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:12:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c4ff970
19:12:02 Searching Annoy index using 1 thread, search_k = 14800
19:12:03 Annoy recall = 100%
19:12:13 Commencing smooth kNN distance calibration using 1 thread
19:12:33 Initializing from normalized Laplacian + noise
19:12:33 Commencing optimization for 500 epochs, with 199978 positive edges
19:12:47 Optimization finished
[1] "148 0.11"19:12:48 UMAP embedding parameters a = 1.544 b = 0.9058
19:12:48 Read 1203 rows and found 38 numeric columns
19:12:48 Using Annoy for neighbor search, n_neighbors = 148
19:12:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:12:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878b27d82
19:12:48 Searching Annoy index using 1 thread, search_k = 14800
19:12:49 Annoy recall = 100%
19:12:59 Commencing smooth kNN distance calibration using 1 thread
19:13:19 Initializing from normalized Laplacian + noise
19:13:20 Commencing optimization for 500 epochs, with 199978 positive edges
19:13:34 Optimization finished
[1] "148 0.12"19:13:34 UMAP embedding parameters a = 1.51 b = 0.9165
19:13:34 Read 1203 rows and found 38 numeric columns
19:13:34 Using Annoy for neighbor search, n_neighbors = 148
19:13:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:13:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87249fa598
19:13:35 Searching Annoy index using 1 thread, search_k = 14800
19:13:36 Annoy recall = 100%
19:13:46 Commencing smooth kNN distance calibration using 1 thread
19:14:06 Initializing from normalized Laplacian + noise
19:14:06 Commencing optimization for 500 epochs, with 199978 positive edges
19:14:21 Optimization finished
[1] "148 0.13"19:14:21 UMAP embedding parameters a = 1.478 b = 0.9272
19:14:21 Read 1203 rows and found 38 numeric columns
19:14:21 Using Annoy for neighbor search, n_neighbors = 148
19:14:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:14:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873865f11
19:14:21 Searching Annoy index using 1 thread, search_k = 14800
19:14:23 Annoy recall = 100%
19:14:33 Commencing smooth kNN distance calibration using 1 thread
19:14:53 Initializing from normalized Laplacian + noise
19:14:53 Commencing optimization for 500 epochs, with 199978 positive edges
19:15:07 Optimization finished
[1] "148 0.14"19:15:07 UMAP embedding parameters a = 1.446 b = 0.938
19:15:07 Read 1203 rows and found 38 numeric columns
19:15:07 Using Annoy for neighbor search, n_neighbors = 148
19:15:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:15:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772b50246
19:15:08 Searching Annoy index using 1 thread, search_k = 14800
19:15:09 Annoy recall = 100%
19:15:19 Commencing smooth kNN distance calibration using 1 thread
19:15:39 Initializing from normalized Laplacian + noise
19:15:39 Commencing optimization for 500 epochs, with 199978 positive edges
19:15:54 Optimization finished
[1] "148 0.15"19:15:54 UMAP embedding parameters a = 1.414 b = 0.9488
19:15:54 Read 1203 rows and found 38 numeric columns
19:15:54 Using Annoy for neighbor search, n_neighbors = 148
19:15:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:15:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87aa39d7e
19:15:55 Searching Annoy index using 1 thread, search_k = 14800
19:15:56 Annoy recall = 100%
19:16:06 Commencing smooth kNN distance calibration using 1 thread
19:16:26 Initializing from normalized Laplacian + noise
19:16:26 Commencing optimization for 500 epochs, with 199978 positive edges
19:16:40 Optimization finished
[1] "148 0.16"19:16:40 UMAP embedding parameters a = 1.383 b = 0.9596
19:16:41 Read 1203 rows and found 38 numeric columns
19:16:41 Using Annoy for neighbor search, n_neighbors = 148
19:16:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:16:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734ecf27e
19:16:41 Searching Annoy index using 1 thread, search_k = 14800
19:16:42 Annoy recall = 100%
19:16:52 Commencing smooth kNN distance calibration using 1 thread
19:17:13 Initializing from normalized Laplacian + noise
19:17:13 Commencing optimization for 500 epochs, with 199978 positive edges
19:17:27 Optimization finished
[1] "148 0.17"19:17:27 UMAP embedding parameters a = 1.352 b = 0.9704
19:17:27 Read 1203 rows and found 38 numeric columns
19:17:27 Using Annoy for neighbor search, n_neighbors = 148
19:17:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:17:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d6329b9
19:17:28 Searching Annoy index using 1 thread, search_k = 14800
19:17:29 Annoy recall = 100%
19:17:39 Commencing smooth kNN distance calibration using 1 thread
19:17:59 Initializing from normalized Laplacian + noise
19:17:59 Commencing optimization for 500 epochs, with 199978 positive edges
19:18:14 Optimization finished
[1] "148 0.18"19:18:14 UMAP embedding parameters a = 1.321 b = 0.9813
19:18:14 Read 1203 rows and found 38 numeric columns
19:18:14 Using Annoy for neighbor search, n_neighbors = 148
19:18:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:18:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87333733
19:18:15 Searching Annoy index using 1 thread, search_k = 14800
19:18:16 Annoy recall = 100%
19:18:26 Commencing smooth kNN distance calibration using 1 thread
19:18:46 Initializing from normalized Laplacian + noise
19:18:46 Commencing optimization for 500 epochs, with 199978 positive edges
19:19:00 Optimization finished
[1] "148 0.19"19:19:00 UMAP embedding parameters a = 1.292 b = 0.9921
19:19:01 Read 1203 rows and found 38 numeric columns
19:19:01 Using Annoy for neighbor search, n_neighbors = 148
19:19:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:19:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750377f5c
19:19:01 Searching Annoy index using 1 thread, search_k = 14800
19:19:02 Annoy recall = 100%
19:19:12 Commencing smooth kNN distance calibration using 1 thread
19:19:32 Initializing from normalized Laplacian + noise
19:19:33 Commencing optimization for 500 epochs, with 199978 positive edges
19:19:47 Optimization finished
[1] "148 0.2"19:19:47 UMAP embedding parameters a = 1.262 b = 1.003
19:19:47 Read 1203 rows and found 38 numeric columns
19:19:47 Using Annoy for neighbor search, n_neighbors = 148
19:19:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:19:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876873f96b
19:19:48 Searching Annoy index using 1 thread, search_k = 14800
19:19:49 Annoy recall = 100%
19:19:59 Commencing smooth kNN distance calibration using 1 thread
19:20:19 Initializing from normalized Laplacian + noise
19:20:19 Commencing optimization for 500 epochs, with 199978 positive edges
19:20:33 Optimization finished
[1] "149 0"19:20:34 UMAP embedding parameters a = 1.933 b = 0.7905
19:20:34 Read 1203 rows and found 38 numeric columns
19:20:34 Using Annoy for neighbor search, n_neighbors = 149
19:20:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:20:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f97f5c4
19:20:34 Searching Annoy index using 1 thread, search_k = 14900
19:20:35 Annoy recall = 100%
19:20:45 Commencing smooth kNN distance calibration using 1 thread
19:21:06 Initializing from normalized Laplacian + noise
19:21:06 Commencing optimization for 500 epochs, with 201184 positive edges
19:21:20 Optimization finished
[1] "149 0.01"19:21:21 UMAP embedding parameters a = 1.896 b = 0.8006
19:21:21 Read 1203 rows and found 38 numeric columns
19:21:21 Using Annoy for neighbor search, n_neighbors = 149
19:21:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:21:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fd95e04
19:21:21 Searching Annoy index using 1 thread, search_k = 14900
19:21:22 Annoy recall = 100%
19:21:32 Commencing smooth kNN distance calibration using 1 thread
19:21:52 Initializing from normalized Laplacian + noise
19:21:52 Commencing optimization for 500 epochs, with 201184 positive edges
19:22:07 Optimization finished
[1] "149 0.02"19:22:07 UMAP embedding parameters a = 1.859 b = 0.8109
19:22:07 Read 1203 rows and found 38 numeric columns
19:22:07 Using Annoy for neighbor search, n_neighbors = 149
19:22:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:22:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873097a72
19:22:08 Searching Annoy index using 1 thread, search_k = 14900
19:22:09 Annoy recall = 100%
19:22:19 Commencing smooth kNN distance calibration using 1 thread
19:22:39 Initializing from normalized Laplacian + noise
19:22:39 Commencing optimization for 500 epochs, with 201184 positive edges
19:22:54 Optimization finished
[1] "149 0.03"19:22:54 UMAP embedding parameters a = 1.822 b = 0.8212
19:22:54 Read 1203 rows and found 38 numeric columns
19:22:54 Using Annoy for neighbor search, n_neighbors = 149
19:22:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:22:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a3db363
19:22:55 Searching Annoy index using 1 thread, search_k = 14900
19:22:56 Annoy recall = 100%
19:23:06 Commencing smooth kNN distance calibration using 1 thread
19:23:26 Initializing from normalized Laplacian + noise
19:23:26 Commencing optimization for 500 epochs, with 201184 positive edges
19:23:40 Optimization finished
[1] "149 0.04"19:23:41 UMAP embedding parameters a = 1.786 b = 0.8316
19:23:41 Read 1203 rows and found 38 numeric columns
19:23:41 Using Annoy for neighbor search, n_neighbors = 149
19:23:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:23:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875267f8a
19:23:41 Searching Annoy index using 1 thread, search_k = 14900
19:23:42 Annoy recall = 100%
19:23:52 Commencing smooth kNN distance calibration using 1 thread
19:24:13 Initializing from normalized Laplacian + noise
19:24:13 Commencing optimization for 500 epochs, with 201184 positive edges
19:24:27 Optimization finished
[1] "149 0.05"19:24:27 UMAP embedding parameters a = 1.75 b = 0.8421
19:24:27 Read 1203 rows and found 38 numeric columns
19:24:27 Using Annoy for neighbor search, n_neighbors = 149
19:24:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:24:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729e861cf
19:24:28 Searching Annoy index using 1 thread, search_k = 14900
19:24:29 Annoy recall = 100%
19:24:39 Commencing smooth kNN distance calibration using 1 thread
19:24:59 Initializing from normalized Laplacian + noise
19:24:59 Commencing optimization for 500 epochs, with 201184 positive edges
19:25:14 Optimization finished
[1] "149 0.06"19:25:14 UMAP embedding parameters a = 1.715 b = 0.8526
19:25:14 Read 1203 rows and found 38 numeric columns
19:25:14 Using Annoy for neighbor search, n_neighbors = 149
19:25:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:25:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e14aa9e
19:25:15 Searching Annoy index using 1 thread, search_k = 14900
19:25:16 Annoy recall = 100%
19:25:26 Commencing smooth kNN distance calibration using 1 thread
19:25:46 Initializing from normalized Laplacian + noise
19:25:46 Commencing optimization for 500 epochs, with 201184 positive edges
19:26:01 Optimization finished
[1] "149 0.07"19:26:01 UMAP embedding parameters a = 1.68 b = 0.8631
19:26:01 Read 1203 rows and found 38 numeric columns
19:26:01 Using Annoy for neighbor search, n_neighbors = 149
19:26:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:26:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fb8695e
19:26:02 Searching Annoy index using 1 thread, search_k = 14900
19:26:03 Annoy recall = 100%
19:26:13 Commencing smooth kNN distance calibration using 1 thread
19:26:33 Initializing from normalized Laplacian + noise
19:26:33 Commencing optimization for 500 epochs, with 201184 positive edges
19:26:48 Optimization finished
[1] "149 0.08"19:26:48 UMAP embedding parameters a = 1.645 b = 0.8737
19:26:48 Read 1203 rows and found 38 numeric columns
19:26:48 Using Annoy for neighbor search, n_neighbors = 149
19:26:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:26:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872345ab63
19:26:49 Searching Annoy index using 1 thread, search_k = 14900
19:26:50 Annoy recall = 100%
19:26:59 Commencing smooth kNN distance calibration using 1 thread
19:27:20 Initializing from normalized Laplacian + noise
19:27:20 Commencing optimization for 500 epochs, with 201184 positive edges
19:27:34 Optimization finished
[1] "149 0.09"19:27:35 UMAP embedding parameters a = 1.611 b = 0.8844
19:27:35 Read 1203 rows and found 38 numeric columns
19:27:35 Using Annoy for neighbor search, n_neighbors = 149
19:27:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:27:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874adfd6c6
19:27:35 Searching Annoy index using 1 thread, search_k = 14900
19:27:36 Annoy recall = 100%
19:27:46 Commencing smooth kNN distance calibration using 1 thread
19:28:07 Initializing from normalized Laplacian + noise
19:28:07 Commencing optimization for 500 epochs, with 201184 positive edges
19:28:21 Optimization finished
[1] "149 0.1"19:28:21 UMAP embedding parameters a = 1.577 b = 0.8951
19:28:21 Read 1203 rows and found 38 numeric columns
19:28:21 Using Annoy for neighbor search, n_neighbors = 149
19:28:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:28:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732076ff2
19:28:22 Searching Annoy index using 1 thread, search_k = 14900
19:28:23 Annoy recall = 100%
19:28:33 Commencing smooth kNN distance calibration using 1 thread
19:28:54 Initializing from normalized Laplacian + noise
19:28:54 Commencing optimization for 500 epochs, with 201184 positive edges
19:29:08 Optimization finished
[1] "149 0.11"19:29:08 UMAP embedding parameters a = 1.544 b = 0.9058
19:29:08 Read 1203 rows and found 38 numeric columns
19:29:08 Using Annoy for neighbor search, n_neighbors = 149
19:29:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:29:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ee96340
19:29:09 Searching Annoy index using 1 thread, search_k = 14900
19:29:10 Annoy recall = 100%
19:29:20 Commencing smooth kNN distance calibration using 1 thread
19:29:40 Initializing from normalized Laplacian + noise
19:29:40 Commencing optimization for 500 epochs, with 201184 positive edges
19:29:55 Optimization finished
[1] "149 0.12"19:29:55 UMAP embedding parameters a = 1.51 b = 0.9165
19:29:55 Read 1203 rows and found 38 numeric columns
19:29:55 Using Annoy for neighbor search, n_neighbors = 149
19:29:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:29:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710d232a3
19:29:56 Searching Annoy index using 1 thread, search_k = 14900
19:29:57 Annoy recall = 100%
19:30:07 Commencing smooth kNN distance calibration using 1 thread
19:30:27 Initializing from normalized Laplacian + noise
19:30:27 Commencing optimization for 500 epochs, with 201184 positive edges
19:30:42 Optimization finished
[1] "149 0.13"19:30:42 UMAP embedding parameters a = 1.478 b = 0.9272
19:30:42 Read 1203 rows and found 38 numeric columns
19:30:42 Using Annoy for neighbor search, n_neighbors = 149
19:30:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:30:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87265dc7ab
19:30:43 Searching Annoy index using 1 thread, search_k = 14900
19:30:44 Annoy recall = 100%
19:30:54 Commencing smooth kNN distance calibration using 1 thread
19:31:14 Initializing from normalized Laplacian + noise
19:31:14 Commencing optimization for 500 epochs, with 201184 positive edges
19:31:28 Optimization finished
[1] "149 0.14"19:31:29 UMAP embedding parameters a = 1.446 b = 0.938
19:31:29 Read 1203 rows and found 38 numeric columns
19:31:29 Using Annoy for neighbor search, n_neighbors = 149
19:31:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:31:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872345b77a
19:31:29 Searching Annoy index using 1 thread, search_k = 14900
19:31:30 Annoy recall = 100%
19:31:41 Commencing smooth kNN distance calibration using 1 thread
19:32:01 Initializing from normalized Laplacian + noise
19:32:01 Commencing optimization for 500 epochs, with 201184 positive edges
19:32:15 Optimization finished
[1] "149 0.15"19:32:16 UMAP embedding parameters a = 1.414 b = 0.9488
19:32:16 Read 1203 rows and found 38 numeric columns
19:32:16 Using Annoy for neighbor search, n_neighbors = 149
19:32:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:32:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f0b6d75
19:32:16 Searching Annoy index using 1 thread, search_k = 14900
19:32:17 Annoy recall = 100%
19:32:27 Commencing smooth kNN distance calibration using 1 thread
19:32:48 Initializing from normalized Laplacian + noise
19:32:48 Commencing optimization for 500 epochs, with 201184 positive edges
19:33:02 Optimization finished
[1] "149 0.16"19:33:03 UMAP embedding parameters a = 1.383 b = 0.9596
19:33:03 Read 1203 rows and found 38 numeric columns
19:33:03 Using Annoy for neighbor search, n_neighbors = 149
19:33:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:33:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cc1087e
19:33:03 Searching Annoy index using 1 thread, search_k = 14900
19:33:04 Annoy recall = 100%
19:33:14 Commencing smooth kNN distance calibration using 1 thread
19:33:35 Initializing from normalized Laplacian + noise
19:33:35 Commencing optimization for 500 epochs, with 201184 positive edges
19:33:49 Optimization finished
[1] "149 0.17"19:33:49 UMAP embedding parameters a = 1.352 b = 0.9704
19:33:49 Read 1203 rows and found 38 numeric columns
19:33:49 Using Annoy for neighbor search, n_neighbors = 149
19:33:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:33:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87464a5869
19:33:50 Searching Annoy index using 1 thread, search_k = 14900
19:33:51 Annoy recall = 100%
19:34:01 Commencing smooth kNN distance calibration using 1 thread
19:34:22 Initializing from normalized Laplacian + noise
19:34:22 Commencing optimization for 500 epochs, with 201184 positive edges
19:34:36 Optimization finished
[1] "149 0.18"19:34:36 UMAP embedding parameters a = 1.321 b = 0.9813
19:34:36 Read 1203 rows and found 38 numeric columns
19:34:36 Using Annoy for neighbor search, n_neighbors = 149
19:34:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:34:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718e73509
19:34:37 Searching Annoy index using 1 thread, search_k = 14900
19:34:38 Annoy recall = 100%
19:34:48 Commencing smooth kNN distance calibration using 1 thread
19:35:09 Initializing from normalized Laplacian + noise
19:35:09 Commencing optimization for 500 epochs, with 201184 positive edges
19:35:23 Optimization finished
[1] "149 0.19"19:35:23 UMAP embedding parameters a = 1.292 b = 0.9921
19:35:23 Read 1203 rows and found 38 numeric columns
19:35:23 Using Annoy for neighbor search, n_neighbors = 149
19:35:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:35:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87348108a5
19:35:24 Searching Annoy index using 1 thread, search_k = 14900
19:35:25 Annoy recall = 100%
19:35:35 Commencing smooth kNN distance calibration using 1 thread
19:35:56 Initializing from normalized Laplacian + noise
19:35:56 Commencing optimization for 500 epochs, with 201184 positive edges
19:36:10 Optimization finished
[1] "149 0.2"19:36:10 UMAP embedding parameters a = 1.262 b = 1.003
19:36:10 Read 1203 rows and found 38 numeric columns
19:36:10 Using Annoy for neighbor search, n_neighbors = 149
19:36:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:36:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87629a51d9
19:36:11 Searching Annoy index using 1 thread, search_k = 14900
19:36:12 Annoy recall = 100%
19:36:22 Commencing smooth kNN distance calibration using 1 thread
19:36:42 Initializing from normalized Laplacian + noise
19:36:43 Commencing optimization for 500 epochs, with 201184 positive edges
19:36:57 Optimization finished
[1] "150 0"19:36:57 UMAP embedding parameters a = 1.933 b = 0.7905
19:36:57 Read 1203 rows and found 38 numeric columns
19:36:57 Using Annoy for neighbor search, n_neighbors = 150
19:36:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:36:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872199b28b
19:36:58 Searching Annoy index using 1 thread, search_k = 15000
19:36:59 Annoy recall = 100%
19:37:09 Commencing smooth kNN distance calibration using 1 thread
19:37:30 Initializing from normalized Laplacian + noise
19:37:30 Commencing optimization for 500 epochs, with 202412 positive edges
19:37:44 Optimization finished
[1] "150 0.01"19:37:44 UMAP embedding parameters a = 1.896 b = 0.8006
19:37:44 Read 1203 rows and found 38 numeric columns
19:37:44 Using Annoy for neighbor search, n_neighbors = 150
19:37:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:37:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875920ae3e
19:37:45 Searching Annoy index using 1 thread, search_k = 15000
19:37:46 Annoy recall = 100%
19:37:56 Commencing smooth kNN distance calibration using 1 thread
19:38:17 Initializing from normalized Laplacian + noise
19:38:17 Commencing optimization for 500 epochs, with 202412 positive edges
19:38:31 Optimization finished
[1] "150 0.02"19:38:31 UMAP embedding parameters a = 1.859 b = 0.8109
19:38:31 Read 1203 rows and found 38 numeric columns
19:38:31 Using Annoy for neighbor search, n_neighbors = 150
19:38:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:38:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876620b0eb
19:38:32 Searching Annoy index using 1 thread, search_k = 15000
19:38:33 Annoy recall = 100%
19:38:43 Commencing smooth kNN distance calibration using 1 thread
19:39:04 Initializing from normalized Laplacian + noise
19:39:04 Commencing optimization for 500 epochs, with 202412 positive edges
19:39:18 Optimization finished
[1] "150 0.03"19:39:18 UMAP embedding parameters a = 1.822 b = 0.8212
19:39:18 Read 1203 rows and found 38 numeric columns
19:39:18 Using Annoy for neighbor search, n_neighbors = 150
19:39:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:39:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87144eb4d2
19:39:19 Searching Annoy index using 1 thread, search_k = 15000
19:39:20 Annoy recall = 100%
19:39:30 Commencing smooth kNN distance calibration using 1 thread
19:39:51 Initializing from normalized Laplacian + noise
19:39:51 Commencing optimization for 500 epochs, with 202412 positive edges
19:40:05 Optimization finished
[1] "150 0.04"19:40:05 UMAP embedding parameters a = 1.786 b = 0.8316
19:40:05 Read 1203 rows and found 38 numeric columns
19:40:05 Using Annoy for neighbor search, n_neighbors = 150
19:40:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:40:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763c44bbc
19:40:06 Searching Annoy index using 1 thread, search_k = 15000
19:40:07 Annoy recall = 100%
19:40:17 Commencing smooth kNN distance calibration using 1 thread
19:40:38 Initializing from normalized Laplacian + noise
19:40:38 Commencing optimization for 500 epochs, with 202412 positive edges
19:40:52 Optimization finished
[1] "150 0.05"19:40:52 UMAP embedding parameters a = 1.75 b = 0.8421
19:40:52 Read 1203 rows and found 38 numeric columns
19:40:52 Using Annoy for neighbor search, n_neighbors = 150
19:40:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:40:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b0da369
19:40:53 Searching Annoy index using 1 thread, search_k = 15000
19:40:54 Annoy recall = 100%
19:41:04 Commencing smooth kNN distance calibration using 1 thread
19:41:25 Initializing from normalized Laplacian + noise
19:41:25 Commencing optimization for 500 epochs, with 202412 positive edges
19:41:39 Optimization finished
[1] "150 0.06"19:41:40 UMAP embedding parameters a = 1.715 b = 0.8526
19:41:40 Read 1203 rows and found 38 numeric columns
19:41:40 Using Annoy for neighbor search, n_neighbors = 150
19:41:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:41:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711b1de8b
19:41:40 Searching Annoy index using 1 thread, search_k = 15000
19:41:41 Annoy recall = 100%
19:41:51 Commencing smooth kNN distance calibration using 1 thread
19:42:12 Initializing from normalized Laplacian + noise
19:42:12 Commencing optimization for 500 epochs, with 202412 positive edges
19:42:26 Optimization finished
[1] "150 0.07"19:42:27 UMAP embedding parameters a = 1.68 b = 0.8631
19:42:27 Read 1203 rows and found 38 numeric columns
19:42:27 Using Annoy for neighbor search, n_neighbors = 150
19:42:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:42:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763f782ef
19:42:27 Searching Annoy index using 1 thread, search_k = 15000
19:42:28 Annoy recall = 100%
19:42:39 Commencing smooth kNN distance calibration using 1 thread
19:42:59 Initializing from normalized Laplacian + noise
19:42:59 Commencing optimization for 500 epochs, with 202412 positive edges
19:43:13 Optimization finished
[1] "150 0.08"19:43:14 UMAP embedding parameters a = 1.645 b = 0.8737
19:43:14 Read 1203 rows and found 38 numeric columns
19:43:14 Using Annoy for neighbor search, n_neighbors = 150
19:43:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:43:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b4522c5
19:43:15 Searching Annoy index using 1 thread, search_k = 15000
19:43:16 Annoy recall = 100%
19:43:26 Commencing smooth kNN distance calibration using 1 thread
19:43:47 Initializing from normalized Laplacian + noise
19:43:47 Commencing optimization for 500 epochs, with 202412 positive edges
19:44:01 Optimization finished
[1] "150 0.09"19:44:01 UMAP embedding parameters a = 1.611 b = 0.8844
19:44:01 Read 1203 rows and found 38 numeric columns
19:44:01 Using Annoy for neighbor search, n_neighbors = 150
19:44:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:44:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a25d7f7
19:44:02 Searching Annoy index using 1 thread, search_k = 15000
19:44:03 Annoy recall = 100%
19:44:13 Commencing smooth kNN distance calibration using 1 thread
19:44:34 Initializing from normalized Laplacian + noise
19:44:34 Commencing optimization for 500 epochs, with 202412 positive edges
19:44:48 Optimization finished
[1] "150 0.1"19:44:48 UMAP embedding parameters a = 1.577 b = 0.8951
19:44:48 Read 1203 rows and found 38 numeric columns
19:44:48 Using Annoy for neighbor search, n_neighbors = 150
19:44:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:44:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87238f78b3
19:44:49 Searching Annoy index using 1 thread, search_k = 15000
19:44:50 Annoy recall = 100%
19:45:00 Commencing smooth kNN distance calibration using 1 thread
19:45:21 Initializing from normalized Laplacian + noise
19:45:21 Commencing optimization for 500 epochs, with 202412 positive edges
19:45:35 Optimization finished
[1] "150 0.11"19:45:36 UMAP embedding parameters a = 1.544 b = 0.9058
19:45:36 Read 1203 rows and found 38 numeric columns
19:45:36 Using Annoy for neighbor search, n_neighbors = 150
19:45:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:45:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b1e80c9
19:45:36 Searching Annoy index using 1 thread, search_k = 15000
19:45:37 Annoy recall = 100%
19:45:48 Commencing smooth kNN distance calibration using 1 thread
19:46:08 Initializing from normalized Laplacian + noise
19:46:08 Commencing optimization for 500 epochs, with 202412 positive edges
19:46:22 Optimization finished
[1] "150 0.12"19:46:23 UMAP embedding parameters a = 1.51 b = 0.9165
19:46:23 Read 1203 rows and found 38 numeric columns
19:46:23 Using Annoy for neighbor search, n_neighbors = 150
19:46:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:46:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d2f5269
19:46:23 Searching Annoy index using 1 thread, search_k = 15000
19:46:25 Annoy recall = 100%
19:46:35 Commencing smooth kNN distance calibration using 1 thread
19:46:55 Initializing from normalized Laplacian + noise
19:46:55 Commencing optimization for 500 epochs, with 202412 positive edges
19:47:10 Optimization finished
[1] "150 0.13"19:47:10 UMAP embedding parameters a = 1.478 b = 0.9272
19:47:10 Read 1203 rows and found 38 numeric columns
19:47:10 Using Annoy for neighbor search, n_neighbors = 150
19:47:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:47:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873dcd2c16
19:47:11 Searching Annoy index using 1 thread, search_k = 15000
19:47:12 Annoy recall = 100%
19:47:22 Commencing smooth kNN distance calibration using 1 thread
19:47:42 Initializing from normalized Laplacian + noise
19:47:43 Commencing optimization for 500 epochs, with 202412 positive edges
19:47:57 Optimization finished
[1] "150 0.14"19:47:57 UMAP embedding parameters a = 1.446 b = 0.938
19:47:57 Read 1203 rows and found 38 numeric columns
19:47:57 Using Annoy for neighbor search, n_neighbors = 150
19:47:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:47:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750450053
19:47:58 Searching Annoy index using 1 thread, search_k = 15000
19:47:59 Annoy recall = 100%
19:48:09 Commencing smooth kNN distance calibration using 1 thread
19:48:30 Initializing from normalized Laplacian + noise
19:48:30 Commencing optimization for 500 epochs, with 202412 positive edges
19:48:44 Optimization finished
[1] "150 0.15"19:48:44 UMAP embedding parameters a = 1.414 b = 0.9488
19:48:44 Read 1203 rows and found 38 numeric columns
19:48:44 Using Annoy for neighbor search, n_neighbors = 150
19:48:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:48:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872717b439
19:48:45 Searching Annoy index using 1 thread, search_k = 15000
19:48:46 Annoy recall = 100%
19:48:56 Commencing smooth kNN distance calibration using 1 thread
19:49:17 Initializing from normalized Laplacian + noise
19:49:17 Commencing optimization for 500 epochs, with 202412 positive edges
19:49:31 Optimization finished
[1] "150 0.16"19:49:32 UMAP embedding parameters a = 1.383 b = 0.9596
19:49:32 Read 1203 rows and found 38 numeric columns
19:49:32 Using Annoy for neighbor search, n_neighbors = 150
19:49:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:49:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872be1d6b4
19:49:32 Searching Annoy index using 1 thread, search_k = 15000
19:49:33 Annoy recall = 100%
19:49:44 Commencing smooth kNN distance calibration using 1 thread
19:50:04 Initializing from normalized Laplacian + noise
19:50:04 Commencing optimization for 500 epochs, with 202412 positive edges
19:50:18 Optimization finished
[1] "150 0.17"19:50:19 UMAP embedding parameters a = 1.352 b = 0.9704
19:50:19 Read 1203 rows and found 38 numeric columns
19:50:19 Using Annoy for neighbor search, n_neighbors = 150
19:50:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:50:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ffd69b1
19:50:19 Searching Annoy index using 1 thread, search_k = 15000
19:50:20 Annoy recall = 100%
19:50:31 Commencing smooth kNN distance calibration using 1 thread
19:50:51 Initializing from normalized Laplacian + noise
19:50:51 Commencing optimization for 500 epochs, with 202412 positive edges
19:51:06 Optimization finished
[1] "150 0.18"19:51:06 UMAP embedding parameters a = 1.321 b = 0.9813
19:51:06 Read 1203 rows and found 38 numeric columns
19:51:06 Using Annoy for neighbor search, n_neighbors = 150
19:51:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:51:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a5d5f9c
19:51:07 Searching Annoy index using 1 thread, search_k = 15000
19:51:08 Annoy recall = 100%
19:51:18 Commencing smooth kNN distance calibration using 1 thread
19:51:38 Initializing from normalized Laplacian + noise
19:51:39 Commencing optimization for 500 epochs, with 202412 positive edges
19:51:53 Optimization finished
[1] "150 0.19"19:51:53 UMAP embedding parameters a = 1.292 b = 0.9921
19:51:53 Read 1203 rows and found 38 numeric columns
19:51:53 Using Annoy for neighbor search, n_neighbors = 150
19:51:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:51:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776c1ad7b
19:51:54 Searching Annoy index using 1 thread, search_k = 15000
19:51:55 Annoy recall = 100%
19:52:05 Commencing smooth kNN distance calibration using 1 thread
19:52:26 Initializing from normalized Laplacian + noise
19:52:26 Commencing optimization for 500 epochs, with 202412 positive edges
19:52:40 Optimization finished
[1] "150 0.2"19:52:40 UMAP embedding parameters a = 1.262 b = 1.003
19:52:40 Read 1203 rows and found 38 numeric columns
19:52:40 Using Annoy for neighbor search, n_neighbors = 150
19:52:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:52:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87204d9a3
19:52:41 Searching Annoy index using 1 thread, search_k = 15000
19:52:42 Annoy recall = 100%
19:52:52 Commencing smooth kNN distance calibration using 1 thread
19:53:13 Initializing from normalized Laplacian + noise
19:53:13 Commencing optimization for 500 epochs, with 202412 positive edges
19:53:27 Optimization finished
[1] "151 0"19:53:28 UMAP embedding parameters a = 1.933 b = 0.7905
19:53:28 Read 1203 rows and found 38 numeric columns
19:53:28 Using Annoy for neighbor search, n_neighbors = 151
19:53:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:53:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876946c2dc
19:53:28 Searching Annoy index using 1 thread, search_k = 15100
19:53:29 Annoy recall = 100%
19:53:40 Commencing smooth kNN distance calibration using 1 thread
19:54:00 Initializing from normalized Laplacian + noise
19:54:00 Commencing optimization for 500 epochs, with 203626 positive edges
19:54:15 Optimization finished
[1] "151 0.01"19:54:15 UMAP embedding parameters a = 1.896 b = 0.8006
19:54:15 Read 1203 rows and found 38 numeric columns
19:54:15 Using Annoy for neighbor search, n_neighbors = 151
19:54:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:54:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87793e01e
19:54:16 Searching Annoy index using 1 thread, search_k = 15100
19:54:17 Annoy recall = 100%
19:54:27 Commencing smooth kNN distance calibration using 1 thread
19:54:48 Initializing from normalized Laplacian + noise
19:54:48 Commencing optimization for 500 epochs, with 203626 positive edges
19:55:02 Optimization finished
[1] "151 0.02"19:55:02 UMAP embedding parameters a = 1.859 b = 0.8109
19:55:02 Read 1203 rows and found 38 numeric columns
19:55:03 Using Annoy for neighbor search, n_neighbors = 151
19:55:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:55:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872862a14e
19:55:03 Searching Annoy index using 1 thread, search_k = 15100
19:55:04 Annoy recall = 100%
19:55:15 Commencing smooth kNN distance calibration using 1 thread
19:55:35 Initializing from normalized Laplacian + noise
19:55:36 Commencing optimization for 500 epochs, with 203626 positive edges
19:55:50 Optimization finished
[1] "151 0.03"19:55:51 UMAP embedding parameters a = 1.822 b = 0.8212
19:55:51 Read 1203 rows and found 38 numeric columns
19:55:51 Using Annoy for neighbor search, n_neighbors = 151
19:55:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:55:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c8c7a57
19:55:51 Searching Annoy index using 1 thread, search_k = 15100
19:55:52 Annoy recall = 100%
19:56:03 Commencing smooth kNN distance calibration using 1 thread
19:56:24 Initializing from normalized Laplacian + noise
19:56:24 Commencing optimization for 500 epochs, with 203626 positive edges
19:56:38 Optimization finished
[1] "151 0.04"19:56:39 UMAP embedding parameters a = 1.786 b = 0.8316
19:56:39 Read 1203 rows and found 38 numeric columns
19:56:39 Using Annoy for neighbor search, n_neighbors = 151
19:56:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:56:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87469f4d94
19:56:39 Searching Annoy index using 1 thread, search_k = 15100
19:56:41 Annoy recall = 100%
19:56:51 Commencing smooth kNN distance calibration using 1 thread
19:57:12 Initializing from normalized Laplacian + noise
19:57:12 Commencing optimization for 500 epochs, with 203626 positive edges
19:57:27 Optimization finished
[1] "151 0.05"19:57:27 UMAP embedding parameters a = 1.75 b = 0.8421
19:57:27 Read 1203 rows and found 38 numeric columns
19:57:27 Using Annoy for neighbor search, n_neighbors = 151
19:57:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:57:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87523a9cd
19:57:28 Searching Annoy index using 1 thread, search_k = 15100
19:57:29 Annoy recall = 100%
19:57:39 Commencing smooth kNN distance calibration using 1 thread
19:58:00 Initializing from normalized Laplacian + noise
19:58:00 Commencing optimization for 500 epochs, with 203626 positive edges
19:58:15 Optimization finished
[1] "151 0.06"19:58:15 UMAP embedding parameters a = 1.715 b = 0.8526
19:58:15 Read 1203 rows and found 38 numeric columns
19:58:15 Using Annoy for neighbor search, n_neighbors = 151
19:58:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:58:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752d6d2c0
19:58:16 Searching Annoy index using 1 thread, search_k = 15100
19:58:17 Annoy recall = 100%
19:58:27 Commencing smooth kNN distance calibration using 1 thread
19:58:48 Initializing from normalized Laplacian + noise
19:58:48 Commencing optimization for 500 epochs, with 203626 positive edges
19:59:03 Optimization finished
[1] "151 0.07"19:59:03 UMAP embedding parameters a = 1.68 b = 0.8631
19:59:03 Read 1203 rows and found 38 numeric columns
19:59:03 Using Annoy for neighbor search, n_neighbors = 151
19:59:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:59:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f86829d
19:59:04 Searching Annoy index using 1 thread, search_k = 15100
19:59:05 Annoy recall = 100%
19:59:16 Commencing smooth kNN distance calibration using 1 thread
19:59:37 Initializing from normalized Laplacian + noise
19:59:37 Commencing optimization for 500 epochs, with 203626 positive edges
19:59:51 Optimization finished
[1] "151 0.08"19:59:52 UMAP embedding parameters a = 1.645 b = 0.8737
19:59:52 Read 1203 rows and found 38 numeric columns
19:59:52 Using Annoy for neighbor search, n_neighbors = 151
19:59:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:59:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739a4b272
19:59:52 Searching Annoy index using 1 thread, search_k = 15100
19:59:53 Annoy recall = 100%
20:00:04 Commencing smooth kNN distance calibration using 1 thread
20:00:25 Initializing from normalized Laplacian + noise
20:00:25 Commencing optimization for 500 epochs, with 203626 positive edges
20:00:40 Optimization finished
[1] "151 0.09"20:00:40 UMAP embedding parameters a = 1.611 b = 0.8844
20:00:40 Read 1203 rows and found 38 numeric columns
20:00:40 Using Annoy for neighbor search, n_neighbors = 151
20:00:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:00:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735712499
20:00:41 Searching Annoy index using 1 thread, search_k = 15100
20:00:42 Annoy recall = 100%
20:00:52 Commencing smooth kNN distance calibration using 1 thread
20:01:13 Initializing from normalized Laplacian + noise
20:01:13 Commencing optimization for 500 epochs, with 203626 positive edges
20:01:28 Optimization finished
[1] "151 0.1"20:01:28 UMAP embedding parameters a = 1.577 b = 0.8951
20:01:28 Read 1203 rows and found 38 numeric columns
20:01:28 Using Annoy for neighbor search, n_neighbors = 151
20:01:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:01:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871203528
20:01:29 Searching Annoy index using 1 thread, search_k = 15100
20:01:30 Annoy recall = 100%
20:01:40 Commencing smooth kNN distance calibration using 1 thread
20:02:01 Initializing from normalized Laplacian + noise
20:02:02 Commencing optimization for 500 epochs, with 203626 positive edges
20:02:16 Optimization finished
[1] "151 0.11"20:02:16 UMAP embedding parameters a = 1.544 b = 0.9058
20:02:17 Read 1203 rows and found 38 numeric columns
20:02:17 Using Annoy for neighbor search, n_neighbors = 151
20:02:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:02:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712c560b0
20:02:17 Searching Annoy index using 1 thread, search_k = 15100
20:02:18 Annoy recall = 100%
20:02:29 Commencing smooth kNN distance calibration using 1 thread
20:02:50 Initializing from normalized Laplacian + noise
20:02:50 Commencing optimization for 500 epochs, with 203626 positive edges
20:03:04 Optimization finished
[1] "151 0.12"20:03:05 UMAP embedding parameters a = 1.51 b = 0.9165
20:03:05 Read 1203 rows and found 38 numeric columns
20:03:05 Using Annoy for neighbor search, n_neighbors = 151
20:03:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:03:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b91d584
20:03:05 Searching Annoy index using 1 thread, search_k = 15100
20:03:07 Annoy recall = 100%
20:03:17 Commencing smooth kNN distance calibration using 1 thread
20:03:38 Initializing from normalized Laplacian + noise
20:03:38 Commencing optimization for 500 epochs, with 203626 positive edges
20:03:53 Optimization finished
[1] "151 0.13"20:03:53 UMAP embedding parameters a = 1.478 b = 0.9272
20:03:53 Read 1203 rows and found 38 numeric columns
20:03:53 Using Annoy for neighbor search, n_neighbors = 151
20:03:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:03:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87156ee9fa
20:03:54 Searching Annoy index using 1 thread, search_k = 15100
20:03:55 Annoy recall = 100%
20:04:05 Commencing smooth kNN distance calibration using 1 thread
20:04:26 Initializing from normalized Laplacian + noise
20:04:26 Commencing optimization for 500 epochs, with 203626 positive edges
20:04:41 Optimization finished
[1] "151 0.14"20:04:41 UMAP embedding parameters a = 1.446 b = 0.938
20:04:41 Read 1203 rows and found 38 numeric columns
20:04:41 Using Annoy for neighbor search, n_neighbors = 151
20:04:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:04:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877689ac6c
20:04:42 Searching Annoy index using 1 thread, search_k = 15100
20:04:43 Annoy recall = 100%
20:04:54 Commencing smooth kNN distance calibration using 1 thread
20:05:15 Initializing from normalized Laplacian + noise
20:05:15 Commencing optimization for 500 epochs, with 203626 positive edges
20:05:29 Optimization finished
[1] "151 0.15"20:05:30 UMAP embedding parameters a = 1.414 b = 0.9488
20:05:30 Read 1203 rows and found 38 numeric columns
20:05:30 Using Annoy for neighbor search, n_neighbors = 151
20:05:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:05:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87369f78ed
20:05:30 Searching Annoy index using 1 thread, search_k = 15100
20:05:31 Annoy recall = 100%
20:05:42 Commencing smooth kNN distance calibration using 1 thread
20:06:03 Initializing from normalized Laplacian + noise
20:06:03 Commencing optimization for 500 epochs, with 203626 positive edges
20:06:18 Optimization finished
[1] "151 0.16"20:06:18 UMAP embedding parameters a = 1.383 b = 0.9596
20:06:18 Read 1203 rows and found 38 numeric columns
20:06:18 Using Annoy for neighbor search, n_neighbors = 151
20:06:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:06:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872720c886
20:06:19 Searching Annoy index using 1 thread, search_k = 15100
20:06:20 Annoy recall = 100%
20:06:30 Commencing smooth kNN distance calibration using 1 thread
20:06:51 Initializing from normalized Laplacian + noise
20:06:51 Commencing optimization for 500 epochs, with 203626 positive edges
20:07:06 Optimization finished
[1] "151 0.17"20:07:06 UMAP embedding parameters a = 1.352 b = 0.9704
20:07:06 Read 1203 rows and found 38 numeric columns
20:07:06 Using Annoy for neighbor search, n_neighbors = 151
20:07:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:07:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a812f5b
20:07:07 Searching Annoy index using 1 thread, search_k = 15100
20:07:08 Annoy recall = 100%
20:07:19 Commencing smooth kNN distance calibration using 1 thread
20:07:40 Initializing from normalized Laplacian + noise
20:07:40 Commencing optimization for 500 epochs, with 203626 positive edges
20:07:54 Optimization finished
[1] "151 0.18"20:07:55 UMAP embedding parameters a = 1.321 b = 0.9813
20:07:55 Read 1203 rows and found 38 numeric columns
20:07:55 Using Annoy for neighbor search, n_neighbors = 151
20:07:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:07:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721e49bb3
20:07:55 Searching Annoy index using 1 thread, search_k = 15100
20:07:57 Annoy recall = 100%
20:08:07 Commencing smooth kNN distance calibration using 1 thread
20:08:28 Initializing from normalized Laplacian + noise
20:08:28 Commencing optimization for 500 epochs, with 203626 positive edges
20:08:43 Optimization finished
[1] "151 0.19"20:08:43 UMAP embedding parameters a = 1.292 b = 0.9921
20:08:43 Read 1203 rows and found 38 numeric columns
20:08:43 Using Annoy for neighbor search, n_neighbors = 151
20:08:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:08:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872146a07d
20:08:44 Searching Annoy index using 1 thread, search_k = 15100
20:08:45 Annoy recall = 100%
20:08:55 Commencing smooth kNN distance calibration using 1 thread
20:09:16 Initializing from normalized Laplacian + noise
20:09:16 Commencing optimization for 500 epochs, with 203626 positive edges
20:09:31 Optimization finished
[1] "151 0.2"20:09:31 UMAP embedding parameters a = 1.262 b = 1.003
20:09:31 Read 1203 rows and found 38 numeric columns
20:09:31 Using Annoy for neighbor search, n_neighbors = 151
20:09:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:09:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e10a80e
20:09:32 Searching Annoy index using 1 thread, search_k = 15100
20:09:33 Annoy recall = 100%
20:09:44 Commencing smooth kNN distance calibration using 1 thread
20:10:05 Initializing from normalized Laplacian + noise
20:10:05 Commencing optimization for 500 epochs, with 203626 positive edges
20:10:19 Optimization finished
[1] "152 0"20:10:20 UMAP embedding parameters a = 1.933 b = 0.7905
20:10:20 Read 1203 rows and found 38 numeric columns
20:10:20 Using Annoy for neighbor search, n_neighbors = 152
20:10:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:10:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d031c7c
20:10:20 Searching Annoy index using 1 thread, search_k = 15200
20:10:22 Annoy recall = 100%
20:10:32 Commencing smooth kNN distance calibration using 1 thread
20:10:53 Initializing from normalized Laplacian + noise
20:10:53 Commencing optimization for 500 epochs, with 204872 positive edges
20:11:08 Optimization finished
[1] "152 0.01"20:11:08 UMAP embedding parameters a = 1.896 b = 0.8006
20:11:08 Read 1203 rows and found 38 numeric columns
20:11:08 Using Annoy for neighbor search, n_neighbors = 152
20:11:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:11:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e75f2e6
20:11:09 Searching Annoy index using 1 thread, search_k = 15200
20:11:10 Annoy recall = 100%
20:11:20 Commencing smooth kNN distance calibration using 1 thread
20:11:42 Initializing from normalized Laplacian + noise
20:11:42 Commencing optimization for 500 epochs, with 204872 positive edges
20:11:57 Optimization finished
[1] "152 0.02"20:11:57 UMAP embedding parameters a = 1.859 b = 0.8109
20:11:57 Read 1203 rows and found 38 numeric columns
20:11:57 Using Annoy for neighbor search, n_neighbors = 152
20:11:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:11:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bddd424
20:11:58 Searching Annoy index using 1 thread, search_k = 15200
20:11:59 Annoy recall = 100%
20:12:09 Commencing smooth kNN distance calibration using 1 thread
20:12:30 Initializing from normalized Laplacian + noise
20:12:30 Commencing optimization for 500 epochs, with 204872 positive edges
20:12:45 Optimization finished
[1] "152 0.03"20:12:45 UMAP embedding parameters a = 1.822 b = 0.8212
20:12:45 Read 1203 rows and found 38 numeric columns
20:12:45 Using Annoy for neighbor search, n_neighbors = 152
20:12:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:12:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d481cd0
20:12:46 Searching Annoy index using 1 thread, search_k = 15200
20:12:47 Annoy recall = 100%
20:12:58 Commencing smooth kNN distance calibration using 1 thread
20:13:18 Initializing from normalized Laplacian + noise
20:13:19 Commencing optimization for 500 epochs, with 204872 positive edges
20:13:33 Optimization finished
[1] "152 0.04"20:13:34 UMAP embedding parameters a = 1.786 b = 0.8316
20:13:34 Read 1203 rows and found 38 numeric columns
20:13:34 Using Annoy for neighbor search, n_neighbors = 152
20:13:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:13:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87458da71f
20:13:34 Searching Annoy index using 1 thread, search_k = 15200
20:13:35 Annoy recall = 100%
20:13:46 Commencing smooth kNN distance calibration using 1 thread
20:14:07 Initializing from normalized Laplacian + noise
20:14:07 Commencing optimization for 500 epochs, with 204872 positive edges
20:14:22 Optimization finished
[1] "152 0.05"20:14:22 UMAP embedding parameters a = 1.75 b = 0.8421
20:14:22 Read 1203 rows and found 38 numeric columns
20:14:22 Using Annoy for neighbor search, n_neighbors = 152
20:14:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:14:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767bfaad9
20:14:23 Searching Annoy index using 1 thread, search_k = 15200
20:14:24 Annoy recall = 100%
20:14:34 Commencing smooth kNN distance calibration using 1 thread
20:14:56 Initializing from normalized Laplacian + noise
20:14:56 Commencing optimization for 500 epochs, with 204872 positive edges
20:15:10 Optimization finished
[1] "152 0.06"20:15:11 UMAP embedding parameters a = 1.715 b = 0.8526
20:15:11 Read 1203 rows and found 38 numeric columns
20:15:11 Using Annoy for neighbor search, n_neighbors = 152
20:15:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:15:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d458681
20:15:11 Searching Annoy index using 1 thread, search_k = 15200
20:15:13 Annoy recall = 100%
20:15:23 Commencing smooth kNN distance calibration using 1 thread
20:15:44 Initializing from normalized Laplacian + noise
20:15:44 Commencing optimization for 500 epochs, with 204872 positive edges
20:15:59 Optimization finished
[1] "152 0.07"20:15:59 UMAP embedding parameters a = 1.68 b = 0.8631
20:15:59 Read 1203 rows and found 38 numeric columns
20:15:59 Using Annoy for neighbor search, n_neighbors = 152
20:15:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:16:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87feb06bb
20:16:00 Searching Annoy index using 1 thread, search_k = 15200
20:16:01 Annoy recall = 100%
20:16:12 Commencing smooth kNN distance calibration using 1 thread
20:16:33 Initializing from normalized Laplacian + noise
20:16:33 Commencing optimization for 500 epochs, with 204872 positive edges
20:16:47 Optimization finished
[1] "152 0.08"20:16:48 UMAP embedding parameters a = 1.645 b = 0.8737
20:16:48 Read 1203 rows and found 38 numeric columns
20:16:48 Using Annoy for neighbor search, n_neighbors = 152
20:16:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:16:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e815854
20:16:48 Searching Annoy index using 1 thread, search_k = 15200
20:16:49 Annoy recall = 100%
20:17:00 Commencing smooth kNN distance calibration using 1 thread
20:17:21 Initializing from normalized Laplacian + noise
20:17:21 Commencing optimization for 500 epochs, with 204872 positive edges
20:17:36 Optimization finished
[1] "152 0.09"20:17:36 UMAP embedding parameters a = 1.611 b = 0.8844
20:17:36 Read 1203 rows and found 38 numeric columns
20:17:36 Using Annoy for neighbor search, n_neighbors = 152
20:17:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:17:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f4a6025
20:17:37 Searching Annoy index using 1 thread, search_k = 15200
20:17:38 Annoy recall = 100%
20:17:48 Commencing smooth kNN distance calibration using 1 thread
20:18:10 Initializing from normalized Laplacian + noise
20:18:10 Commencing optimization for 500 epochs, with 204872 positive edges
20:18:25 Optimization finished
[1] "152 0.1"20:18:25 UMAP embedding parameters a = 1.577 b = 0.8951
20:18:25 Read 1203 rows and found 38 numeric columns
20:18:25 Using Annoy for neighbor search, n_neighbors = 152
20:18:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:18:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877931c998
20:18:26 Searching Annoy index using 1 thread, search_k = 15200
20:18:27 Annoy recall = 100%
20:18:37 Commencing smooth kNN distance calibration using 1 thread
20:18:58 Initializing from normalized Laplacian + noise
20:18:58 Commencing optimization for 500 epochs, with 204872 positive edges
20:19:13 Optimization finished
[1] "152 0.11"20:19:13 UMAP embedding parameters a = 1.544 b = 0.9058
20:19:13 Read 1203 rows and found 38 numeric columns
20:19:13 Using Annoy for neighbor search, n_neighbors = 152
20:19:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:19:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766153872
20:19:14 Searching Annoy index using 1 thread, search_k = 15200
20:19:15 Annoy recall = 100%
20:19:26 Commencing smooth kNN distance calibration using 1 thread
20:19:47 Initializing from normalized Laplacian + noise
20:19:47 Commencing optimization for 500 epochs, with 204872 positive edges
20:20:02 Optimization finished
[1] "152 0.12"20:20:02 UMAP embedding parameters a = 1.51 b = 0.9165
20:20:02 Read 1203 rows and found 38 numeric columns
20:20:02 Using Annoy for neighbor search, n_neighbors = 152
20:20:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:20:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737ad0173
20:20:03 Searching Annoy index using 1 thread, search_k = 15200
20:20:04 Annoy recall = 100%
20:20:14 Commencing smooth kNN distance calibration using 1 thread
20:20:36 Initializing from normalized Laplacian + noise
20:20:36 Commencing optimization for 500 epochs, with 204872 positive edges
20:20:50 Optimization finished
[1] "152 0.13"20:20:50 UMAP embedding parameters a = 1.478 b = 0.9272
20:20:50 Read 1203 rows and found 38 numeric columns
20:20:51 Using Annoy for neighbor search, n_neighbors = 152
20:20:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:20:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875be43ef
20:20:51 Searching Annoy index using 1 thread, search_k = 15200
20:20:52 Annoy recall = 100%
20:21:03 Commencing smooth kNN distance calibration using 1 thread
20:21:24 Initializing from normalized Laplacian + noise
20:21:24 Commencing optimization for 500 epochs, with 204872 positive edges
20:21:39 Optimization finished
[1] "152 0.14"20:21:39 UMAP embedding parameters a = 1.446 b = 0.938
20:21:39 Read 1203 rows and found 38 numeric columns
20:21:39 Using Annoy for neighbor search, n_neighbors = 152
20:21:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:21:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cb48606
20:21:40 Searching Annoy index using 1 thread, search_k = 15200
20:21:41 Annoy recall = 100%
20:21:51 Commencing smooth kNN distance calibration using 1 thread
20:22:13 Initializing from normalized Laplacian + noise
20:22:13 Commencing optimization for 500 epochs, with 204872 positive edges
20:22:28 Optimization finished
[1] "152 0.15"20:22:28 UMAP embedding parameters a = 1.414 b = 0.9488
20:22:28 Read 1203 rows and found 38 numeric columns
20:22:28 Using Annoy for neighbor search, n_neighbors = 152
20:22:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:22:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cd0ab40
20:22:29 Searching Annoy index using 1 thread, search_k = 15200
20:22:30 Annoy recall = 100%
20:22:40 Commencing smooth kNN distance calibration using 1 thread
20:23:01 Initializing from normalized Laplacian + noise
20:23:01 Commencing optimization for 500 epochs, with 204872 positive edges
20:23:16 Optimization finished
[1] "152 0.16"20:23:16 UMAP embedding parameters a = 1.383 b = 0.9596
20:23:16 Read 1203 rows and found 38 numeric columns
20:23:16 Using Annoy for neighbor search, n_neighbors = 152
20:23:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:23:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87589516af
20:23:17 Searching Annoy index using 1 thread, search_k = 15200
20:23:18 Annoy recall = 100%
20:23:29 Commencing smooth kNN distance calibration using 1 thread
20:23:50 Initializing from normalized Laplacian + noise
20:23:50 Commencing optimization for 500 epochs, with 204872 positive edges
20:24:05 Optimization finished
[1] "152 0.17"20:24:05 UMAP embedding parameters a = 1.352 b = 0.9704
20:24:05 Read 1203 rows and found 38 numeric columns
20:24:05 Using Annoy for neighbor search, n_neighbors = 152
20:24:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:24:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c3b08a3
20:24:06 Searching Annoy index using 1 thread, search_k = 15200
20:24:07 Annoy recall = 100%
20:24:17 Commencing smooth kNN distance calibration using 1 thread
20:24:39 Initializing from normalized Laplacian + noise
20:24:39 Commencing optimization for 500 epochs, with 204872 positive edges
20:24:53 Optimization finished
[1] "152 0.18"20:24:54 UMAP embedding parameters a = 1.321 b = 0.9813
20:24:54 Read 1203 rows and found 38 numeric columns
20:24:54 Using Annoy for neighbor search, n_neighbors = 152
20:24:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:24:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776755db3
20:24:54 Searching Annoy index using 1 thread, search_k = 15200
20:24:56 Annoy recall = 100%
20:25:06 Commencing smooth kNN distance calibration using 1 thread
20:25:27 Initializing from normalized Laplacian + noise
20:25:27 Commencing optimization for 500 epochs, with 204872 positive edges
20:25:42 Optimization finished
[1] "152 0.19"20:25:42 UMAP embedding parameters a = 1.292 b = 0.9921
20:25:42 Read 1203 rows and found 38 numeric columns
20:25:43 Using Annoy for neighbor search, n_neighbors = 152
20:25:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:25:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e063b48
20:25:43 Searching Annoy index using 1 thread, search_k = 15200
20:25:44 Annoy recall = 100%
20:25:55 Commencing smooth kNN distance calibration using 1 thread
20:26:16 Initializing from normalized Laplacian + noise
20:26:16 Commencing optimization for 500 epochs, with 204872 positive edges
20:26:31 Optimization finished
[1] "152 0.2"20:26:31 UMAP embedding parameters a = 1.262 b = 1.003
20:26:31 Read 1203 rows and found 38 numeric columns
20:26:31 Using Annoy for neighbor search, n_neighbors = 152
20:26:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:26:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d5b3dcc
20:26:32 Searching Annoy index using 1 thread, search_k = 15200
20:26:33 Annoy recall = 100%
20:26:44 Commencing smooth kNN distance calibration using 1 thread
20:27:05 Initializing from normalized Laplacian + noise
20:27:05 Commencing optimization for 500 epochs, with 204872 positive edges
20:27:19 Optimization finished
[1] "153 0"20:27:20 UMAP embedding parameters a = 1.933 b = 0.7905
20:27:20 Read 1203 rows and found 38 numeric columns
20:27:20 Using Annoy for neighbor search, n_neighbors = 153
20:27:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:27:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8793abe63
20:27:20 Searching Annoy index using 1 thread, search_k = 15300
20:27:22 Annoy recall = 100%
20:27:32 Commencing smooth kNN distance calibration using 1 thread
20:27:54 Initializing from normalized Laplacian + noise
20:27:54 Commencing optimization for 500 epochs, with 206086 positive edges
20:28:08 Optimization finished
[1] "153 0.01"20:28:08 UMAP embedding parameters a = 1.896 b = 0.8006
20:28:08 Read 1203 rows and found 38 numeric columns
20:28:08 Using Annoy for neighbor search, n_neighbors = 153
20:28:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:28:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87299810cd
20:28:09 Searching Annoy index using 1 thread, search_k = 15300
20:28:10 Annoy recall = 100%
20:28:21 Commencing smooth kNN distance calibration using 1 thread
20:28:42 Initializing from normalized Laplacian + noise
20:28:42 Commencing optimization for 500 epochs, with 206086 positive edges
20:28:57 Optimization finished
[1] "153 0.02"20:28:57 UMAP embedding parameters a = 1.859 b = 0.8109
20:28:57 Read 1203 rows and found 38 numeric columns
20:28:57 Using Annoy for neighbor search, n_neighbors = 153
20:28:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:28:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722ca27c6
20:28:58 Searching Annoy index using 1 thread, search_k = 15300
20:28:59 Annoy recall = 100%
20:29:10 Commencing smooth kNN distance calibration using 1 thread
20:29:31 Initializing from normalized Laplacian + noise
20:29:31 Commencing optimization for 500 epochs, with 206086 positive edges
20:29:46 Optimization finished
[1] "153 0.03"20:29:46 UMAP embedding parameters a = 1.822 b = 0.8212
20:29:46 Read 1203 rows and found 38 numeric columns
20:29:46 Using Annoy for neighbor search, n_neighbors = 153
20:29:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:29:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fc46ad0
20:29:47 Searching Annoy index using 1 thread, search_k = 15300
20:29:48 Annoy recall = 100%
20:29:59 Commencing smooth kNN distance calibration using 1 thread
20:30:20 Initializing from normalized Laplacian + noise
20:30:20 Commencing optimization for 500 epochs, with 206086 positive edges
20:30:35 Optimization finished
[1] "153 0.04"20:30:35 UMAP embedding parameters a = 1.786 b = 0.8316
20:30:35 Read 1203 rows and found 38 numeric columns
20:30:35 Using Annoy for neighbor search, n_neighbors = 153
20:30:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:30:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87603789ba
20:30:36 Searching Annoy index using 1 thread, search_k = 15300
20:30:37 Annoy recall = 100%
20:30:47 Commencing smooth kNN distance calibration using 1 thread
20:31:08 Initializing from normalized Laplacian + noise
20:31:09 Commencing optimization for 500 epochs, with 206086 positive edges
20:31:23 Optimization finished
[1] "153 0.05"20:31:23 UMAP embedding parameters a = 1.75 b = 0.8421
20:31:23 Read 1203 rows and found 38 numeric columns
20:31:24 Using Annoy for neighbor search, n_neighbors = 153
20:31:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:31:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749eaf04c
20:31:24 Searching Annoy index using 1 thread, search_k = 15300
20:31:25 Annoy recall = 100%
20:31:36 Commencing smooth kNN distance calibration using 1 thread
20:31:57 Initializing from normalized Laplacian + noise
20:31:57 Commencing optimization for 500 epochs, with 206086 positive edges
20:32:12 Optimization finished
[1] "153 0.06"20:32:12 UMAP embedding parameters a = 1.715 b = 0.8526
20:32:12 Read 1203 rows and found 38 numeric columns
20:32:12 Using Annoy for neighbor search, n_neighbors = 153
20:32:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:32:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a459a2b
20:32:13 Searching Annoy index using 1 thread, search_k = 15300
20:32:14 Annoy recall = 100%
20:32:25 Commencing smooth kNN distance calibration using 1 thread
20:32:46 Initializing from normalized Laplacian + noise
20:32:46 Commencing optimization for 500 epochs, with 206086 positive edges
20:33:01 Optimization finished
[1] "153 0.07"20:33:01 UMAP embedding parameters a = 1.68 b = 0.8631
20:33:01 Read 1203 rows and found 38 numeric columns
20:33:01 Using Annoy for neighbor search, n_neighbors = 153
20:33:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:33:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721c256d
20:33:02 Searching Annoy index using 1 thread, search_k = 15300
20:33:03 Annoy recall = 100%
20:33:14 Commencing smooth kNN distance calibration using 1 thread
20:33:35 Initializing from normalized Laplacian + noise
20:33:35 Commencing optimization for 500 epochs, with 206086 positive edges
20:33:50 Optimization finished
[1] "153 0.08"20:33:50 UMAP embedding parameters a = 1.645 b = 0.8737
20:33:50 Read 1203 rows and found 38 numeric columns
20:33:50 Using Annoy for neighbor search, n_neighbors = 153
20:33:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:33:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b3190c9
20:33:51 Searching Annoy index using 1 thread, search_k = 15300
20:33:52 Annoy recall = 100%
20:34:02 Commencing smooth kNN distance calibration using 1 thread
20:34:24 Initializing from normalized Laplacian + noise
20:34:24 Commencing optimization for 500 epochs, with 206086 positive edges
20:34:38 Optimization finished
[1] "153 0.09"20:34:39 UMAP embedding parameters a = 1.611 b = 0.8844
20:34:39 Read 1203 rows and found 38 numeric columns
20:34:39 Using Annoy for neighbor search, n_neighbors = 153
20:34:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:34:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875856423a
20:34:39 Searching Annoy index using 1 thread, search_k = 15300
20:34:41 Annoy recall = 100%
20:34:51 Commencing smooth kNN distance calibration using 1 thread
20:35:13 Initializing from normalized Laplacian + noise
20:35:13 Commencing optimization for 500 epochs, with 206086 positive edges
20:35:27 Optimization finished
[1] "153 0.1"20:35:28 UMAP embedding parameters a = 1.577 b = 0.8951
20:35:28 Read 1203 rows and found 38 numeric columns
20:35:28 Using Annoy for neighbor search, n_neighbors = 153
20:35:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:35:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f1f41ea
20:35:28 Searching Annoy index using 1 thread, search_k = 15300
20:35:29 Annoy recall = 100%
20:35:40 Commencing smooth kNN distance calibration using 1 thread
20:36:01 Initializing from normalized Laplacian + noise
20:36:01 Commencing optimization for 500 epochs, with 206086 positive edges
20:36:16 Optimization finished
[1] "153 0.11"20:36:17 UMAP embedding parameters a = 1.544 b = 0.9058
20:36:17 Read 1203 rows and found 38 numeric columns
20:36:17 Using Annoy for neighbor search, n_neighbors = 153
20:36:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:36:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879a783b0
20:36:17 Searching Annoy index using 1 thread, search_k = 15300
20:36:18 Annoy recall = 100%
20:36:29 Commencing smooth kNN distance calibration using 1 thread
20:36:50 Initializing from normalized Laplacian + noise
20:36:50 Commencing optimization for 500 epochs, with 206086 positive edges
20:37:05 Optimization finished
[1] "153 0.12"20:37:05 UMAP embedding parameters a = 1.51 b = 0.9165
20:37:05 Read 1203 rows and found 38 numeric columns
20:37:05 Using Annoy for neighbor search, n_neighbors = 153
20:37:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:37:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871434165e
20:37:06 Searching Annoy index using 1 thread, search_k = 15300
20:37:07 Annoy recall = 100%
20:37:18 Commencing smooth kNN distance calibration using 1 thread
20:37:39 Initializing from normalized Laplacian + noise
20:37:39 Commencing optimization for 500 epochs, with 206086 positive edges
20:37:54 Optimization finished
[1] "153 0.13"20:37:54 UMAP embedding parameters a = 1.478 b = 0.9272
20:37:54 Read 1203 rows and found 38 numeric columns
20:37:54 Using Annoy for neighbor search, n_neighbors = 153
20:37:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:37:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c675eba
20:37:55 Searching Annoy index using 1 thread, search_k = 15300
20:37:56 Annoy recall = 100%
20:38:07 Commencing smooth kNN distance calibration using 1 thread
20:38:28 Initializing from normalized Laplacian + noise
20:38:28 Commencing optimization for 500 epochs, with 206086 positive edges
20:38:43 Optimization finished
[1] "153 0.14"20:38:43 UMAP embedding parameters a = 1.446 b = 0.938
20:38:43 Read 1203 rows and found 38 numeric columns
20:38:43 Using Annoy for neighbor search, n_neighbors = 153
20:38:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:38:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f352acf
20:38:44 Searching Annoy index using 1 thread, search_k = 15300
20:38:45 Annoy recall = 100%
20:38:55 Commencing smooth kNN distance calibration using 1 thread
20:39:17 Initializing from normalized Laplacian + noise
20:39:17 Commencing optimization for 500 epochs, with 206086 positive edges
20:39:32 Optimization finished
[1] "153 0.15"20:39:32 UMAP embedding parameters a = 1.414 b = 0.9488
20:39:32 Read 1203 rows and found 38 numeric columns
20:39:32 Using Annoy for neighbor search, n_neighbors = 153
20:39:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:39:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bf3c137
20:39:33 Searching Annoy index using 1 thread, search_k = 15300
20:39:34 Annoy recall = 100%
20:39:44 Commencing smooth kNN distance calibration using 1 thread
20:40:06 Initializing from normalized Laplacian + noise
20:40:06 Commencing optimization for 500 epochs, with 206086 positive edges
20:40:21 Optimization finished
[1] "153 0.16"20:40:21 UMAP embedding parameters a = 1.383 b = 0.9596
20:40:21 Read 1203 rows and found 38 numeric columns
20:40:21 Using Annoy for neighbor search, n_neighbors = 153
20:40:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:40:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739ace53b
20:40:22 Searching Annoy index using 1 thread, search_k = 15300
20:40:23 Annoy recall = 100%
20:40:33 Commencing smooth kNN distance calibration using 1 thread
20:40:55 Initializing from normalized Laplacian + noise
20:40:55 Commencing optimization for 500 epochs, with 206086 positive edges
20:41:10 Optimization finished
[1] "153 0.17"20:41:10 UMAP embedding parameters a = 1.352 b = 0.9704
20:41:10 Read 1203 rows and found 38 numeric columns
20:41:10 Using Annoy for neighbor search, n_neighbors = 153
20:41:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:41:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f20318b
20:41:11 Searching Annoy index using 1 thread, search_k = 15300
20:41:12 Annoy recall = 100%
20:41:22 Commencing smooth kNN distance calibration using 1 thread
20:41:44 Initializing from normalized Laplacian + noise
20:41:44 Commencing optimization for 500 epochs, with 206086 positive edges
20:41:59 Optimization finished
[1] "153 0.18"20:41:59 UMAP embedding parameters a = 1.321 b = 0.9813
20:41:59 Read 1203 rows and found 38 numeric columns
20:41:59 Using Annoy for neighbor search, n_neighbors = 153
20:41:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:42:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a75198b
20:42:00 Searching Annoy index using 1 thread, search_k = 15300
20:42:01 Annoy recall = 100%
20:42:11 Commencing smooth kNN distance calibration using 1 thread
20:42:33 Initializing from normalized Laplacian + noise
20:42:33 Commencing optimization for 500 epochs, with 206086 positive edges
20:42:48 Optimization finished
[1] "153 0.19"20:42:48 UMAP embedding parameters a = 1.292 b = 0.9921
20:42:48 Read 1203 rows and found 38 numeric columns
20:42:48 Using Annoy for neighbor search, n_neighbors = 153
20:42:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:42:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748f74560
20:42:49 Searching Annoy index using 1 thread, search_k = 15300
20:42:50 Annoy recall = 100%
20:43:00 Commencing smooth kNN distance calibration using 1 thread
20:43:21 Initializing from normalized Laplacian + noise
20:43:22 Commencing optimization for 500 epochs, with 206086 positive edges
20:43:37 Optimization finished
[1] "153 0.2"20:43:37 UMAP embedding parameters a = 1.262 b = 1.003
20:43:37 Read 1203 rows and found 38 numeric columns
20:43:37 Using Annoy for neighbor search, n_neighbors = 153
20:43:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:43:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875851fb23
20:43:38 Searching Annoy index using 1 thread, search_k = 15300
20:43:39 Annoy recall = 100%
20:43:49 Commencing smooth kNN distance calibration using 1 thread
20:44:10 Initializing from normalized Laplacian + noise
20:44:11 Commencing optimization for 500 epochs, with 206086 positive edges
20:44:25 Optimization finished
[1] "154 0"20:44:26 UMAP embedding parameters a = 1.933 b = 0.7905
20:44:26 Read 1203 rows and found 38 numeric columns
20:44:26 Using Annoy for neighbor search, n_neighbors = 154
20:44:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:44:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87408a51fe
20:44:26 Searching Annoy index using 1 thread, search_k = 15400
20:44:28 Annoy recall = 100%
20:44:38 Commencing smooth kNN distance calibration using 1 thread
20:45:00 Initializing from normalized Laplacian + noise
20:45:00 Commencing optimization for 500 epochs, with 207258 positive edges
20:45:14 Optimization finished
[1] "154 0.01"20:45:15 UMAP embedding parameters a = 1.896 b = 0.8006
20:45:15 Read 1203 rows and found 38 numeric columns
20:45:15 Using Annoy for neighbor search, n_neighbors = 154
20:45:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:45:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a446d4
20:45:15 Searching Annoy index using 1 thread, search_k = 15400
20:45:17 Annoy recall = 100%
20:45:27 Commencing smooth kNN distance calibration using 1 thread
20:45:49 Initializing from normalized Laplacian + noise
20:45:49 Commencing optimization for 500 epochs, with 207258 positive edges
20:46:04 Optimization finished
[1] "154 0.02"20:46:04 UMAP embedding parameters a = 1.859 b = 0.8109
20:46:04 Read 1203 rows and found 38 numeric columns
20:46:04 Using Annoy for neighbor search, n_neighbors = 154
20:46:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e103f12
20:46:05 Searching Annoy index using 1 thread, search_k = 15400
20:46:06 Annoy recall = 100%
20:46:16 Commencing smooth kNN distance calibration using 1 thread
20:46:38 Initializing from normalized Laplacian + noise
20:46:38 Commencing optimization for 500 epochs, with 207258 positive edges
20:46:53 Optimization finished
[1] "154 0.03"20:46:53 UMAP embedding parameters a = 1.822 b = 0.8212
20:46:53 Read 1203 rows and found 38 numeric columns
20:46:53 Using Annoy for neighbor search, n_neighbors = 154
20:46:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d3ed804
20:46:54 Searching Annoy index using 1 thread, search_k = 15400
20:46:55 Annoy recall = 100%
20:47:05 Commencing smooth kNN distance calibration using 1 thread
20:47:27 Initializing from normalized Laplacian + noise
20:47:27 Commencing optimization for 500 epochs, with 207258 positive edges
20:47:42 Optimization finished
[1] "154 0.04"20:47:42 UMAP embedding parameters a = 1.786 b = 0.8316
20:47:42 Read 1203 rows and found 38 numeric columns
20:47:42 Using Annoy for neighbor search, n_neighbors = 154
20:47:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:47:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d74f214
20:47:43 Searching Annoy index using 1 thread, search_k = 15400
20:47:44 Annoy recall = 100%
20:47:54 Commencing smooth kNN distance calibration using 1 thread
20:48:16 Initializing from normalized Laplacian + noise
20:48:16 Commencing optimization for 500 epochs, with 207258 positive edges
20:48:31 Optimization finished
[1] "154 0.05"20:48:31 UMAP embedding parameters a = 1.75 b = 0.8421
20:48:31 Read 1203 rows and found 38 numeric columns
20:48:31 Using Annoy for neighbor search, n_neighbors = 154
20:48:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:48:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736a555c1
20:48:32 Searching Annoy index using 1 thread, search_k = 15400
20:48:33 Annoy recall = 100%
20:48:43 Commencing smooth kNN distance calibration using 1 thread
20:49:05 Initializing from normalized Laplacian + noise
20:49:05 Commencing optimization for 500 epochs, with 207258 positive edges
20:49:20 Optimization finished
[1] "154 0.06"20:49:20 UMAP embedding parameters a = 1.715 b = 0.8526
20:49:20 Read 1203 rows and found 38 numeric columns
20:49:20 Using Annoy for neighbor search, n_neighbors = 154
20:49:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:49:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877979e0a8
20:49:21 Searching Annoy index using 1 thread, search_k = 15400
20:49:22 Annoy recall = 100%
20:49:32 Commencing smooth kNN distance calibration using 1 thread
20:49:54 Initializing from normalized Laplacian + noise
20:49:54 Commencing optimization for 500 epochs, with 207258 positive edges
20:50:09 Optimization finished
[1] "154 0.07"20:50:09 UMAP embedding parameters a = 1.68 b = 0.8631
20:50:09 Read 1203 rows and found 38 numeric columns
20:50:09 Using Annoy for neighbor search, n_neighbors = 154
20:50:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:50:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733ea4fc7
20:50:10 Searching Annoy index using 1 thread, search_k = 15400
20:50:11 Annoy recall = 100%
20:50:22 Commencing smooth kNN distance calibration using 1 thread
20:50:43 Initializing from normalized Laplacian + noise
20:50:43 Commencing optimization for 500 epochs, with 207258 positive edges
20:50:58 Optimization finished
[1] "154 0.08"20:50:58 UMAP embedding parameters a = 1.645 b = 0.8737
20:50:58 Read 1203 rows and found 38 numeric columns
20:50:58 Using Annoy for neighbor search, n_neighbors = 154
20:50:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:50:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744ab9109
20:50:59 Searching Annoy index using 1 thread, search_k = 15400
20:51:00 Annoy recall = 100%
20:51:11 Commencing smooth kNN distance calibration using 1 thread
20:51:32 Initializing from normalized Laplacian + noise
20:51:32 Commencing optimization for 500 epochs, with 207258 positive edges
20:51:47 Optimization finished
[1] "154 0.09"20:51:47 UMAP embedding parameters a = 1.611 b = 0.8844
20:51:47 Read 1203 rows and found 38 numeric columns
20:51:47 Using Annoy for neighbor search, n_neighbors = 154
20:51:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:51:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d51e74
20:51:48 Searching Annoy index using 1 thread, search_k = 15400
20:51:49 Annoy recall = 100%
20:52:00 Commencing smooth kNN distance calibration using 1 thread
20:52:21 Initializing from normalized Laplacian + noise
20:52:21 Commencing optimization for 500 epochs, with 207258 positive edges
20:52:36 Optimization finished
[1] "154 0.1"20:52:36 UMAP embedding parameters a = 1.577 b = 0.8951
20:52:36 Read 1203 rows and found 38 numeric columns
20:52:36 Using Annoy for neighbor search, n_neighbors = 154
20:52:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:52:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d250e2b
20:52:37 Searching Annoy index using 1 thread, search_k = 15400
20:52:38 Annoy recall = 100%
20:52:49 Commencing smooth kNN distance calibration using 1 thread
20:53:10 Initializing from normalized Laplacian + noise
20:53:11 Commencing optimization for 500 epochs, with 207258 positive edges
20:53:26 Optimization finished
[1] "154 0.11"20:53:26 UMAP embedding parameters a = 1.544 b = 0.9058
20:53:26 Read 1203 rows and found 38 numeric columns
20:53:26 Using Annoy for neighbor search, n_neighbors = 154
20:53:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:53:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e43a1d6
20:53:27 Searching Annoy index using 1 thread, search_k = 15400
20:53:28 Annoy recall = 100%
20:53:38 Commencing smooth kNN distance calibration using 1 thread
20:53:59 Initializing from normalized Laplacian + noise
20:54:00 Commencing optimization for 500 epochs, with 207258 positive edges
20:54:15 Optimization finished
[1] "154 0.12"20:54:15 UMAP embedding parameters a = 1.51 b = 0.9165
20:54:15 Read 1203 rows and found 38 numeric columns
20:54:15 Using Annoy for neighbor search, n_neighbors = 154
20:54:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:54:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87299f463a
20:54:16 Searching Annoy index using 1 thread, search_k = 15400
20:54:17 Annoy recall = 100%
20:54:27 Commencing smooth kNN distance calibration using 1 thread
20:54:49 Initializing from normalized Laplacian + noise
20:54:49 Commencing optimization for 500 epochs, with 207258 positive edges
20:55:04 Optimization finished
[1] "154 0.13"20:55:04 UMAP embedding parameters a = 1.478 b = 0.9272
20:55:04 Read 1203 rows and found 38 numeric columns
20:55:04 Using Annoy for neighbor search, n_neighbors = 154
20:55:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:55:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ce978fb
20:55:05 Searching Annoy index using 1 thread, search_k = 15400
20:55:06 Annoy recall = 100%
20:55:16 Commencing smooth kNN distance calibration using 1 thread
20:55:38 Initializing from normalized Laplacian + noise
20:55:38 Commencing optimization for 500 epochs, with 207258 positive edges
20:55:53 Optimization finished
[1] "154 0.14"20:55:53 UMAP embedding parameters a = 1.446 b = 0.938
20:55:53 Read 1203 rows and found 38 numeric columns
20:55:53 Using Annoy for neighbor search, n_neighbors = 154
20:55:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:55:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e7b2b91
20:55:54 Searching Annoy index using 1 thread, search_k = 15400
20:55:55 Annoy recall = 100%
20:56:06 Commencing smooth kNN distance calibration using 1 thread
20:56:27 Initializing from normalized Laplacian + noise
20:56:27 Commencing optimization for 500 epochs, with 207258 positive edges
20:56:42 Optimization finished
[1] "154 0.15"20:56:42 UMAP embedding parameters a = 1.414 b = 0.9488
20:56:43 Read 1203 rows and found 38 numeric columns
20:56:43 Using Annoy for neighbor search, n_neighbors = 154
20:56:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:56:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87738a3687
20:56:43 Searching Annoy index using 1 thread, search_k = 15400
20:56:44 Annoy recall = 100%
20:56:55 Commencing smooth kNN distance calibration using 1 thread
20:57:16 Initializing from normalized Laplacian + noise
20:57:16 Commencing optimization for 500 epochs, with 207258 positive edges
20:57:31 Optimization finished
[1] "154 0.16"20:57:32 UMAP embedding parameters a = 1.383 b = 0.9596
20:57:32 Read 1203 rows and found 38 numeric columns
20:57:32 Using Annoy for neighbor search, n_neighbors = 154
20:57:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:57:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87172f1326
20:57:32 Searching Annoy index using 1 thread, search_k = 15400
20:57:33 Annoy recall = 100%
20:57:44 Commencing smooth kNN distance calibration using 1 thread
20:58:05 Initializing from normalized Laplacian + noise
20:58:06 Commencing optimization for 500 epochs, with 207258 positive edges
20:58:21 Optimization finished
[1] "154 0.17"20:58:21 UMAP embedding parameters a = 1.352 b = 0.9704
20:58:21 Read 1203 rows and found 38 numeric columns
20:58:21 Using Annoy for neighbor search, n_neighbors = 154
20:58:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:58:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87509750fe
20:58:22 Searching Annoy index using 1 thread, search_k = 15400
20:58:23 Annoy recall = 100%
20:58:33 Commencing smooth kNN distance calibration using 1 thread
20:58:55 Initializing from normalized Laplacian + noise
20:58:55 Commencing optimization for 500 epochs, with 207258 positive edges
20:59:10 Optimization finished
[1] "154 0.18"20:59:10 UMAP embedding parameters a = 1.321 b = 0.9813
20:59:10 Read 1203 rows and found 38 numeric columns
20:59:10 Using Annoy for neighbor search, n_neighbors = 154
20:59:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:59:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ebbc750
20:59:11 Searching Annoy index using 1 thread, search_k = 15400
20:59:12 Annoy recall = 100%
20:59:23 Commencing smooth kNN distance calibration using 1 thread
20:59:44 Initializing from normalized Laplacian + noise
20:59:44 Commencing optimization for 500 epochs, with 207258 positive edges
20:59:59 Optimization finished
[1] "154 0.19"20:59:59 UMAP embedding parameters a = 1.292 b = 0.9921
20:59:59 Read 1203 rows and found 38 numeric columns
21:00:00 Using Annoy for neighbor search, n_neighbors = 154
21:00:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:00:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f855560
21:00:00 Searching Annoy index using 1 thread, search_k = 15400
21:00:01 Annoy recall = 100%
21:00:12 Commencing smooth kNN distance calibration using 1 thread
21:00:33 Initializing from normalized Laplacian + noise
21:00:34 Commencing optimization for 500 epochs, with 207258 positive edges
21:00:49 Optimization finished
[1] "154 0.2"21:00:49 UMAP embedding parameters a = 1.262 b = 1.003
21:00:49 Read 1203 rows and found 38 numeric columns
21:00:49 Using Annoy for neighbor search, n_neighbors = 154
21:00:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:00:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873fb692e8
21:00:50 Searching Annoy index using 1 thread, search_k = 15400
21:00:51 Annoy recall = 100%
21:01:01 Commencing smooth kNN distance calibration using 1 thread
21:01:23 Initializing from normalized Laplacian + noise
21:01:23 Commencing optimization for 500 epochs, with 207258 positive edges
21:01:38 Optimization finished
[1] "155 0"21:01:38 UMAP embedding parameters a = 1.933 b = 0.7905
21:01:38 Read 1203 rows and found 38 numeric columns
21:01:38 Using Annoy for neighbor search, n_neighbors = 155
21:01:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:01:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768634b00
21:01:39 Searching Annoy index using 1 thread, search_k = 15500
21:01:40 Annoy recall = 100%
21:01:51 Commencing smooth kNN distance calibration using 1 thread
21:02:12 Initializing from normalized Laplacian + noise
21:02:12 Commencing optimization for 500 epochs, with 208460 positive edges
21:02:27 Optimization finished
[1] "155 0.01"21:02:27 UMAP embedding parameters a = 1.896 b = 0.8006
21:02:27 Read 1203 rows and found 38 numeric columns
21:02:27 Using Annoy for neighbor search, n_neighbors = 155
21:02:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:02:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b96bbf
21:02:28 Searching Annoy index using 1 thread, search_k = 15500
21:02:29 Annoy recall = 100%
21:02:40 Commencing smooth kNN distance calibration using 1 thread
21:03:02 Initializing from normalized Laplacian + noise
21:03:02 Commencing optimization for 500 epochs, with 208460 positive edges
21:03:16 Optimization finished
[1] "155 0.02"21:03:17 UMAP embedding parameters a = 1.859 b = 0.8109
21:03:17 Read 1203 rows and found 38 numeric columns
21:03:17 Using Annoy for neighbor search, n_neighbors = 155
21:03:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:03:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c1df1a2
21:03:18 Searching Annoy index using 1 thread, search_k = 15500
21:03:19 Annoy recall = 100%
21:03:29 Commencing smooth kNN distance calibration using 1 thread
21:03:51 Initializing from normalized Laplacian + noise
21:03:51 Commencing optimization for 500 epochs, with 208460 positive edges
21:04:06 Optimization finished
[1] "155 0.03"21:04:06 UMAP embedding parameters a = 1.822 b = 0.8212
21:04:06 Read 1203 rows and found 38 numeric columns
21:04:06 Using Annoy for neighbor search, n_neighbors = 155
21:04:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:04:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87379875d0
21:04:07 Searching Annoy index using 1 thread, search_k = 15500
21:04:08 Annoy recall = 100%
21:04:19 Commencing smooth kNN distance calibration using 1 thread
21:04:40 Initializing from normalized Laplacian + noise
21:04:40 Commencing optimization for 500 epochs, with 208460 positive edges
21:04:55 Optimization finished
[1] "155 0.04"21:04:56 UMAP embedding parameters a = 1.786 b = 0.8316
21:04:56 Read 1203 rows and found 38 numeric columns
21:04:56 Using Annoy for neighbor search, n_neighbors = 155
21:04:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:04:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fad2cf6
21:04:56 Searching Annoy index using 1 thread, search_k = 15500
21:04:57 Annoy recall = 100%
21:05:08 Commencing smooth kNN distance calibration using 1 thread
21:05:30 Initializing from normalized Laplacian + noise
21:05:30 Commencing optimization for 500 epochs, with 208460 positive edges
21:05:45 Optimization finished
[1] "155 0.05"21:05:45 UMAP embedding parameters a = 1.75 b = 0.8421
21:05:45 Read 1203 rows and found 38 numeric columns
21:05:45 Using Annoy for neighbor search, n_neighbors = 155
21:05:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:05:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725cad6de
21:05:46 Searching Annoy index using 1 thread, search_k = 15500
21:05:47 Annoy recall = 100%
21:05:57 Commencing smooth kNN distance calibration using 1 thread
21:06:19 Initializing from normalized Laplacian + noise
21:06:19 Commencing optimization for 500 epochs, with 208460 positive edges
21:06:34 Optimization finished
[1] "155 0.06"21:06:34 UMAP embedding parameters a = 1.715 b = 0.8526
21:06:34 Read 1203 rows and found 38 numeric columns
21:06:34 Using Annoy for neighbor search, n_neighbors = 155
21:06:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:06:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716b8a75b
21:06:35 Searching Annoy index using 1 thread, search_k = 15500
21:06:36 Annoy recall = 100%
21:06:47 Commencing smooth kNN distance calibration using 1 thread
21:07:08 Initializing from normalized Laplacian + noise
21:07:09 Commencing optimization for 500 epochs, with 208460 positive edges
21:07:24 Optimization finished
[1] "155 0.07"21:07:24 UMAP embedding parameters a = 1.68 b = 0.8631
21:07:24 Read 1203 rows and found 38 numeric columns
21:07:24 Using Annoy for neighbor search, n_neighbors = 155
21:07:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:07:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a224682
21:07:25 Searching Annoy index using 1 thread, search_k = 15500
21:07:26 Annoy recall = 100%
21:07:36 Commencing smooth kNN distance calibration using 1 thread
21:07:58 Initializing from normalized Laplacian + noise
21:07:58 Commencing optimization for 500 epochs, with 208460 positive edges
21:08:13 Optimization finished
[1] "155 0.08"21:08:13 UMAP embedding parameters a = 1.645 b = 0.8737
21:08:13 Read 1203 rows and found 38 numeric columns
21:08:13 Using Annoy for neighbor search, n_neighbors = 155
21:08:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:08:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ec21c3e
21:08:14 Searching Annoy index using 1 thread, search_k = 15500
21:08:15 Annoy recall = 100%
21:08:26 Commencing smooth kNN distance calibration using 1 thread
21:08:47 Initializing from normalized Laplacian + noise
21:08:47 Commencing optimization for 500 epochs, with 208460 positive edges
21:09:02 Optimization finished
[1] "155 0.09"21:09:03 UMAP embedding parameters a = 1.611 b = 0.8844
21:09:03 Read 1203 rows and found 38 numeric columns
21:09:03 Using Annoy for neighbor search, n_neighbors = 155
21:09:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:09:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f0aa27e
21:09:04 Searching Annoy index using 1 thread, search_k = 15500
21:09:05 Annoy recall = 100%
21:09:15 Commencing smooth kNN distance calibration using 1 thread
21:09:37 Initializing from normalized Laplacian + noise
21:09:37 Commencing optimization for 500 epochs, with 208460 positive edges
21:09:52 Optimization finished
[1] "155 0.1"21:09:52 UMAP embedding parameters a = 1.577 b = 0.8951
21:09:52 Read 1203 rows and found 38 numeric columns
21:09:52 Using Annoy for neighbor search, n_neighbors = 155
21:09:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:09:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871aac9880
21:09:53 Searching Annoy index using 1 thread, search_k = 15500
21:09:54 Annoy recall = 100%
21:10:05 Commencing smooth kNN distance calibration using 1 thread
21:10:26 Initializing from normalized Laplacian + noise
21:10:27 Commencing optimization for 500 epochs, with 208460 positive edges
21:10:41 Optimization finished
[1] "155 0.11"21:10:42 UMAP embedding parameters a = 1.544 b = 0.9058
21:10:42 Read 1203 rows and found 38 numeric columns
21:10:42 Using Annoy for neighbor search, n_neighbors = 155
21:10:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:10:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f666312
21:10:42 Searching Annoy index using 1 thread, search_k = 15500
21:10:44 Annoy recall = 100%
21:10:54 Commencing smooth kNN distance calibration using 1 thread
21:11:16 Initializing from normalized Laplacian + noise
21:11:16 Commencing optimization for 500 epochs, with 208460 positive edges
21:11:31 Optimization finished
[1] "155 0.12"21:11:31 UMAP embedding parameters a = 1.51 b = 0.9165
21:11:31 Read 1203 rows and found 38 numeric columns
21:11:31 Using Annoy for neighbor search, n_neighbors = 155
21:11:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:11:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d1ae190
21:11:32 Searching Annoy index using 1 thread, search_k = 15500
21:11:33 Annoy recall = 100%
21:11:44 Commencing smooth kNN distance calibration using 1 thread
21:12:05 Initializing from normalized Laplacian + noise
21:12:05 Commencing optimization for 500 epochs, with 208460 positive edges
21:12:20 Optimization finished
[1] "155 0.13"21:12:21 UMAP embedding parameters a = 1.478 b = 0.9272
21:12:21 Read 1203 rows and found 38 numeric columns
21:12:21 Using Annoy for neighbor search, n_neighbors = 155
21:12:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:12:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877eb7084
21:12:21 Searching Annoy index using 1 thread, search_k = 15500
21:12:22 Annoy recall = 100%
21:12:33 Commencing smooth kNN distance calibration using 1 thread
21:12:55 Initializing from normalized Laplacian + noise
21:12:55 Commencing optimization for 500 epochs, with 208460 positive edges
21:13:10 Optimization finished
[1] "155 0.14"21:13:10 UMAP embedding parameters a = 1.446 b = 0.938
21:13:10 Read 1203 rows and found 38 numeric columns
21:13:10 Using Annoy for neighbor search, n_neighbors = 155
21:13:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:13:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cdb5527
21:13:11 Searching Annoy index using 1 thread, search_k = 15500
21:13:12 Annoy recall = 100%
21:13:23 Commencing smooth kNN distance calibration using 1 thread
21:13:44 Initializing from normalized Laplacian + noise
21:13:45 Commencing optimization for 500 epochs, with 208460 positive edges
21:13:59 Optimization finished
[1] "155 0.15"21:14:00 UMAP embedding parameters a = 1.414 b = 0.9488
21:14:00 Read 1203 rows and found 38 numeric columns
21:14:00 Using Annoy for neighbor search, n_neighbors = 155
21:14:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:14:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c03751
21:14:00 Searching Annoy index using 1 thread, search_k = 15500
21:14:02 Annoy recall = 100%
21:14:12 Commencing smooth kNN distance calibration using 1 thread
21:14:34 Initializing from normalized Laplacian + noise
21:14:34 Commencing optimization for 500 epochs, with 208460 positive edges
21:14:49 Optimization finished
[1] "155 0.16"21:14:49 UMAP embedding parameters a = 1.383 b = 0.9596
21:14:49 Read 1203 rows and found 38 numeric columns
21:14:49 Using Annoy for neighbor search, n_neighbors = 155
21:14:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:14:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87165512c
21:14:50 Searching Annoy index using 1 thread, search_k = 15500
21:14:51 Annoy recall = 100%
21:15:02 Commencing smooth kNN distance calibration using 1 thread
21:15:23 Initializing from normalized Laplacian + noise
21:15:23 Commencing optimization for 500 epochs, with 208460 positive edges
21:15:39 Optimization finished
[1] "155 0.17"21:15:39 UMAP embedding parameters a = 1.352 b = 0.9704
21:15:39 Read 1203 rows and found 38 numeric columns
21:15:39 Using Annoy for neighbor search, n_neighbors = 155
21:15:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:15:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760c5a4ee
21:15:40 Searching Annoy index using 1 thread, search_k = 15500
21:15:41 Annoy recall = 100%
21:15:51 Commencing smooth kNN distance calibration using 1 thread
21:16:13 Initializing from normalized Laplacian + noise
21:16:13 Commencing optimization for 500 epochs, with 208460 positive edges
21:16:28 Optimization finished
[1] "155 0.18"21:16:28 UMAP embedding parameters a = 1.321 b = 0.9813
21:16:28 Read 1203 rows and found 38 numeric columns
21:16:28 Using Annoy for neighbor search, n_neighbors = 155
21:16:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:16:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87486bc85a
21:16:29 Searching Annoy index using 1 thread, search_k = 15500
21:16:30 Annoy recall = 100%
21:16:41 Commencing smooth kNN distance calibration using 1 thread
21:17:03 Initializing from normalized Laplacian + noise
21:17:03 Commencing optimization for 500 epochs, with 208460 positive edges
21:17:18 Optimization finished
[1] "155 0.19"21:17:18 UMAP embedding parameters a = 1.292 b = 0.9921
21:17:18 Read 1203 rows and found 38 numeric columns
21:17:18 Using Annoy for neighbor search, n_neighbors = 155
21:17:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:17:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8783a6fa0
21:17:19 Searching Annoy index using 1 thread, search_k = 15500
21:17:20 Annoy recall = 100%
21:17:30 Commencing smooth kNN distance calibration using 1 thread
21:17:52 Initializing from normalized Laplacian + noise
21:17:52 Commencing optimization for 500 epochs, with 208460 positive edges
21:18:07 Optimization finished
[1] "155 0.2"21:18:08 UMAP embedding parameters a = 1.262 b = 1.003
21:18:08 Read 1203 rows and found 38 numeric columns
21:18:08 Using Annoy for neighbor search, n_neighbors = 155
21:18:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:18:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871deab319
21:18:08 Searching Annoy index using 1 thread, search_k = 15500
21:18:09 Annoy recall = 100%
21:18:20 Commencing smooth kNN distance calibration using 1 thread
21:18:42 Initializing from normalized Laplacian + noise
21:18:42 Commencing optimization for 500 epochs, with 208460 positive edges
21:18:57 Optimization finished
[1] "156 0"21:18:57 UMAP embedding parameters a = 1.933 b = 0.7905
21:18:57 Read 1203 rows and found 38 numeric columns
21:18:58 Using Annoy for neighbor search, n_neighbors = 156
21:18:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:18:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736af6a31
21:18:58 Searching Annoy index using 1 thread, search_k = 15600
21:18:59 Annoy recall = 100%
21:19:10 Commencing smooth kNN distance calibration using 1 thread
21:19:32 Initializing from normalized Laplacian + noise
21:19:32 Commencing optimization for 500 epochs, with 209724 positive edges
21:19:47 Optimization finished
[1] "156 0.01"21:19:47 UMAP embedding parameters a = 1.896 b = 0.8006
21:19:47 Read 1203 rows and found 38 numeric columns
21:19:47 Using Annoy for neighbor search, n_neighbors = 156
21:19:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:19:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731d9b5db
21:19:48 Searching Annoy index using 1 thread, search_k = 15600
21:19:49 Annoy recall = 100%
21:20:00 Commencing smooth kNN distance calibration using 1 thread
21:20:22 Initializing from normalized Laplacian + noise
21:20:22 Commencing optimization for 500 epochs, with 209724 positive edges
21:20:37 Optimization finished
[1] "156 0.02"21:20:37 UMAP embedding parameters a = 1.859 b = 0.8109
21:20:37 Read 1203 rows and found 38 numeric columns
21:20:37 Using Annoy for neighbor search, n_neighbors = 156
21:20:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:20:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ad42c14
21:20:38 Searching Annoy index using 1 thread, search_k = 15600
21:20:39 Annoy recall = 100%
21:20:50 Commencing smooth kNN distance calibration using 1 thread
21:21:12 Initializing from normalized Laplacian + noise
21:21:12 Commencing optimization for 500 epochs, with 209724 positive edges
21:21:26 Optimization finished
[1] "156 0.03"21:21:27 UMAP embedding parameters a = 1.822 b = 0.8212
21:21:27 Read 1203 rows and found 38 numeric columns
21:21:27 Using Annoy for neighbor search, n_neighbors = 156
21:21:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:21:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752a95c2
21:21:28 Searching Annoy index using 1 thread, search_k = 15600
21:21:29 Annoy recall = 100%
21:21:39 Commencing smooth kNN distance calibration using 1 thread
21:22:01 Initializing from normalized Laplacian + noise
21:22:01 Commencing optimization for 500 epochs, with 209724 positive edges
21:22:16 Optimization finished
[1] "156 0.04"21:22:17 UMAP embedding parameters a = 1.786 b = 0.8316
21:22:17 Read 1203 rows and found 38 numeric columns
21:22:17 Using Annoy for neighbor search, n_neighbors = 156
21:22:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:22:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872563ec62
21:22:17 Searching Annoy index using 1 thread, search_k = 15600
21:22:18 Annoy recall = 100%
21:22:29 Commencing smooth kNN distance calibration using 1 thread
21:22:51 Initializing from normalized Laplacian + noise
21:22:51 Commencing optimization for 500 epochs, with 209724 positive edges
21:23:06 Optimization finished
[1] "156 0.05"21:23:06 UMAP embedding parameters a = 1.75 b = 0.8421
21:23:06 Read 1203 rows and found 38 numeric columns
21:23:06 Using Annoy for neighbor search, n_neighbors = 156
21:23:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:23:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772033f3b
21:23:07 Searching Annoy index using 1 thread, search_k = 15600
21:23:08 Annoy recall = 100%
21:23:19 Commencing smooth kNN distance calibration using 1 thread
21:23:40 Initializing from normalized Laplacian + noise
21:23:41 Commencing optimization for 500 epochs, with 209724 positive edges
21:23:56 Optimization finished
[1] "156 0.06"21:23:56 UMAP embedding parameters a = 1.715 b = 0.8526
21:23:56 Read 1203 rows and found 38 numeric columns
21:23:56 Using Annoy for neighbor search, n_neighbors = 156
21:23:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:23:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755c1e6c0
21:23:57 Searching Annoy index using 1 thread, search_k = 15600
21:23:58 Annoy recall = 100%
21:24:09 Commencing smooth kNN distance calibration using 1 thread
21:24:30 Initializing from normalized Laplacian + noise
21:24:30 Commencing optimization for 500 epochs, with 209724 positive edges
21:24:45 Optimization finished
[1] "156 0.07"21:24:46 UMAP embedding parameters a = 1.68 b = 0.8631
21:24:46 Read 1203 rows and found 38 numeric columns
21:24:46 Using Annoy for neighbor search, n_neighbors = 156
21:24:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:24:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741fb3b2
21:24:46 Searching Annoy index using 1 thread, search_k = 15600
21:24:47 Annoy recall = 100%
21:24:58 Commencing smooth kNN distance calibration using 1 thread
21:25:20 Initializing from normalized Laplacian + noise
21:25:20 Commencing optimization for 500 epochs, with 209724 positive edges
21:25:35 Optimization finished
[1] "156 0.08"21:25:36 UMAP embedding parameters a = 1.645 b = 0.8737
21:25:36 Read 1203 rows and found 38 numeric columns
21:25:36 Using Annoy for neighbor search, n_neighbors = 156
21:25:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:25:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876188949b
21:25:36 Searching Annoy index using 1 thread, search_k = 15600
21:25:38 Annoy recall = 100%
21:25:48 Commencing smooth kNN distance calibration using 1 thread
21:26:10 Initializing from normalized Laplacian + noise
21:26:10 Commencing optimization for 500 epochs, with 209724 positive edges
21:26:25 Optimization finished
[1] "156 0.09"21:26:26 UMAP embedding parameters a = 1.611 b = 0.8844
21:26:26 Read 1203 rows and found 38 numeric columns
21:26:26 Using Annoy for neighbor search, n_neighbors = 156
21:26:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:26:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87157879a9
21:26:26 Searching Annoy index using 1 thread, search_k = 15600
21:26:28 Annoy recall = 100%
21:26:38 Commencing smooth kNN distance calibration using 1 thread
21:27:00 Initializing from normalized Laplacian + noise
21:27:00 Commencing optimization for 500 epochs, with 209724 positive edges
21:27:15 Optimization finished
[1] "156 0.1"21:27:15 UMAP embedding parameters a = 1.577 b = 0.8951
21:27:15 Read 1203 rows and found 38 numeric columns
21:27:15 Using Annoy for neighbor search, n_neighbors = 156
21:27:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:27:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c82feb3
21:27:16 Searching Annoy index using 1 thread, search_k = 15600
21:27:17 Annoy recall = 100%
21:27:28 Commencing smooth kNN distance calibration using 1 thread
21:27:50 Initializing from normalized Laplacian + noise
21:27:50 Commencing optimization for 500 epochs, with 209724 positive edges
21:28:05 Optimization finished
[1] "156 0.11"21:28:05 UMAP embedding parameters a = 1.544 b = 0.9058
21:28:05 Read 1203 rows and found 38 numeric columns
21:28:05 Using Annoy for neighbor search, n_neighbors = 156
21:28:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876542005a
21:28:06 Searching Annoy index using 1 thread, search_k = 15600
21:28:07 Annoy recall = 100%
21:28:18 Commencing smooth kNN distance calibration using 1 thread
21:28:40 Initializing from normalized Laplacian + noise
21:28:40 Commencing optimization for 500 epochs, with 209724 positive edges
21:28:55 Optimization finished
[1] "156 0.12"21:28:55 UMAP embedding parameters a = 1.51 b = 0.9165
21:28:55 Read 1203 rows and found 38 numeric columns
21:28:55 Using Annoy for neighbor search, n_neighbors = 156
21:28:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:28:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871966b4b
21:28:56 Searching Annoy index using 1 thread, search_k = 15600
21:28:57 Annoy recall = 100%
21:29:08 Commencing smooth kNN distance calibration using 1 thread
21:29:29 Initializing from normalized Laplacian + noise
21:29:30 Commencing optimization for 500 epochs, with 209724 positive edges
21:29:45 Optimization finished
[1] "156 0.13"21:29:45 UMAP embedding parameters a = 1.478 b = 0.9272
21:29:45 Read 1203 rows and found 38 numeric columns
21:29:45 Using Annoy for neighbor search, n_neighbors = 156
21:29:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:29:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87241b7483
21:29:46 Searching Annoy index using 1 thread, search_k = 15600
21:29:47 Annoy recall = 100%
21:29:57 Commencing smooth kNN distance calibration using 1 thread
21:30:19 Initializing from normalized Laplacian + noise
21:30:19 Commencing optimization for 500 epochs, with 209724 positive edges
21:30:34 Optimization finished
[1] "156 0.14"21:30:35 UMAP embedding parameters a = 1.446 b = 0.938
21:30:35 Read 1203 rows and found 38 numeric columns
21:30:35 Using Annoy for neighbor search, n_neighbors = 156
21:30:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:30:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764ef2d51
21:30:35 Searching Annoy index using 1 thread, search_k = 15600
21:30:37 Annoy recall = 100%
21:30:47 Commencing smooth kNN distance calibration using 1 thread
21:31:09 Initializing from normalized Laplacian + noise
21:31:09 Commencing optimization for 500 epochs, with 209724 positive edges
21:31:24 Optimization finished
[1] "156 0.15"21:31:24 UMAP embedding parameters a = 1.414 b = 0.9488
21:31:24 Read 1203 rows and found 38 numeric columns
21:31:24 Using Annoy for neighbor search, n_neighbors = 156
21:31:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:31:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727614229
21:31:25 Searching Annoy index using 1 thread, search_k = 15600
21:31:26 Annoy recall = 100%
21:31:37 Commencing smooth kNN distance calibration using 1 thread
21:31:59 Initializing from normalized Laplacian + noise
21:31:59 Commencing optimization for 500 epochs, with 209724 positive edges
21:32:14 Optimization finished
[1] "156 0.16"21:32:14 UMAP embedding parameters a = 1.383 b = 0.9596
21:32:14 Read 1203 rows and found 38 numeric columns
21:32:14 Using Annoy for neighbor search, n_neighbors = 156
21:32:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:32:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ad41bde
21:32:15 Searching Annoy index using 1 thread, search_k = 15600
21:32:16 Annoy recall = 100%
21:32:27 Commencing smooth kNN distance calibration using 1 thread
21:32:49 Initializing from normalized Laplacian + noise
21:32:49 Commencing optimization for 500 epochs, with 209724 positive edges
21:33:04 Optimization finished
[1] "156 0.17"21:33:04 UMAP embedding parameters a = 1.352 b = 0.9704
21:33:04 Read 1203 rows and found 38 numeric columns
21:33:04 Using Annoy for neighbor search, n_neighbors = 156
21:33:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f1173d3
21:33:05 Searching Annoy index using 1 thread, search_k = 15600
21:33:06 Annoy recall = 100%
21:33:17 Commencing smooth kNN distance calibration using 1 thread
21:33:39 Initializing from normalized Laplacian + noise
21:33:39 Commencing optimization for 500 epochs, with 209724 positive edges
21:33:54 Optimization finished
[1] "156 0.18"21:33:54 UMAP embedding parameters a = 1.321 b = 0.9813
21:33:54 Read 1203 rows and found 38 numeric columns
21:33:54 Using Annoy for neighbor search, n_neighbors = 156
21:33:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:33:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716235e68
21:33:55 Searching Annoy index using 1 thread, search_k = 15600
21:33:56 Annoy recall = 100%
21:34:07 Commencing smooth kNN distance calibration using 1 thread
21:34:29 Initializing from normalized Laplacian + noise
21:34:29 Commencing optimization for 500 epochs, with 209724 positive edges
21:34:44 Optimization finished
[1] "156 0.19"21:34:44 UMAP embedding parameters a = 1.292 b = 0.9921
21:34:45 Read 1203 rows and found 38 numeric columns
21:34:45 Using Annoy for neighbor search, n_neighbors = 156
21:34:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:34:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729debe5c
21:34:46 Searching Annoy index using 1 thread, search_k = 15600
21:34:47 Annoy recall = 100%
21:35:02 Commencing smooth kNN distance calibration using 1 thread
21:35:26 Initializing from normalized Laplacian + noise
21:35:26 Commencing optimization for 500 epochs, with 209724 positive edges
21:35:41 Optimization finished
[1] "156 0.2"21:35:41 UMAP embedding parameters a = 1.262 b = 1.003
21:35:41 Read 1203 rows and found 38 numeric columns
21:35:41 Using Annoy for neighbor search, n_neighbors = 156
21:35:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:35:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759be0c53
21:35:42 Searching Annoy index using 1 thread, search_k = 15600
21:35:43 Annoy recall = 100%
21:35:54 Commencing smooth kNN distance calibration using 1 thread
21:36:15 Initializing from normalized Laplacian + noise
21:36:15 Commencing optimization for 500 epochs, with 209724 positive edges
21:36:30 Optimization finished
[1] "157 0"21:36:30 UMAP embedding parameters a = 1.933 b = 0.7905
21:36:30 Read 1203 rows and found 38 numeric columns
21:36:30 Using Annoy for neighbor search, n_neighbors = 157
21:36:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:36:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87589c17a
21:36:31 Searching Annoy index using 1 thread, search_k = 15700
21:36:32 Annoy recall = 100%
21:36:42 Commencing smooth kNN distance calibration using 1 thread
21:37:04 Initializing from normalized Laplacian + noise
21:37:04 Commencing optimization for 500 epochs, with 210892 positive edges
21:37:19 Optimization finished
[1] "157 0.01"21:37:19 UMAP embedding parameters a = 1.896 b = 0.8006
21:37:19 Read 1203 rows and found 38 numeric columns
21:37:19 Using Annoy for neighbor search, n_neighbors = 157
21:37:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:37:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776f99fec
21:37:20 Searching Annoy index using 1 thread, search_k = 15700
21:37:21 Annoy recall = 100%
21:37:31 Commencing smooth kNN distance calibration using 1 thread
21:37:53 Initializing from normalized Laplacian + noise
21:37:53 Commencing optimization for 500 epochs, with 210892 positive edges
21:38:08 Optimization finished
[1] "157 0.02"21:38:08 UMAP embedding parameters a = 1.859 b = 0.8109
21:38:08 Read 1203 rows and found 38 numeric columns
21:38:08 Using Annoy for neighbor search, n_neighbors = 157
21:38:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761a97cd7
21:38:09 Searching Annoy index using 1 thread, search_k = 15700
21:38:10 Annoy recall = 100%
21:38:20 Commencing smooth kNN distance calibration using 1 thread
21:38:41 Initializing from normalized Laplacian + noise
21:38:42 Commencing optimization for 500 epochs, with 210892 positive edges
21:38:56 Optimization finished
[1] "157 0.03"21:38:57 UMAP embedding parameters a = 1.822 b = 0.8212
21:38:57 Read 1203 rows and found 38 numeric columns
21:38:57 Using Annoy for neighbor search, n_neighbors = 157
21:38:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:38:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87326516a1
21:38:57 Searching Annoy index using 1 thread, search_k = 15700
21:38:59 Annoy recall = 100%
21:39:09 Commencing smooth kNN distance calibration using 1 thread
21:39:30 Initializing from normalized Laplacian + noise
21:39:30 Commencing optimization for 500 epochs, with 210892 positive edges
21:39:45 Optimization finished
[1] "157 0.04"21:39:46 UMAP embedding parameters a = 1.786 b = 0.8316
21:39:46 Read 1203 rows and found 38 numeric columns
21:39:46 Using Annoy for neighbor search, n_neighbors = 157
21:39:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:39:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ab9d73d
21:39:46 Searching Annoy index using 1 thread, search_k = 15700
21:39:47 Annoy recall = 100%
21:39:58 Commencing smooth kNN distance calibration using 1 thread
21:40:19 Initializing from normalized Laplacian + noise
21:40:20 Commencing optimization for 500 epochs, with 210892 positive edges
21:40:34 Optimization finished
[1] "157 0.05"21:40:34 UMAP embedding parameters a = 1.75 b = 0.8421
21:40:34 Read 1203 rows and found 38 numeric columns
21:40:34 Using Annoy for neighbor search, n_neighbors = 157
21:40:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:40:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87630ece04
21:40:35 Searching Annoy index using 1 thread, search_k = 15700
21:40:36 Annoy recall = 100%
21:40:47 Commencing smooth kNN distance calibration using 1 thread
21:41:09 Initializing from normalized Laplacian + noise
21:41:09 Commencing optimization for 500 epochs, with 210892 positive edges
21:41:24 Optimization finished
[1] "157 0.06"21:41:24 UMAP embedding parameters a = 1.715 b = 0.8526
21:41:25 Read 1203 rows and found 38 numeric columns
21:41:25 Using Annoy for neighbor search, n_neighbors = 157
21:41:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87132abb90
21:41:25 Searching Annoy index using 1 thread, search_k = 15700
21:41:26 Annoy recall = 100%
21:41:37 Commencing smooth kNN distance calibration using 1 thread
21:41:59 Initializing from normalized Laplacian + noise
21:41:59 Commencing optimization for 500 epochs, with 210892 positive edges
21:42:15 Optimization finished
[1] "157 0.07"21:42:15 UMAP embedding parameters a = 1.68 b = 0.8631
21:42:15 Read 1203 rows and found 38 numeric columns
21:42:15 Using Annoy for neighbor search, n_neighbors = 157
21:42:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743259f97
21:42:16 Searching Annoy index using 1 thread, search_k = 15700
21:42:17 Annoy recall = 100%
21:42:28 Commencing smooth kNN distance calibration using 1 thread
21:42:50 Initializing from normalized Laplacian + noise
21:42:50 Commencing optimization for 500 epochs, with 210892 positive edges
21:43:05 Optimization finished
[1] "157 0.08"21:43:05 UMAP embedding parameters a = 1.645 b = 0.8737
21:43:05 Read 1203 rows and found 38 numeric columns
21:43:05 Using Annoy for neighbor search, n_neighbors = 157
21:43:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b493da4
21:43:06 Searching Annoy index using 1 thread, search_k = 15700
21:43:07 Annoy recall = 100%
21:43:18 Commencing smooth kNN distance calibration using 1 thread
21:43:40 Initializing from normalized Laplacian + noise
21:43:40 Commencing optimization for 500 epochs, with 210892 positive edges
21:43:55 Optimization finished
[1] "157 0.09"21:43:55 UMAP embedding parameters a = 1.611 b = 0.8844
21:43:56 Read 1203 rows and found 38 numeric columns
21:43:56 Using Annoy for neighbor search, n_neighbors = 157
21:43:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:43:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731156ea9
21:43:56 Searching Annoy index using 1 thread, search_k = 15700
21:43:57 Annoy recall = 100%
21:44:08 Commencing smooth kNN distance calibration using 1 thread
21:44:31 Initializing from normalized Laplacian + noise
21:44:31 Commencing optimization for 500 epochs, with 210892 positive edges
21:44:46 Optimization finished
[1] "157 0.1"21:44:46 UMAP embedding parameters a = 1.577 b = 0.8951
21:44:46 Read 1203 rows and found 38 numeric columns
21:44:46 Using Annoy for neighbor search, n_neighbors = 157
21:44:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:44:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779d509c8
21:44:47 Searching Annoy index using 1 thread, search_k = 15700
21:44:48 Annoy recall = 100%
21:44:59 Commencing smooth kNN distance calibration using 1 thread
21:45:21 Initializing from normalized Laplacian + noise
21:45:21 Commencing optimization for 500 epochs, with 210892 positive edges
21:45:37 Optimization finished
[1] "157 0.11"21:45:37 UMAP embedding parameters a = 1.544 b = 0.9058
21:45:37 Read 1203 rows and found 38 numeric columns
21:45:37 Using Annoy for neighbor search, n_neighbors = 157
21:45:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:45:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d22f37f
21:45:38 Searching Annoy index using 1 thread, search_k = 15700
21:45:39 Annoy recall = 100%
21:45:51 Commencing smooth kNN distance calibration using 1 thread
21:46:14 Initializing from normalized Laplacian + noise
21:46:14 Commencing optimization for 500 epochs, with 210892 positive edges
21:46:30 Optimization finished
[1] "157 0.12"21:46:30 UMAP embedding parameters a = 1.51 b = 0.9165
21:46:30 Read 1203 rows and found 38 numeric columns
21:46:30 Using Annoy for neighbor search, n_neighbors = 157
21:46:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:46:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87be99abe
21:46:31 Searching Annoy index using 1 thread, search_k = 15700
21:46:32 Annoy recall = 100%
21:46:43 Commencing smooth kNN distance calibration using 1 thread
21:47:04 Initializing from normalized Laplacian + noise
21:47:05 Commencing optimization for 500 epochs, with 210892 positive edges
21:47:20 Optimization finished
[1] "157 0.13"21:47:20 UMAP embedding parameters a = 1.478 b = 0.9272
21:47:20 Read 1203 rows and found 38 numeric columns
21:47:20 Using Annoy for neighbor search, n_neighbors = 157
21:47:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:47:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877eff9f8a
21:47:21 Searching Annoy index using 1 thread, search_k = 15700
21:47:22 Annoy recall = 100%
21:47:33 Commencing smooth kNN distance calibration using 1 thread
21:47:54 Initializing from normalized Laplacian + noise
21:47:54 Commencing optimization for 500 epochs, with 210892 positive edges
21:48:09 Optimization finished
[1] "157 0.14"21:48:09 UMAP embedding parameters a = 1.446 b = 0.938
21:48:09 Read 1203 rows and found 38 numeric columns
21:48:09 Using Annoy for neighbor search, n_neighbors = 157
21:48:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874286dfe1
21:48:10 Searching Annoy index using 1 thread, search_k = 15700
21:48:11 Annoy recall = 100%
21:48:22 Commencing smooth kNN distance calibration using 1 thread
21:48:43 Initializing from normalized Laplacian + noise
21:48:43 Commencing optimization for 500 epochs, with 210892 positive edges
21:48:58 Optimization finished
[1] "157 0.15"21:48:58 UMAP embedding parameters a = 1.414 b = 0.9488
21:48:58 Read 1203 rows and found 38 numeric columns
21:48:59 Using Annoy for neighbor search, n_neighbors = 157
21:48:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:48:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877decd9f9
21:48:59 Searching Annoy index using 1 thread, search_k = 15700
21:49:00 Annoy recall = 100%
21:49:11 Commencing smooth kNN distance calibration using 1 thread
21:49:33 Initializing from normalized Laplacian + noise
21:49:33 Commencing optimization for 500 epochs, with 210892 positive edges
21:49:48 Optimization finished
[1] "157 0.16"21:49:48 UMAP embedding parameters a = 1.383 b = 0.9596
21:49:48 Read 1203 rows and found 38 numeric columns
21:49:48 Using Annoy for neighbor search, n_neighbors = 157
21:49:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:49:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754c1864b
21:49:49 Searching Annoy index using 1 thread, search_k = 15700
21:49:50 Annoy recall = 100%
21:50:00 Commencing smooth kNN distance calibration using 1 thread
21:50:22 Initializing from normalized Laplacian + noise
21:50:22 Commencing optimization for 500 epochs, with 210892 positive edges
21:50:37 Optimization finished
[1] "157 0.17"21:50:37 UMAP embedding parameters a = 1.352 b = 0.9704
21:50:37 Read 1203 rows and found 38 numeric columns
21:50:37 Using Annoy for neighbor search, n_neighbors = 157
21:50:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:50:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746a69394
21:50:38 Searching Annoy index using 1 thread, search_k = 15700
21:50:39 Annoy recall = 100%
21:50:50 Commencing smooth kNN distance calibration using 1 thread
21:51:11 Initializing from normalized Laplacian + noise
21:51:11 Commencing optimization for 500 epochs, with 210892 positive edges
21:51:26 Optimization finished
[1] "157 0.18"21:51:26 UMAP embedding parameters a = 1.321 b = 0.9813
21:51:26 Read 1203 rows and found 38 numeric columns
21:51:26 Using Annoy for neighbor search, n_neighbors = 157
21:51:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:51:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f756e94
21:51:27 Searching Annoy index using 1 thread, search_k = 15700
21:51:28 Annoy recall = 100%
21:51:39 Commencing smooth kNN distance calibration using 1 thread
21:52:01 Initializing from normalized Laplacian + noise
21:52:01 Commencing optimization for 500 epochs, with 210892 positive edges
21:52:15 Optimization finished
[1] "157 0.19"21:52:16 UMAP embedding parameters a = 1.292 b = 0.9921
21:52:16 Read 1203 rows and found 38 numeric columns
21:52:16 Using Annoy for neighbor search, n_neighbors = 157
21:52:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:52:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a39fff4
21:52:16 Searching Annoy index using 1 thread, search_k = 15700
21:52:18 Annoy recall = 100%
21:52:28 Commencing smooth kNN distance calibration using 1 thread
21:52:50 Initializing from normalized Laplacian + noise
21:52:50 Commencing optimization for 500 epochs, with 210892 positive edges
21:53:05 Optimization finished
[1] "157 0.2"21:53:05 UMAP embedding parameters a = 1.262 b = 1.003
21:53:05 Read 1203 rows and found 38 numeric columns
21:53:05 Using Annoy for neighbor search, n_neighbors = 157
21:53:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733299247
21:53:06 Searching Annoy index using 1 thread, search_k = 15700
21:53:07 Annoy recall = 100%
21:53:18 Commencing smooth kNN distance calibration using 1 thread
21:53:39 Initializing from normalized Laplacian + noise
21:53:39 Commencing optimization for 500 epochs, with 210892 positive edges
21:53:54 Optimization finished
[1] "158 0"21:53:55 UMAP embedding parameters a = 1.933 b = 0.7905
21:53:55 Read 1203 rows and found 38 numeric columns
21:53:55 Using Annoy for neighbor search, n_neighbors = 158
21:53:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:53:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744b76eef
21:53:55 Searching Annoy index using 1 thread, search_k = 15800
21:53:57 Annoy recall = 100%
21:54:07 Commencing smooth kNN distance calibration using 1 thread
21:54:29 Initializing from normalized Laplacian + noise
21:54:29 Commencing optimization for 500 epochs, with 212082 positive edges
21:54:44 Optimization finished
[1] "158 0.01"21:54:44 UMAP embedding parameters a = 1.896 b = 0.8006
21:54:44 Read 1203 rows and found 38 numeric columns
21:54:44 Using Annoy for neighbor search, n_neighbors = 158
21:54:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:54:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876bd06b3f
21:54:45 Searching Annoy index using 1 thread, search_k = 15800
21:54:46 Annoy recall = 100%
21:54:57 Commencing smooth kNN distance calibration using 1 thread
21:55:18 Initializing from normalized Laplacian + noise
21:55:18 Commencing optimization for 500 epochs, with 212082 positive edges
21:55:33 Optimization finished
[1] "158 0.02"21:55:33 UMAP embedding parameters a = 1.859 b = 0.8109
21:55:33 Read 1203 rows and found 38 numeric columns
21:55:33 Using Annoy for neighbor search, n_neighbors = 158
21:55:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:55:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87574506ca
21:55:34 Searching Annoy index using 1 thread, search_k = 15800
21:55:35 Annoy recall = 100%
21:55:46 Commencing smooth kNN distance calibration using 1 thread
21:56:08 Initializing from normalized Laplacian + noise
21:56:08 Commencing optimization for 500 epochs, with 212082 positive edges
21:56:23 Optimization finished
[1] "158 0.03"21:56:23 UMAP embedding parameters a = 1.822 b = 0.8212
21:56:23 Read 1203 rows and found 38 numeric columns
21:56:23 Using Annoy for neighbor search, n_neighbors = 158
21:56:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:56:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729a69c40
21:56:24 Searching Annoy index using 1 thread, search_k = 15800
21:56:25 Annoy recall = 100%
21:56:35 Commencing smooth kNN distance calibration using 1 thread
21:56:57 Initializing from normalized Laplacian + noise
21:56:57 Commencing optimization for 500 epochs, with 212082 positive edges
21:57:12 Optimization finished
[1] "158 0.04"21:57:13 UMAP embedding parameters a = 1.786 b = 0.8316
21:57:13 Read 1203 rows and found 38 numeric columns
21:57:13 Using Annoy for neighbor search, n_neighbors = 158
21:57:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:57:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871331ad69
21:57:13 Searching Annoy index using 1 thread, search_k = 15800
21:57:14 Annoy recall = 100%
21:57:25 Commencing smooth kNN distance calibration using 1 thread
21:57:47 Initializing from normalized Laplacian + noise
21:57:47 Commencing optimization for 500 epochs, with 212082 positive edges
21:58:02 Optimization finished
[1] "158 0.05"21:58:02 UMAP embedding parameters a = 1.75 b = 0.8421
21:58:02 Read 1203 rows and found 38 numeric columns
21:58:02 Using Annoy for neighbor search, n_neighbors = 158
21:58:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87121922a8
21:58:03 Searching Annoy index using 1 thread, search_k = 15800
21:58:04 Annoy recall = 100%
21:58:15 Commencing smooth kNN distance calibration using 1 thread
21:58:36 Initializing from normalized Laplacian + noise
21:58:36 Commencing optimization for 500 epochs, with 212082 positive edges
21:58:51 Optimization finished
[1] "158 0.06"21:58:51 UMAP embedding parameters a = 1.715 b = 0.8526
21:58:51 Read 1203 rows and found 38 numeric columns
21:58:51 Using Annoy for neighbor search, n_neighbors = 158
21:58:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:58:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768b81013
21:58:52 Searching Annoy index using 1 thread, search_k = 15800
21:58:53 Annoy recall = 100%
21:59:04 Commencing smooth kNN distance calibration using 1 thread
21:59:26 Initializing from normalized Laplacian + noise
21:59:26 Commencing optimization for 500 epochs, with 212082 positive edges
21:59:41 Optimization finished
[1] "158 0.07"21:59:41 UMAP embedding parameters a = 1.68 b = 0.8631
21:59:41 Read 1203 rows and found 38 numeric columns
21:59:41 Using Annoy for neighbor search, n_neighbors = 158
21:59:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:59:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729550bd1
21:59:42 Searching Annoy index using 1 thread, search_k = 15800
21:59:43 Annoy recall = 100%
21:59:54 Commencing smooth kNN distance calibration using 1 thread
22:00:15 Initializing from normalized Laplacian + noise
22:00:15 Commencing optimization for 500 epochs, with 212082 positive edges
22:00:30 Optimization finished
[1] "158 0.08"22:00:30 UMAP embedding parameters a = 1.645 b = 0.8737
22:00:30 Read 1203 rows and found 38 numeric columns
22:00:30 Using Annoy for neighbor search, n_neighbors = 158
22:00:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:00:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bf7e104
22:00:31 Searching Annoy index using 1 thread, search_k = 15800
22:00:32 Annoy recall = 100%
22:00:43 Commencing smooth kNN distance calibration using 1 thread
22:01:05 Initializing from normalized Laplacian + noise
22:01:05 Commencing optimization for 500 epochs, with 212082 positive edges
22:01:20 Optimization finished
[1] "158 0.09"22:01:20 UMAP embedding parameters a = 1.611 b = 0.8844
22:01:20 Read 1203 rows and found 38 numeric columns
22:01:20 Using Annoy for neighbor search, n_neighbors = 158
22:01:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:01:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742761c66
22:01:21 Searching Annoy index using 1 thread, search_k = 15800
22:01:22 Annoy recall = 100%
22:01:33 Commencing smooth kNN distance calibration using 1 thread
22:01:54 Initializing from normalized Laplacian + noise
22:01:54 Commencing optimization for 500 epochs, with 212082 positive edges
22:02:09 Optimization finished
[1] "158 0.1"22:02:09 UMAP embedding parameters a = 1.577 b = 0.8951
22:02:09 Read 1203 rows and found 38 numeric columns
22:02:10 Using Annoy for neighbor search, n_neighbors = 158
22:02:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:02:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872edecd4b
22:02:10 Searching Annoy index using 1 thread, search_k = 15800
22:02:11 Annoy recall = 100%
22:02:22 Commencing smooth kNN distance calibration using 1 thread
22:02:44 Initializing from normalized Laplacian + noise
22:02:44 Commencing optimization for 500 epochs, with 212082 positive edges
22:02:59 Optimization finished
[1] "158 0.11"22:02:59 UMAP embedding parameters a = 1.544 b = 0.9058
22:02:59 Read 1203 rows and found 38 numeric columns
22:02:59 Using Annoy for neighbor search, n_neighbors = 158
22:02:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732f180f0
22:03:00 Searching Annoy index using 1 thread, search_k = 15800
22:03:01 Annoy recall = 100%
22:03:12 Commencing smooth kNN distance calibration using 1 thread
22:03:33 Initializing from normalized Laplacian + noise
22:03:33 Commencing optimization for 500 epochs, with 212082 positive edges
22:03:48 Optimization finished
[1] "158 0.12"22:03:49 UMAP embedding parameters a = 1.51 b = 0.9165
22:03:49 Read 1203 rows and found 38 numeric columns
22:03:49 Using Annoy for neighbor search, n_neighbors = 158
22:03:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:03:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87241f993d
22:03:49 Searching Annoy index using 1 thread, search_k = 15800
22:03:50 Annoy recall = 100%
22:04:01 Commencing smooth kNN distance calibration using 1 thread
22:04:23 Initializing from normalized Laplacian + noise
22:04:23 Commencing optimization for 500 epochs, with 212082 positive edges
22:04:38 Optimization finished
[1] "158 0.13"22:04:38 UMAP embedding parameters a = 1.478 b = 0.9272
22:04:38 Read 1203 rows and found 38 numeric columns
22:04:38 Using Annoy for neighbor search, n_neighbors = 158
22:04:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:04:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876143e3ed
22:04:39 Searching Annoy index using 1 thread, search_k = 15800
22:04:40 Annoy recall = 100%
22:04:51 Commencing smooth kNN distance calibration using 1 thread
22:05:12 Initializing from normalized Laplacian + noise
22:05:12 Commencing optimization for 500 epochs, with 212082 positive edges
22:05:28 Optimization finished
[1] "158 0.14"22:05:28 UMAP embedding parameters a = 1.446 b = 0.938
22:05:28 Read 1203 rows and found 38 numeric columns
22:05:28 Using Annoy for neighbor search, n_neighbors = 158
22:05:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:05:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dab582d
22:05:29 Searching Annoy index using 1 thread, search_k = 15800
22:05:30 Annoy recall = 100%
22:05:41 Commencing smooth kNN distance calibration using 1 thread
22:06:02 Initializing from normalized Laplacian + noise
22:06:02 Commencing optimization for 500 epochs, with 212082 positive edges
22:06:17 Optimization finished
[1] "158 0.15"22:06:17 UMAP embedding parameters a = 1.414 b = 0.9488
22:06:17 Read 1203 rows and found 38 numeric columns
22:06:17 Using Annoy for neighbor search, n_neighbors = 158
22:06:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:06:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772e6741
22:06:18 Searching Annoy index using 1 thread, search_k = 15800
22:06:19 Annoy recall = 100%
22:06:30 Commencing smooth kNN distance calibration using 1 thread
22:06:52 Initializing from normalized Laplacian + noise
22:06:52 Commencing optimization for 500 epochs, with 212082 positive edges
22:07:07 Optimization finished
[1] "158 0.16"22:07:07 UMAP embedding parameters a = 1.383 b = 0.9596
22:07:07 Read 1203 rows and found 38 numeric columns
22:07:07 Using Annoy for neighbor search, n_neighbors = 158
22:07:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87746e9f7d
22:07:08 Searching Annoy index using 1 thread, search_k = 15800
22:07:09 Annoy recall = 100%
22:07:20 Commencing smooth kNN distance calibration using 1 thread
22:07:41 Initializing from normalized Laplacian + noise
22:07:42 Commencing optimization for 500 epochs, with 212082 positive edges
22:07:56 Optimization finished
[1] "158 0.17"22:07:57 UMAP embedding parameters a = 1.352 b = 0.9704
22:07:57 Read 1203 rows and found 38 numeric columns
22:07:57 Using Annoy for neighbor search, n_neighbors = 158
22:07:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:07:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770d0f7c4
22:07:58 Searching Annoy index using 1 thread, search_k = 15800
22:07:59 Annoy recall = 100%
22:08:09 Commencing smooth kNN distance calibration using 1 thread
22:08:31 Initializing from normalized Laplacian + noise
22:08:31 Commencing optimization for 500 epochs, with 212082 positive edges
22:08:46 Optimization finished
[1] "158 0.18"22:08:46 UMAP embedding parameters a = 1.321 b = 0.9813
22:08:47 Read 1203 rows and found 38 numeric columns
22:08:47 Using Annoy for neighbor search, n_neighbors = 158
22:08:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:08:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877277a4e6
22:08:47 Searching Annoy index using 1 thread, search_k = 15800
22:08:48 Annoy recall = 100%
22:08:59 Commencing smooth kNN distance calibration using 1 thread
22:09:21 Initializing from normalized Laplacian + noise
22:09:21 Commencing optimization for 500 epochs, with 212082 positive edges
22:09:36 Optimization finished
[1] "158 0.19"22:09:36 UMAP embedding parameters a = 1.292 b = 0.9921
22:09:36 Read 1203 rows and found 38 numeric columns
22:09:36 Using Annoy for neighbor search, n_neighbors = 158
22:09:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:09:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725840e26
22:09:37 Searching Annoy index using 1 thread, search_k = 15800
22:09:38 Annoy recall = 100%
22:09:49 Commencing smooth kNN distance calibration using 1 thread
22:10:10 Initializing from normalized Laplacian + noise
22:10:10 Commencing optimization for 500 epochs, with 212082 positive edges
22:10:25 Optimization finished
[1] "158 0.2"22:10:26 UMAP embedding parameters a = 1.262 b = 1.003
22:10:26 Read 1203 rows and found 38 numeric columns
22:10:26 Using Annoy for neighbor search, n_neighbors = 158
22:10:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:10:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aa6018d
22:10:26 Searching Annoy index using 1 thread, search_k = 15800
22:10:28 Annoy recall = 100%
22:10:38 Commencing smooth kNN distance calibration using 1 thread
22:11:00 Initializing from normalized Laplacian + noise
22:11:00 Commencing optimization for 500 epochs, with 212082 positive edges
22:11:15 Optimization finished
[1] "159 0"22:11:15 UMAP embedding parameters a = 1.933 b = 0.7905
22:11:15 Read 1203 rows and found 38 numeric columns
22:11:15 Using Annoy for neighbor search, n_neighbors = 159
22:11:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:11:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f9a9865
22:11:16 Searching Annoy index using 1 thread, search_k = 15900
22:11:17 Annoy recall = 100%
22:11:28 Commencing smooth kNN distance calibration using 1 thread
22:11:50 Initializing from normalized Laplacian + noise
22:11:50 Commencing optimization for 500 epochs, with 213294 positive edges
22:12:05 Optimization finished
[1] "159 0.01"22:12:05 UMAP embedding parameters a = 1.896 b = 0.8006
22:12:05 Read 1203 rows and found 38 numeric columns
22:12:05 Using Annoy for neighbor search, n_neighbors = 159
22:12:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87316da8e4
22:12:06 Searching Annoy index using 1 thread, search_k = 15900
22:12:07 Annoy recall = 100%
22:12:18 Commencing smooth kNN distance calibration using 1 thread
22:12:39 Initializing from normalized Laplacian + noise
22:12:40 Commencing optimization for 500 epochs, with 213294 positive edges
22:12:55 Optimization finished
[1] "159 0.02"22:12:55 UMAP embedding parameters a = 1.859 b = 0.8109
22:12:55 Read 1203 rows and found 38 numeric columns
22:12:55 Using Annoy for neighbor search, n_neighbors = 159
22:12:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:12:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769a5a117
22:12:56 Searching Annoy index using 1 thread, search_k = 15900
22:12:57 Annoy recall = 100%
22:13:08 Commencing smooth kNN distance calibration using 1 thread
22:13:29 Initializing from normalized Laplacian + noise
22:13:29 Commencing optimization for 500 epochs, with 213294 positive edges
22:13:44 Optimization finished
[1] "159 0.03"22:13:45 UMAP embedding parameters a = 1.822 b = 0.8212
22:13:45 Read 1203 rows and found 38 numeric columns
22:13:45 Using Annoy for neighbor search, n_neighbors = 159
22:13:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:13:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752217847
22:13:45 Searching Annoy index using 1 thread, search_k = 15900
22:13:46 Annoy recall = 100%
22:13:57 Commencing smooth kNN distance calibration using 1 thread
22:14:19 Initializing from normalized Laplacian + noise
22:14:19 Commencing optimization for 500 epochs, with 213294 positive edges
22:14:34 Optimization finished
[1] "159 0.04"22:14:34 UMAP embedding parameters a = 1.786 b = 0.8316
22:14:34 Read 1203 rows and found 38 numeric columns
22:14:34 Using Annoy for neighbor search, n_neighbors = 159
22:14:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:14:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f5a82dd
22:14:35 Searching Annoy index using 1 thread, search_k = 15900
22:14:36 Annoy recall = 100%
22:14:47 Commencing smooth kNN distance calibration using 1 thread
22:15:09 Initializing from normalized Laplacian + noise
22:15:09 Commencing optimization for 500 epochs, with 213294 positive edges
22:15:24 Optimization finished
[1] "159 0.05"22:15:24 UMAP embedding parameters a = 1.75 b = 0.8421
22:15:24 Read 1203 rows and found 38 numeric columns
22:15:24 Using Annoy for neighbor search, n_neighbors = 159
22:15:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:15:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e672762
22:15:25 Searching Annoy index using 1 thread, search_k = 15900
22:15:26 Annoy recall = 100%
22:15:37 Commencing smooth kNN distance calibration using 1 thread
22:15:59 Initializing from normalized Laplacian + noise
22:15:59 Commencing optimization for 500 epochs, with 213294 positive edges
22:16:14 Optimization finished
[1] "159 0.06"22:16:14 UMAP embedding parameters a = 1.715 b = 0.8526
22:16:14 Read 1203 rows and found 38 numeric columns
22:16:14 Using Annoy for neighbor search, n_neighbors = 159
22:16:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:16:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718c80bdb
22:16:15 Searching Annoy index using 1 thread, search_k = 15900
22:16:16 Annoy recall = 100%
22:16:27 Commencing smooth kNN distance calibration using 1 thread
22:16:48 Initializing from normalized Laplacian + noise
22:16:48 Commencing optimization for 500 epochs, with 213294 positive edges
22:17:04 Optimization finished
[1] "159 0.07"22:17:04 UMAP embedding parameters a = 1.68 b = 0.8631
22:17:04 Read 1203 rows and found 38 numeric columns
22:17:04 Using Annoy for neighbor search, n_neighbors = 159
22:17:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ecff172
22:17:05 Searching Annoy index using 1 thread, search_k = 15900
22:17:06 Annoy recall = 100%
22:17:17 Commencing smooth kNN distance calibration using 1 thread
22:17:39 Initializing from normalized Laplacian + noise
22:17:39 Commencing optimization for 500 epochs, with 213294 positive edges
22:17:55 Optimization finished
[1] "159 0.08"22:17:55 UMAP embedding parameters a = 1.645 b = 0.8737
22:17:55 Read 1203 rows and found 38 numeric columns
22:17:55 Using Annoy for neighbor search, n_neighbors = 159
22:17:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:17:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728a12756
22:17:56 Searching Annoy index using 1 thread, search_k = 15900
22:17:57 Annoy recall = 100%
22:18:08 Commencing smooth kNN distance calibration using 1 thread
22:18:31 Initializing from normalized Laplacian + noise
22:18:31 Commencing optimization for 500 epochs, with 213294 positive edges
22:18:46 Optimization finished
[1] "159 0.09"22:18:46 UMAP embedding parameters a = 1.611 b = 0.8844
22:18:46 Read 1203 rows and found 38 numeric columns
22:18:46 Using Annoy for neighbor search, n_neighbors = 159
22:18:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:18:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bf19e22
22:18:47 Searching Annoy index using 1 thread, search_k = 15900
22:18:48 Annoy recall = 100%
22:18:59 Commencing smooth kNN distance calibration using 1 thread
22:19:21 Initializing from normalized Laplacian + noise
22:19:22 Commencing optimization for 500 epochs, with 213294 positive edges
22:19:37 Optimization finished
[1] "159 0.1"22:19:37 UMAP embedding parameters a = 1.577 b = 0.8951
22:19:37 Read 1203 rows and found 38 numeric columns
22:19:37 Using Annoy for neighbor search, n_neighbors = 159
22:19:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:19:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753876061
22:19:38 Searching Annoy index using 1 thread, search_k = 15900
22:19:39 Annoy recall = 100%
22:19:50 Commencing smooth kNN distance calibration using 1 thread
22:20:12 Initializing from normalized Laplacian + noise
22:20:13 Commencing optimization for 500 epochs, with 213294 positive edges
22:20:28 Optimization finished
[1] "159 0.11"22:20:28 UMAP embedding parameters a = 1.544 b = 0.9058
22:20:28 Read 1203 rows and found 38 numeric columns
22:20:28 Using Annoy for neighbor search, n_neighbors = 159
22:20:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:20:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714719296
22:20:29 Searching Annoy index using 1 thread, search_k = 15900
22:20:30 Annoy recall = 100%
22:20:41 Commencing smooth kNN distance calibration using 1 thread
22:21:03 Initializing from normalized Laplacian + noise
22:21:03 Commencing optimization for 500 epochs, with 213294 positive edges
22:21:19 Optimization finished
[1] "159 0.12"22:21:19 UMAP embedding parameters a = 1.51 b = 0.9165
22:21:19 Read 1203 rows and found 38 numeric columns
22:21:19 Using Annoy for neighbor search, n_neighbors = 159
22:21:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:21:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872336a4ec
22:21:20 Searching Annoy index using 1 thread, search_k = 15900
22:21:21 Annoy recall = 100%
22:21:32 Commencing smooth kNN distance calibration using 1 thread
22:21:54 Initializing from normalized Laplacian + noise
22:21:55 Commencing optimization for 500 epochs, with 213294 positive edges
22:22:10 Optimization finished
[1] "159 0.13"22:22:10 UMAP embedding parameters a = 1.478 b = 0.9272
22:22:10 Read 1203 rows and found 38 numeric columns
22:22:10 Using Annoy for neighbor search, n_neighbors = 159
22:22:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:22:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d2dfca1
22:22:11 Searching Annoy index using 1 thread, search_k = 15900
22:22:12 Annoy recall = 100%
22:22:23 Commencing smooth kNN distance calibration using 1 thread
22:22:46 Initializing from normalized Laplacian + noise
22:22:46 Commencing optimization for 500 epochs, with 213294 positive edges
22:23:01 Optimization finished
[1] "159 0.14"22:23:01 UMAP embedding parameters a = 1.446 b = 0.938
22:23:01 Read 1203 rows and found 38 numeric columns
22:23:01 Using Annoy for neighbor search, n_neighbors = 159
22:23:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727a33fff
22:23:02 Searching Annoy index using 1 thread, search_k = 15900
22:23:03 Annoy recall = 100%
22:23:14 Commencing smooth kNN distance calibration using 1 thread
22:23:36 Initializing from normalized Laplacian + noise
22:23:37 Commencing optimization for 500 epochs, with 213294 positive edges
22:23:52 Optimization finished
[1] "159 0.15"22:23:52 UMAP embedding parameters a = 1.414 b = 0.9488
22:23:52 Read 1203 rows and found 38 numeric columns
22:23:52 Using Annoy for neighbor search, n_neighbors = 159
22:23:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:23:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87354fc794
22:23:53 Searching Annoy index using 1 thread, search_k = 15900
22:23:54 Annoy recall = 100%
22:24:05 Commencing smooth kNN distance calibration using 1 thread
22:24:27 Initializing from normalized Laplacian + noise
22:24:27 Commencing optimization for 500 epochs, with 213294 positive edges
22:24:43 Optimization finished
[1] "159 0.16"22:24:43 UMAP embedding parameters a = 1.383 b = 0.9596
22:24:43 Read 1203 rows and found 38 numeric columns
22:24:43 Using Annoy for neighbor search, n_neighbors = 159
22:24:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:24:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765e60cb4
22:24:44 Searching Annoy index using 1 thread, search_k = 15900
22:24:45 Annoy recall = 100%
22:24:56 Commencing smooth kNN distance calibration using 1 thread
22:25:18 Initializing from normalized Laplacian + noise
22:25:18 Commencing optimization for 500 epochs, with 213294 positive edges
22:25:33 Optimization finished
[1] "159 0.17"22:25:34 UMAP embedding parameters a = 1.352 b = 0.9704
22:25:34 Read 1203 rows and found 38 numeric columns
22:25:34 Using Annoy for neighbor search, n_neighbors = 159
22:25:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:25:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750f84bd0
22:25:34 Searching Annoy index using 1 thread, search_k = 15900
22:25:36 Annoy recall = 100%
22:25:47 Commencing smooth kNN distance calibration using 1 thread
22:26:09 Initializing from normalized Laplacian + noise
22:26:09 Commencing optimization for 500 epochs, with 213294 positive edges
22:26:24 Optimization finished
[1] "159 0.18"22:26:24 UMAP embedding parameters a = 1.321 b = 0.9813
22:26:25 Read 1203 rows and found 38 numeric columns
22:26:25 Using Annoy for neighbor search, n_neighbors = 159
22:26:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:26:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877147a898
22:26:25 Searching Annoy index using 1 thread, search_k = 15900
22:26:26 Annoy recall = 100%
22:26:38 Commencing smooth kNN distance calibration using 1 thread
22:27:00 Initializing from normalized Laplacian + noise
22:27:00 Commencing optimization for 500 epochs, with 213294 positive edges
22:27:15 Optimization finished
[1] "159 0.19"22:27:15 UMAP embedding parameters a = 1.292 b = 0.9921
22:27:15 Read 1203 rows and found 38 numeric columns
22:27:15 Using Annoy for neighbor search, n_neighbors = 159
22:27:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:27:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87285c291a
22:27:16 Searching Annoy index using 1 thread, search_k = 15900
22:27:17 Annoy recall = 100%
22:27:28 Commencing smooth kNN distance calibration using 1 thread
22:27:51 Initializing from normalized Laplacian + noise
22:27:51 Commencing optimization for 500 epochs, with 213294 positive edges
22:28:06 Optimization finished
[1] "159 0.2"22:28:06 UMAP embedding parameters a = 1.262 b = 1.003
22:28:06 Read 1203 rows and found 38 numeric columns
22:28:06 Using Annoy for neighbor search, n_neighbors = 159
22:28:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:28:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fd7191b
22:28:07 Searching Annoy index using 1 thread, search_k = 15900
22:28:08 Annoy recall = 100%
22:28:19 Commencing smooth kNN distance calibration using 1 thread
22:28:41 Initializing from normalized Laplacian + noise
22:28:42 Commencing optimization for 500 epochs, with 213294 positive edges
22:28:57 Optimization finished
[1] "160 0"22:28:57 UMAP embedding parameters a = 1.933 b = 0.7905
22:28:57 Read 1203 rows and found 38 numeric columns
22:28:57 Using Annoy for neighbor search, n_neighbors = 160
22:28:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:28:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724392988
22:28:58 Searching Annoy index using 1 thread, search_k = 16000
22:28:59 Annoy recall = 100%
22:29:10 Commencing smooth kNN distance calibration using 1 thread
22:29:32 Initializing from normalized Laplacian + noise
22:29:32 Commencing optimization for 500 epochs, with 214498 positive edges
22:29:48 Optimization finished
[1] "160 0.01"22:29:48 UMAP embedding parameters a = 1.896 b = 0.8006
22:29:48 Read 1203 rows and found 38 numeric columns
22:29:48 Using Annoy for neighbor search, n_neighbors = 160
22:29:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:29:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c7bc257
22:29:49 Searching Annoy index using 1 thread, search_k = 16000
22:29:50 Annoy recall = 100%
22:30:01 Commencing smooth kNN distance calibration using 1 thread
22:30:23 Initializing from normalized Laplacian + noise
22:30:23 Commencing optimization for 500 epochs, with 214498 positive edges
22:30:39 Optimization finished
[1] "160 0.02"22:30:39 UMAP embedding parameters a = 1.859 b = 0.8109
22:30:39 Read 1203 rows and found 38 numeric columns
22:30:39 Using Annoy for neighbor search, n_neighbors = 160
22:30:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:30:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87611afd08
22:30:40 Searching Annoy index using 1 thread, search_k = 16000
22:30:41 Annoy recall = 100%
22:30:52 Commencing smooth kNN distance calibration using 1 thread
22:31:14 Initializing from normalized Laplacian + noise
22:31:15 Commencing optimization for 500 epochs, with 214498 positive edges
22:31:30 Optimization finished
[1] "160 0.03"22:31:30 UMAP embedding parameters a = 1.822 b = 0.8212
22:31:30 Read 1203 rows and found 38 numeric columns
22:31:30 Using Annoy for neighbor search, n_neighbors = 160
22:31:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:31:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751e481b5
22:31:31 Searching Annoy index using 1 thread, search_k = 16000
22:31:32 Annoy recall = 100%
22:31:43 Commencing smooth kNN distance calibration using 1 thread
22:32:05 Initializing from normalized Laplacian + noise
22:32:06 Commencing optimization for 500 epochs, with 214498 positive edges
22:32:21 Optimization finished
[1] "160 0.04"22:32:21 UMAP embedding parameters a = 1.786 b = 0.8316
22:32:21 Read 1203 rows and found 38 numeric columns
22:32:21 Using Annoy for neighbor search, n_neighbors = 160
22:32:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:32:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753aa2999
22:32:22 Searching Annoy index using 1 thread, search_k = 16000
22:32:23 Annoy recall = 100%
22:32:34 Commencing smooth kNN distance calibration using 1 thread
22:32:57 Initializing from normalized Laplacian + noise
22:32:57 Commencing optimization for 500 epochs, with 214498 positive edges
22:33:12 Optimization finished
[1] "160 0.05"22:33:12 UMAP embedding parameters a = 1.75 b = 0.8421
22:33:12 Read 1203 rows and found 38 numeric columns
22:33:12 Using Annoy for neighbor search, n_neighbors = 160
22:33:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:33:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755899c85
22:33:13 Searching Annoy index using 1 thread, search_k = 16000
22:33:14 Annoy recall = 100%
22:33:25 Commencing smooth kNN distance calibration using 1 thread
22:33:47 Initializing from normalized Laplacian + noise
22:33:48 Commencing optimization for 500 epochs, with 214498 positive edges
22:34:03 Optimization finished
[1] "160 0.06"22:34:03 UMAP embedding parameters a = 1.715 b = 0.8526
22:34:03 Read 1203 rows and found 38 numeric columns
22:34:03 Using Annoy for neighbor search, n_neighbors = 160
22:34:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:34:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742b57979
22:34:04 Searching Annoy index using 1 thread, search_k = 16000
22:34:05 Annoy recall = 100%
22:34:16 Commencing smooth kNN distance calibration using 1 thread
22:34:38 Initializing from normalized Laplacian + noise
22:34:39 Commencing optimization for 500 epochs, with 214498 positive edges
22:34:54 Optimization finished
[1] "160 0.07"22:34:54 UMAP embedding parameters a = 1.68 b = 0.8631
22:34:54 Read 1203 rows and found 38 numeric columns
22:34:54 Using Annoy for neighbor search, n_neighbors = 160
22:34:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:34:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874621ce7f
22:34:55 Searching Annoy index using 1 thread, search_k = 16000
22:34:56 Annoy recall = 100%
22:35:07 Commencing smooth kNN distance calibration using 1 thread
22:35:30 Initializing from normalized Laplacian + noise
22:35:30 Commencing optimization for 500 epochs, with 214498 positive edges
22:35:45 Optimization finished
[1] "160 0.08"22:35:45 UMAP embedding parameters a = 1.645 b = 0.8737
22:35:45 Read 1203 rows and found 38 numeric columns
22:35:45 Using Annoy for neighbor search, n_neighbors = 160
22:35:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:35:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b0daaac
22:35:46 Searching Annoy index using 1 thread, search_k = 16000
22:35:47 Annoy recall = 100%
22:35:58 Commencing smooth kNN distance calibration using 1 thread
22:36:21 Initializing from normalized Laplacian + noise
22:36:21 Commencing optimization for 500 epochs, with 214498 positive edges
22:36:36 Optimization finished
[1] "160 0.09"22:36:36 UMAP embedding parameters a = 1.611 b = 0.8844
22:36:36 Read 1203 rows and found 38 numeric columns
22:36:36 Using Annoy for neighbor search, n_neighbors = 160
22:36:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:36:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d5b7b06
22:36:37 Searching Annoy index using 1 thread, search_k = 16000
22:36:38 Annoy recall = 100%
22:36:49 Commencing smooth kNN distance calibration using 1 thread
22:37:12 Initializing from normalized Laplacian + noise
22:37:12 Commencing optimization for 500 epochs, with 214498 positive edges
22:37:27 Optimization finished
[1] "160 0.1"22:37:27 UMAP embedding parameters a = 1.577 b = 0.8951
22:37:27 Read 1203 rows and found 38 numeric columns
22:37:27 Using Annoy for neighbor search, n_neighbors = 160
22:37:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:37:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755bc66e4
22:37:28 Searching Annoy index using 1 thread, search_k = 16000
22:37:29 Annoy recall = 100%
22:37:40 Commencing smooth kNN distance calibration using 1 thread
22:38:03 Initializing from normalized Laplacian + noise
22:38:03 Commencing optimization for 500 epochs, with 214498 positive edges
22:38:18 Optimization finished
[1] "160 0.11"22:38:18 UMAP embedding parameters a = 1.544 b = 0.9058
22:38:18 Read 1203 rows and found 38 numeric columns
22:38:18 Using Annoy for neighbor search, n_neighbors = 160
22:38:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:38:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c7b5390
22:38:19 Searching Annoy index using 1 thread, search_k = 16000
22:38:20 Annoy recall = 100%
22:38:31 Commencing smooth kNN distance calibration using 1 thread
22:38:54 Initializing from normalized Laplacian + noise
22:38:54 Commencing optimization for 500 epochs, with 214498 positive edges
22:39:09 Optimization finished
[1] "160 0.12"22:39:09 UMAP embedding parameters a = 1.51 b = 0.9165
22:39:09 Read 1203 rows and found 38 numeric columns
22:39:09 Using Annoy for neighbor search, n_neighbors = 160
22:39:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:39:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717011c1e
22:39:10 Searching Annoy index using 1 thread, search_k = 16000
22:39:11 Annoy recall = 100%
22:39:23 Commencing smooth kNN distance calibration using 1 thread
22:39:45 Initializing from normalized Laplacian + noise
22:39:45 Commencing optimization for 500 epochs, with 214498 positive edges
22:40:00 Optimization finished
[1] "160 0.13"22:40:00 UMAP embedding parameters a = 1.478 b = 0.9272
22:40:00 Read 1203 rows and found 38 numeric columns
22:40:01 Using Annoy for neighbor search, n_neighbors = 160
22:40:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:40:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727dddf2b
22:40:01 Searching Annoy index using 1 thread, search_k = 16000
22:40:02 Annoy recall = 100%
22:40:14 Commencing smooth kNN distance calibration using 1 thread
22:40:36 Initializing from normalized Laplacian + noise
22:40:36 Commencing optimization for 500 epochs, with 214498 positive edges
22:40:51 Optimization finished
[1] "160 0.14"22:40:52 UMAP embedding parameters a = 1.446 b = 0.938
22:40:52 Read 1203 rows and found 38 numeric columns
22:40:52 Using Annoy for neighbor search, n_neighbors = 160
22:40:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:40:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bd5d66e
22:40:53 Searching Annoy index using 1 thread, search_k = 16000
22:40:54 Annoy recall = 100%
22:41:05 Commencing smooth kNN distance calibration using 1 thread
22:41:27 Initializing from normalized Laplacian + noise
22:41:27 Commencing optimization for 500 epochs, with 214498 positive edges
22:41:43 Optimization finished
[1] "160 0.15"22:41:43 UMAP embedding parameters a = 1.414 b = 0.9488
22:41:43 Read 1203 rows and found 38 numeric columns
22:41:43 Using Annoy for neighbor search, n_neighbors = 160
22:41:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:41:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755684380
22:41:44 Searching Annoy index using 1 thread, search_k = 16000
22:41:45 Annoy recall = 100%
22:41:56 Commencing smooth kNN distance calibration using 1 thread
22:42:18 Initializing from normalized Laplacian + noise
22:42:18 Commencing optimization for 500 epochs, with 214498 positive edges
22:42:34 Optimization finished
[1] "160 0.16"22:42:34 UMAP embedding parameters a = 1.383 b = 0.9596
22:42:34 Read 1203 rows and found 38 numeric columns
22:42:34 Using Annoy for neighbor search, n_neighbors = 160
22:42:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:42:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740a5eb06
22:42:35 Searching Annoy index using 1 thread, search_k = 16000
22:42:36 Annoy recall = 100%
22:42:47 Commencing smooth kNN distance calibration using 1 thread
22:43:09 Initializing from normalized Laplacian + noise
22:43:09 Commencing optimization for 500 epochs, with 214498 positive edges
22:43:25 Optimization finished
[1] "160 0.17"22:43:25 UMAP embedding parameters a = 1.352 b = 0.9704
22:43:25 Read 1203 rows and found 38 numeric columns
22:43:25 Using Annoy for neighbor search, n_neighbors = 160
22:43:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:43:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876aa5c7e0
22:43:26 Searching Annoy index using 1 thread, search_k = 16000
22:43:27 Annoy recall = 100%
22:43:38 Commencing smooth kNN distance calibration using 1 thread
22:44:01 Initializing from normalized Laplacian + noise
22:44:01 Commencing optimization for 500 epochs, with 214498 positive edges
22:44:16 Optimization finished
[1] "160 0.18"22:44:16 UMAP embedding parameters a = 1.321 b = 0.9813
22:44:16 Read 1203 rows and found 38 numeric columns
22:44:16 Using Annoy for neighbor search, n_neighbors = 160
22:44:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:44:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e096ad7
22:44:17 Searching Annoy index using 1 thread, search_k = 16000
22:44:18 Annoy recall = 100%
22:44:29 Commencing smooth kNN distance calibration using 1 thread
22:44:52 Initializing from normalized Laplacian + noise
22:44:52 Commencing optimization for 500 epochs, with 214498 positive edges
22:45:07 Optimization finished
[1] "160 0.19"22:45:07 UMAP embedding parameters a = 1.292 b = 0.9921
22:45:07 Read 1203 rows and found 38 numeric columns
22:45:07 Using Annoy for neighbor search, n_neighbors = 160
22:45:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:45:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c978928
22:45:08 Searching Annoy index using 1 thread, search_k = 16000
22:45:09 Annoy recall = 100%
22:45:20 Commencing smooth kNN distance calibration using 1 thread
22:45:43 Initializing from normalized Laplacian + noise
22:45:43 Commencing optimization for 500 epochs, with 214498 positive edges
22:45:58 Optimization finished
[1] "160 0.2"22:45:59 UMAP embedding parameters a = 1.262 b = 1.003
22:45:59 Read 1203 rows and found 38 numeric columns
22:45:59 Using Annoy for neighbor search, n_neighbors = 160
22:45:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:45:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e2d2841
22:46:00 Searching Annoy index using 1 thread, search_k = 16000
22:46:01 Annoy recall = 100%
22:46:12 Commencing smooth kNN distance calibration using 1 thread
22:46:34 Initializing from normalized Laplacian + noise
22:46:34 Commencing optimization for 500 epochs, with 214498 positive edges
22:46:50 Optimization finished
[1] "161 0"22:46:50 UMAP embedding parameters a = 1.933 b = 0.7905
22:46:50 Read 1203 rows and found 38 numeric columns
22:46:50 Using Annoy for neighbor search, n_neighbors = 161
22:46:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:46:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87127afd6d
22:46:51 Searching Annoy index using 1 thread, search_k = 16100
22:46:52 Annoy recall = 100%
22:47:03 Commencing smooth kNN distance calibration using 1 thread
22:47:25 Initializing from normalized Laplacian + noise
22:47:25 Commencing optimization for 500 epochs, with 215696 positive edges
22:47:41 Optimization finished
[1] "161 0.01"22:47:41 UMAP embedding parameters a = 1.896 b = 0.8006
22:47:41 Read 1203 rows and found 38 numeric columns
22:47:41 Using Annoy for neighbor search, n_neighbors = 161
22:47:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:47:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fce2e14
22:47:42 Searching Annoy index using 1 thread, search_k = 16100
22:47:43 Annoy recall = 100%
22:47:54 Commencing smooth kNN distance calibration using 1 thread
22:48:17 Initializing from normalized Laplacian + noise
22:48:17 Commencing optimization for 500 epochs, with 215696 positive edges
22:48:32 Optimization finished
[1] "161 0.02"22:48:32 UMAP embedding parameters a = 1.859 b = 0.8109
22:48:32 Read 1203 rows and found 38 numeric columns
22:48:32 Using Annoy for neighbor search, n_neighbors = 161
22:48:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:48:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b5b24e2
22:48:33 Searching Annoy index using 1 thread, search_k = 16100
22:48:34 Annoy recall = 100%
22:48:45 Commencing smooth kNN distance calibration using 1 thread
22:49:08 Initializing from normalized Laplacian + noise
22:49:08 Commencing optimization for 500 epochs, with 215696 positive edges
22:49:23 Optimization finished
[1] "161 0.03"22:49:24 UMAP embedding parameters a = 1.822 b = 0.8212
22:49:24 Read 1203 rows and found 38 numeric columns
22:49:24 Using Annoy for neighbor search, n_neighbors = 161
22:49:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:49:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a1e3d6c
22:49:24 Searching Annoy index using 1 thread, search_k = 16100
22:49:25 Annoy recall = 100%
22:49:36 Commencing smooth kNN distance calibration using 1 thread
22:49:59 Initializing from normalized Laplacian + noise
22:49:59 Commencing optimization for 500 epochs, with 215696 positive edges
22:50:15 Optimization finished
[1] "161 0.04"22:50:15 UMAP embedding parameters a = 1.786 b = 0.8316
22:50:15 Read 1203 rows and found 38 numeric columns
22:50:15 Using Annoy for neighbor search, n_neighbors = 161
22:50:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:50:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87651df5a8
22:50:16 Searching Annoy index using 1 thread, search_k = 16100
22:50:17 Annoy recall = 100%
22:50:28 Commencing smooth kNN distance calibration using 1 thread
22:50:50 Initializing from normalized Laplacian + noise
22:50:50 Commencing optimization for 500 epochs, with 215696 positive edges
22:51:06 Optimization finished
[1] "161 0.05"22:51:06 UMAP embedding parameters a = 1.75 b = 0.8421
22:51:06 Read 1203 rows and found 38 numeric columns
22:51:06 Using Annoy for neighbor search, n_neighbors = 161
22:51:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:51:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721413196
22:51:07 Searching Annoy index using 1 thread, search_k = 16100
22:51:08 Annoy recall = 100%
22:51:19 Commencing smooth kNN distance calibration using 1 thread
22:51:41 Initializing from normalized Laplacian + noise
22:51:42 Commencing optimization for 500 epochs, with 215696 positive edges
22:51:57 Optimization finished
[1] "161 0.06"22:51:57 UMAP embedding parameters a = 1.715 b = 0.8526
22:51:57 Read 1203 rows and found 38 numeric columns
22:51:57 Using Annoy for neighbor search, n_neighbors = 161
22:51:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:51:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b16893c
22:51:58 Searching Annoy index using 1 thread, search_k = 16100
22:51:59 Annoy recall = 100%
22:52:10 Commencing smooth kNN distance calibration using 1 thread
22:52:33 Initializing from normalized Laplacian + noise
22:52:33 Commencing optimization for 500 epochs, with 215696 positive edges
22:52:48 Optimization finished
[1] "161 0.07"22:52:49 UMAP embedding parameters a = 1.68 b = 0.8631
22:52:49 Read 1203 rows and found 38 numeric columns
22:52:49 Using Annoy for neighbor search, n_neighbors = 161
22:52:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:52:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756659e40
22:52:49 Searching Annoy index using 1 thread, search_k = 16100
22:52:51 Annoy recall = 100%
22:53:02 Commencing smooth kNN distance calibration using 1 thread
22:53:24 Initializing from normalized Laplacian + noise
22:53:24 Commencing optimization for 500 epochs, with 215696 positive edges
22:53:40 Optimization finished
[1] "161 0.08"22:53:40 UMAP embedding parameters a = 1.645 b = 0.8737
22:53:40 Read 1203 rows and found 38 numeric columns
22:53:40 Using Annoy for neighbor search, n_neighbors = 161
22:53:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:53:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87499d5ab0
22:53:41 Searching Annoy index using 1 thread, search_k = 16100
22:53:42 Annoy recall = 100%
22:53:53 Commencing smooth kNN distance calibration using 1 thread
22:54:16 Initializing from normalized Laplacian + noise
22:54:16 Commencing optimization for 500 epochs, with 215696 positive edges
22:54:31 Optimization finished
[1] "161 0.09"22:54:32 UMAP embedding parameters a = 1.611 b = 0.8844
22:54:32 Read 1203 rows and found 38 numeric columns
22:54:32 Using Annoy for neighbor search, n_neighbors = 161
22:54:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:54:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87aeda257
22:54:32 Searching Annoy index using 1 thread, search_k = 16100
22:54:34 Annoy recall = 100%
22:54:45 Commencing smooth kNN distance calibration using 1 thread
22:55:07 Initializing from normalized Laplacian + noise
22:55:07 Commencing optimization for 500 epochs, with 215696 positive edges
22:55:23 Optimization finished
[1] "161 0.1"22:55:23 UMAP embedding parameters a = 1.577 b = 0.8951
22:55:23 Read 1203 rows and found 38 numeric columns
22:55:23 Using Annoy for neighbor search, n_neighbors = 161
22:55:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:55:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a9ec7c8
22:55:24 Searching Annoy index using 1 thread, search_k = 16100
22:55:25 Annoy recall = 100%
22:55:36 Commencing smooth kNN distance calibration using 1 thread
22:55:58 Initializing from normalized Laplacian + noise
22:55:59 Commencing optimization for 500 epochs, with 215696 positive edges
22:56:14 Optimization finished
[1] "161 0.11"22:56:14 UMAP embedding parameters a = 1.544 b = 0.9058
22:56:14 Read 1203 rows and found 38 numeric columns
22:56:14 Using Annoy for neighbor search, n_neighbors = 161
22:56:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:56:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716191d07
22:56:15 Searching Annoy index using 1 thread, search_k = 16100
22:56:16 Annoy recall = 100%
22:56:27 Commencing smooth kNN distance calibration using 1 thread
22:56:50 Initializing from normalized Laplacian + noise
22:56:50 Commencing optimization for 500 epochs, with 215696 positive edges
22:57:05 Optimization finished
[1] "161 0.12"22:57:06 UMAP embedding parameters a = 1.51 b = 0.9165
22:57:06 Read 1203 rows and found 38 numeric columns
22:57:06 Using Annoy for neighbor search, n_neighbors = 161
22:57:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:57:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c089f60
22:57:06 Searching Annoy index using 1 thread, search_k = 16100
22:57:08 Annoy recall = 100%
22:57:19 Commencing smooth kNN distance calibration using 1 thread
22:57:41 Initializing from normalized Laplacian + noise
22:57:41 Commencing optimization for 500 epochs, with 215696 positive edges
22:57:57 Optimization finished
[1] "161 0.13"22:57:57 UMAP embedding parameters a = 1.478 b = 0.9272
22:57:57 Read 1203 rows and found 38 numeric columns
22:57:57 Using Annoy for neighbor search, n_neighbors = 161
22:57:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:57:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c83497d
22:57:58 Searching Annoy index using 1 thread, search_k = 16100
22:57:59 Annoy recall = 100%
22:58:10 Commencing smooth kNN distance calibration using 1 thread
22:58:33 Initializing from normalized Laplacian + noise
22:58:33 Commencing optimization for 500 epochs, with 215696 positive edges
22:58:48 Optimization finished
[1] "161 0.14"22:58:49 UMAP embedding parameters a = 1.446 b = 0.938
22:58:49 Read 1203 rows and found 38 numeric columns
22:58:49 Using Annoy for neighbor search, n_neighbors = 161
22:58:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769c346a0
22:58:49 Searching Annoy index using 1 thread, search_k = 16100
22:58:51 Annoy recall = 100%
22:59:02 Commencing smooth kNN distance calibration using 1 thread
22:59:24 Initializing from normalized Laplacian + noise
22:59:24 Commencing optimization for 500 epochs, with 215696 positive edges
22:59:40 Optimization finished
[1] "161 0.15"22:59:40 UMAP embedding parameters a = 1.414 b = 0.9488
22:59:40 Read 1203 rows and found 38 numeric columns
22:59:40 Using Annoy for neighbor search, n_neighbors = 161
22:59:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:59:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741923be5
22:59:41 Searching Annoy index using 1 thread, search_k = 16100
22:59:42 Annoy recall = 100%
22:59:53 Commencing smooth kNN distance calibration using 1 thread
23:00:15 Initializing from normalized Laplacian + noise
23:00:16 Commencing optimization for 500 epochs, with 215696 positive edges
23:00:31 Optimization finished
[1] "161 0.16"23:00:31 UMAP embedding parameters a = 1.383 b = 0.9596
23:00:31 Read 1203 rows and found 38 numeric columns
23:00:31 Using Annoy for neighbor search, n_neighbors = 161
23:00:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:00:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f38c2f7
23:00:32 Searching Annoy index using 1 thread, search_k = 16100
23:00:33 Annoy recall = 100%
23:00:45 Commencing smooth kNN distance calibration using 1 thread
23:01:07 Initializing from normalized Laplacian + noise
23:01:07 Commencing optimization for 500 epochs, with 215696 positive edges
23:01:23 Optimization finished
[1] "161 0.17"23:01:23 UMAP embedding parameters a = 1.352 b = 0.9704
23:01:23 Read 1203 rows and found 38 numeric columns
23:01:23 Using Annoy for neighbor search, n_neighbors = 161
23:01:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:01:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fe5151f
23:01:24 Searching Annoy index using 1 thread, search_k = 16100
23:01:25 Annoy recall = 100%
23:01:36 Commencing smooth kNN distance calibration using 1 thread
23:01:58 Initializing from normalized Laplacian + noise
23:01:58 Commencing optimization for 500 epochs, with 215696 positive edges
23:02:13 Optimization finished
[1] "161 0.18"23:02:14 UMAP embedding parameters a = 1.321 b = 0.9813
23:02:14 Read 1203 rows and found 38 numeric columns
23:02:14 Using Annoy for neighbor search, n_neighbors = 161
23:02:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:02:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c9fe691
23:02:14 Searching Annoy index using 1 thread, search_k = 16100
23:02:16 Annoy recall = 100%
23:02:26 Commencing smooth kNN distance calibration using 1 thread
23:02:49 Initializing from normalized Laplacian + noise
23:02:49 Commencing optimization for 500 epochs, with 215696 positive edges
23:03:04 Optimization finished
[1] "161 0.19"23:03:04 UMAP embedding parameters a = 1.292 b = 0.9921
23:03:04 Read 1203 rows and found 38 numeric columns
23:03:04 Using Annoy for neighbor search, n_neighbors = 161
23:03:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:03:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c943dfd
23:03:05 Searching Annoy index using 1 thread, search_k = 16100
23:03:06 Annoy recall = 100%
23:03:17 Commencing smooth kNN distance calibration using 1 thread
23:03:39 Initializing from normalized Laplacian + noise
23:03:39 Commencing optimization for 500 epochs, with 215696 positive edges
23:03:55 Optimization finished
[1] "161 0.2"23:03:55 UMAP embedding parameters a = 1.262 b = 1.003
23:03:55 Read 1203 rows and found 38 numeric columns
23:03:55 Using Annoy for neighbor search, n_neighbors = 161
23:03:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:03:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a17c04
23:03:56 Searching Annoy index using 1 thread, search_k = 16100
23:03:57 Annoy recall = 100%
23:04:08 Commencing smooth kNN distance calibration using 1 thread
23:04:30 Initializing from normalized Laplacian + noise
23:04:30 Commencing optimization for 500 epochs, with 215696 positive edges
23:04:45 Optimization finished
[1] "162 0"23:04:45 UMAP embedding parameters a = 1.933 b = 0.7905
23:04:45 Read 1203 rows and found 38 numeric columns
23:04:46 Using Annoy for neighbor search, n_neighbors = 162
23:04:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:04:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87691b3a22
23:04:46 Searching Annoy index using 1 thread, search_k = 16200
23:04:47 Annoy recall = 100%
23:04:58 Commencing smooth kNN distance calibration using 1 thread
23:05:21 Initializing from normalized Laplacian + noise
23:05:21 Commencing optimization for 500 epochs, with 216838 positive edges
23:05:36 Optimization finished
[1] "162 0.01"23:05:36 UMAP embedding parameters a = 1.896 b = 0.8006
23:05:36 Read 1203 rows and found 38 numeric columns
23:05:36 Using Annoy for neighbor search, n_neighbors = 162
23:05:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:05:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753955a1b
23:05:37 Searching Annoy index using 1 thread, search_k = 16200
23:05:38 Annoy recall = 100%
23:05:49 Commencing smooth kNN distance calibration using 1 thread
23:06:11 Initializing from normalized Laplacian + noise
23:06:11 Commencing optimization for 500 epochs, with 216838 positive edges
23:06:27 Optimization finished
[1] "162 0.02"23:06:27 UMAP embedding parameters a = 1.859 b = 0.8109
23:06:27 Read 1203 rows and found 38 numeric columns
23:06:27 Using Annoy for neighbor search, n_neighbors = 162
23:06:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:06:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d7f5b2f
23:06:28 Searching Annoy index using 1 thread, search_k = 16200
23:06:29 Annoy recall = 100%
23:06:40 Commencing smooth kNN distance calibration using 1 thread
23:07:02 Initializing from normalized Laplacian + noise
23:07:02 Commencing optimization for 500 epochs, with 216838 positive edges
23:07:17 Optimization finished
[1] "162 0.03"23:07:18 UMAP embedding parameters a = 1.822 b = 0.8212
23:07:18 Read 1203 rows and found 38 numeric columns
23:07:18 Using Annoy for neighbor search, n_neighbors = 162
23:07:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:07:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744f11090
23:07:19 Searching Annoy index using 1 thread, search_k = 16200
23:07:20 Annoy recall = 100%
23:07:30 Commencing smooth kNN distance calibration using 1 thread
23:07:52 Initializing from normalized Laplacian + noise
23:07:53 Commencing optimization for 500 epochs, with 216838 positive edges
23:08:08 Optimization finished
[1] "162 0.04"23:08:08 UMAP embedding parameters a = 1.786 b = 0.8316
23:08:08 Read 1203 rows and found 38 numeric columns
23:08:08 Using Annoy for neighbor search, n_neighbors = 162
23:08:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:08:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728fd9d9c
23:08:09 Searching Annoy index using 1 thread, search_k = 16200
23:08:10 Annoy recall = 100%
23:08:21 Commencing smooth kNN distance calibration using 1 thread
23:08:43 Initializing from normalized Laplacian + noise
23:08:43 Commencing optimization for 500 epochs, with 216838 positive edges
23:08:59 Optimization finished
[1] "162 0.05"23:08:59 UMAP embedding parameters a = 1.75 b = 0.8421
23:08:59 Read 1203 rows and found 38 numeric columns
23:08:59 Using Annoy for neighbor search, n_neighbors = 162
23:08:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:09:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e254636
23:09:00 Searching Annoy index using 1 thread, search_k = 16200
23:09:01 Annoy recall = 100%
23:09:12 Commencing smooth kNN distance calibration using 1 thread
23:09:34 Initializing from normalized Laplacian + noise
23:09:34 Commencing optimization for 500 epochs, with 216838 positive edges
23:09:49 Optimization finished
[1] "162 0.06"23:09:49 UMAP embedding parameters a = 1.715 b = 0.8526
23:09:49 Read 1203 rows and found 38 numeric columns
23:09:49 Using Annoy for neighbor search, n_neighbors = 162
23:09:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:09:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f96d870
23:09:50 Searching Annoy index using 1 thread, search_k = 16200
23:09:51 Annoy recall = 100%
23:10:02 Commencing smooth kNN distance calibration using 1 thread
23:10:25 Initializing from normalized Laplacian + noise
23:10:25 Commencing optimization for 500 epochs, with 216838 positive edges
23:10:40 Optimization finished
[1] "162 0.07"23:10:40 UMAP embedding parameters a = 1.68 b = 0.8631
23:10:40 Read 1203 rows and found 38 numeric columns
23:10:40 Using Annoy for neighbor search, n_neighbors = 162
23:10:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:10:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727070873
23:10:41 Searching Annoy index using 1 thread, search_k = 16200
23:10:42 Annoy recall = 100%
23:10:53 Commencing smooth kNN distance calibration using 1 thread
23:11:15 Initializing from normalized Laplacian + noise
23:11:15 Commencing optimization for 500 epochs, with 216838 positive edges
23:11:31 Optimization finished
[1] "162 0.08"23:11:31 UMAP embedding parameters a = 1.645 b = 0.8737
23:11:31 Read 1203 rows and found 38 numeric columns
23:11:31 Using Annoy for neighbor search, n_neighbors = 162
23:11:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:11:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877abccf5e
23:11:32 Searching Annoy index using 1 thread, search_k = 16200
23:11:33 Annoy recall = 100%
23:11:44 Commencing smooth kNN distance calibration using 1 thread
23:12:06 Initializing from normalized Laplacian + noise
23:12:06 Commencing optimization for 500 epochs, with 216838 positive edges
23:12:21 Optimization finished
[1] "162 0.09"23:12:21 UMAP embedding parameters a = 1.611 b = 0.8844
23:12:22 Read 1203 rows and found 38 numeric columns
23:12:22 Using Annoy for neighbor search, n_neighbors = 162
23:12:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:12:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876dc400b1
23:12:22 Searching Annoy index using 1 thread, search_k = 16200
23:12:23 Annoy recall = 100%
23:12:35 Commencing smooth kNN distance calibration using 1 thread
23:12:56 Initializing from normalized Laplacian + noise
23:12:57 Commencing optimization for 500 epochs, with 216838 positive edges
23:13:12 Optimization finished
[1] "162 0.1"23:13:12 UMAP embedding parameters a = 1.577 b = 0.8951
23:13:12 Read 1203 rows and found 38 numeric columns
23:13:12 Using Annoy for neighbor search, n_neighbors = 162
23:13:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:13:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87398205e0
23:13:13 Searching Annoy index using 1 thread, search_k = 16200
23:13:14 Annoy recall = 100%
23:13:25 Commencing smooth kNN distance calibration using 1 thread
23:13:48 Initializing from normalized Laplacian + noise
23:13:48 Commencing optimization for 500 epochs, with 216838 positive edges
23:14:03 Optimization finished
[1] "162 0.11"23:14:04 UMAP embedding parameters a = 1.544 b = 0.9058
23:14:04 Read 1203 rows and found 38 numeric columns
23:14:04 Using Annoy for neighbor search, n_neighbors = 162
23:14:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:14:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a8afd73
23:14:04 Searching Annoy index using 1 thread, search_k = 16200
23:14:06 Annoy recall = 100%
23:14:17 Commencing smooth kNN distance calibration using 1 thread
23:14:40 Initializing from normalized Laplacian + noise
23:14:40 Commencing optimization for 500 epochs, with 216838 positive edges
23:14:55 Optimization finished
[1] "162 0.12"23:14:55 UMAP embedding parameters a = 1.51 b = 0.9165
23:14:55 Read 1203 rows and found 38 numeric columns
23:14:55 Using Annoy for neighbor search, n_neighbors = 162
23:14:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:14:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87291f2593
23:14:56 Searching Annoy index using 1 thread, search_k = 16200
23:14:57 Annoy recall = 100%
23:15:08 Commencing smooth kNN distance calibration using 1 thread
23:15:31 Initializing from normalized Laplacian + noise
23:15:31 Commencing optimization for 500 epochs, with 216838 positive edges
23:15:47 Optimization finished
[1] "162 0.13"23:15:47 UMAP embedding parameters a = 1.478 b = 0.9272
23:15:47 Read 1203 rows and found 38 numeric columns
23:15:47 Using Annoy for neighbor search, n_neighbors = 162
23:15:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:15:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773a0434c
23:15:48 Searching Annoy index using 1 thread, search_k = 16200
23:15:49 Annoy recall = 100%
23:16:00 Commencing smooth kNN distance calibration using 1 thread
23:16:23 Initializing from normalized Laplacian + noise
23:16:23 Commencing optimization for 500 epochs, with 216838 positive edges
23:16:38 Optimization finished
[1] "162 0.14"23:16:39 UMAP embedding parameters a = 1.446 b = 0.938
23:16:39 Read 1203 rows and found 38 numeric columns
23:16:39 Using Annoy for neighbor search, n_neighbors = 162
23:16:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:16:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fa8f31b
23:16:39 Searching Annoy index using 1 thread, search_k = 16200
23:16:41 Annoy recall = 100%
23:16:52 Commencing smooth kNN distance calibration using 1 thread
23:17:14 Initializing from normalized Laplacian + noise
23:17:14 Commencing optimization for 500 epochs, with 216838 positive edges
23:17:30 Optimization finished
[1] "162 0.15"23:17:30 UMAP embedding parameters a = 1.414 b = 0.9488
23:17:30 Read 1203 rows and found 38 numeric columns
23:17:30 Using Annoy for neighbor search, n_neighbors = 162
23:17:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:17:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a605729
23:17:31 Searching Annoy index using 1 thread, search_k = 16200
23:17:32 Annoy recall = 100%
23:17:44 Commencing smooth kNN distance calibration using 1 thread
23:18:06 Initializing from normalized Laplacian + noise
23:18:06 Commencing optimization for 500 epochs, with 216838 positive edges
23:18:22 Optimization finished
[1] "162 0.16"23:18:22 UMAP embedding parameters a = 1.383 b = 0.9596
23:18:22 Read 1203 rows and found 38 numeric columns
23:18:22 Using Annoy for neighbor search, n_neighbors = 162
23:18:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:18:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877eb6cc88
23:18:23 Searching Annoy index using 1 thread, search_k = 16200
23:18:24 Annoy recall = 100%
23:18:35 Commencing smooth kNN distance calibration using 1 thread
23:18:58 Initializing from normalized Laplacian + noise
23:18:58 Commencing optimization for 500 epochs, with 216838 positive edges
23:19:13 Optimization finished
[1] "162 0.17"23:19:14 UMAP embedding parameters a = 1.352 b = 0.9704
23:19:14 Read 1203 rows and found 38 numeric columns
23:19:14 Using Annoy for neighbor search, n_neighbors = 162
23:19:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:19:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87660e915c
23:19:14 Searching Annoy index using 1 thread, search_k = 16200
23:19:16 Annoy recall = 100%
23:19:27 Commencing smooth kNN distance calibration using 1 thread
23:19:50 Initializing from normalized Laplacian + noise
23:19:50 Commencing optimization for 500 epochs, with 216838 positive edges
23:20:05 Optimization finished
[1] "162 0.18"23:20:06 UMAP embedding parameters a = 1.321 b = 0.9813
23:20:06 Read 1203 rows and found 38 numeric columns
23:20:06 Using Annoy for neighbor search, n_neighbors = 162
23:20:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:20:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713fdb1d9
23:20:06 Searching Annoy index using 1 thread, search_k = 16200
23:20:08 Annoy recall = 100%
23:20:19 Commencing smooth kNN distance calibration using 1 thread
23:20:41 Initializing from normalized Laplacian + noise
23:20:41 Commencing optimization for 500 epochs, with 216838 positive edges
23:20:57 Optimization finished
[1] "162 0.19"23:20:57 UMAP embedding parameters a = 1.292 b = 0.9921
23:20:57 Read 1203 rows and found 38 numeric columns
23:20:57 Using Annoy for neighbor search, n_neighbors = 162
23:20:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:20:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879a46edf
23:20:58 Searching Annoy index using 1 thread, search_k = 16200
23:20:59 Annoy recall = 100%
23:21:10 Commencing smooth kNN distance calibration using 1 thread
23:21:32 Initializing from normalized Laplacian + noise
23:21:32 Commencing optimization for 500 epochs, with 216838 positive edges
23:21:48 Optimization finished
[1] "162 0.2"23:21:48 UMAP embedding parameters a = 1.262 b = 1.003
23:21:48 Read 1203 rows and found 38 numeric columns
23:21:48 Using Annoy for neighbor search, n_neighbors = 162
23:21:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:21:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760ad5924
23:21:49 Searching Annoy index using 1 thread, search_k = 16200
23:21:50 Annoy recall = 100%
23:22:01 Commencing smooth kNN distance calibration using 1 thread
23:22:23 Initializing from normalized Laplacian + noise
23:22:23 Commencing optimization for 500 epochs, with 216838 positive edges
23:22:38 Optimization finished
[1] "163 0"23:22:38 UMAP embedding parameters a = 1.933 b = 0.7905
23:22:38 Read 1203 rows and found 38 numeric columns
23:22:38 Using Annoy for neighbor search, n_neighbors = 163
23:22:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:22:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a16cee0
23:22:39 Searching Annoy index using 1 thread, search_k = 16300
23:22:40 Annoy recall = 100%
23:22:51 Commencing smooth kNN distance calibration using 1 thread
23:23:13 Initializing from normalized Laplacian + noise
23:23:14 Commencing optimization for 500 epochs, with 218016 positive edges
23:23:29 Optimization finished
[1] "163 0.01"23:23:29 UMAP embedding parameters a = 1.896 b = 0.8006
23:23:29 Read 1203 rows and found 38 numeric columns
23:23:29 Using Annoy for neighbor search, n_neighbors = 163
23:23:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:23:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775ad0e3f
23:23:30 Searching Annoy index using 1 thread, search_k = 16300
23:23:31 Annoy recall = 100%
23:23:42 Commencing smooth kNN distance calibration using 1 thread
23:24:04 Initializing from normalized Laplacian + noise
23:24:04 Commencing optimization for 500 epochs, with 218016 positive edges
23:24:19 Optimization finished
[1] "163 0.02"23:24:20 UMAP embedding parameters a = 1.859 b = 0.8109
23:24:20 Read 1203 rows and found 38 numeric columns
23:24:20 Using Annoy for neighbor search, n_neighbors = 163
23:24:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:24:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d30a2a2
23:24:20 Searching Annoy index using 1 thread, search_k = 16300
23:24:22 Annoy recall = 100%
23:24:32 Commencing smooth kNN distance calibration using 1 thread
23:24:54 Initializing from normalized Laplacian + noise
23:24:55 Commencing optimization for 500 epochs, with 218016 positive edges
23:25:10 Optimization finished
[1] "163 0.03"23:25:10 UMAP embedding parameters a = 1.822 b = 0.8212
23:25:10 Read 1203 rows and found 38 numeric columns
23:25:10 Using Annoy for neighbor search, n_neighbors = 163
23:25:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:25:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713da1581
23:25:11 Searching Annoy index using 1 thread, search_k = 16300
23:25:12 Annoy recall = 100%
23:25:23 Commencing smooth kNN distance calibration using 1 thread
23:25:45 Initializing from normalized Laplacian + noise
23:25:45 Commencing optimization for 500 epochs, with 218016 positive edges
23:26:00 Optimization finished
[1] "163 0.04"23:26:01 UMAP embedding parameters a = 1.786 b = 0.8316
23:26:01 Read 1203 rows and found 38 numeric columns
23:26:01 Using Annoy for neighbor search, n_neighbors = 163
23:26:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:26:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87373f4a25
23:26:01 Searching Annoy index using 1 thread, search_k = 16300
23:26:03 Annoy recall = 100%
23:26:14 Commencing smooth kNN distance calibration using 1 thread
23:26:36 Initializing from normalized Laplacian + noise
23:26:36 Commencing optimization for 500 epochs, with 218016 positive edges
23:26:51 Optimization finished
[1] "163 0.05"23:26:51 UMAP embedding parameters a = 1.75 b = 0.8421
23:26:51 Read 1203 rows and found 38 numeric columns
23:26:51 Using Annoy for neighbor search, n_neighbors = 163
23:26:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:26:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c696599
23:26:52 Searching Annoy index using 1 thread, search_k = 16300
23:26:53 Annoy recall = 100%
23:27:04 Commencing smooth kNN distance calibration using 1 thread
23:27:26 Initializing from normalized Laplacian + noise
23:27:26 Commencing optimization for 500 epochs, with 218016 positive edges
23:27:42 Optimization finished
[1] "163 0.06"23:27:42 UMAP embedding parameters a = 1.715 b = 0.8526
23:27:42 Read 1203 rows and found 38 numeric columns
23:27:42 Using Annoy for neighbor search, n_neighbors = 163
23:27:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:27:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743bf2aa0
23:27:43 Searching Annoy index using 1 thread, search_k = 16300
23:27:44 Annoy recall = 100%
23:27:55 Commencing smooth kNN distance calibration using 1 thread
23:28:17 Initializing from normalized Laplacian + noise
23:28:17 Commencing optimization for 500 epochs, with 218016 positive edges
23:28:32 Optimization finished
[1] "163 0.07"23:28:33 UMAP embedding parameters a = 1.68 b = 0.8631
23:28:33 Read 1203 rows and found 38 numeric columns
23:28:33 Using Annoy for neighbor search, n_neighbors = 163
23:28:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:28:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8773df30b6
23:28:33 Searching Annoy index using 1 thread, search_k = 16300
23:28:34 Annoy recall = 100%
23:28:45 Commencing smooth kNN distance calibration using 1 thread
23:29:07 Initializing from normalized Laplacian + noise
23:29:07 Commencing optimization for 500 epochs, with 218016 positive edges
23:29:23 Optimization finished
[1] "163 0.08"23:29:23 UMAP embedding parameters a = 1.645 b = 0.8737
23:29:23 Read 1203 rows and found 38 numeric columns
23:29:23 Using Annoy for neighbor search, n_neighbors = 163
23:29:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:29:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778fda396
23:29:24 Searching Annoy index using 1 thread, search_k = 16300
23:29:25 Annoy recall = 100%
23:29:36 Commencing smooth kNN distance calibration using 1 thread
23:29:58 Initializing from normalized Laplacian + noise
23:29:58 Commencing optimization for 500 epochs, with 218016 positive edges
23:30:13 Optimization finished
[1] "163 0.09"23:30:14 UMAP embedding parameters a = 1.611 b = 0.8844
23:30:14 Read 1203 rows and found 38 numeric columns
23:30:14 Using Annoy for neighbor search, n_neighbors = 163
23:30:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:30:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874960a6a4
23:30:15 Searching Annoy index using 1 thread, search_k = 16300
23:30:16 Annoy recall = 100%
23:30:27 Commencing smooth kNN distance calibration using 1 thread
23:30:49 Initializing from normalized Laplacian + noise
23:30:49 Commencing optimization for 500 epochs, with 218016 positive edges
23:31:04 Optimization finished
[1] "163 0.1"23:31:04 UMAP embedding parameters a = 1.577 b = 0.8951
23:31:04 Read 1203 rows and found 38 numeric columns
23:31:04 Using Annoy for neighbor search, n_neighbors = 163
23:31:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:31:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875cfa6ad8
23:31:05 Searching Annoy index using 1 thread, search_k = 16300
23:31:06 Annoy recall = 100%
23:31:17 Commencing smooth kNN distance calibration using 1 thread
23:31:39 Initializing from normalized Laplacian + noise
23:31:40 Commencing optimization for 500 epochs, with 218016 positive edges
23:31:55 Optimization finished
[1] "163 0.11"23:31:55 UMAP embedding parameters a = 1.544 b = 0.9058
23:31:55 Read 1203 rows and found 38 numeric columns
23:31:55 Using Annoy for neighbor search, n_neighbors = 163
23:31:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:31:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c92fdb2
23:31:56 Searching Annoy index using 1 thread, search_k = 16300
23:31:57 Annoy recall = 100%
23:32:08 Commencing smooth kNN distance calibration using 1 thread
23:32:30 Initializing from normalized Laplacian + noise
23:32:30 Commencing optimization for 500 epochs, with 218016 positive edges
23:32:46 Optimization finished
[1] "163 0.12"23:32:46 UMAP embedding parameters a = 1.51 b = 0.9165
23:32:46 Read 1203 rows and found 38 numeric columns
23:32:46 Using Annoy for neighbor search, n_neighbors = 163
23:32:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:32:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776e001d4
23:32:47 Searching Annoy index using 1 thread, search_k = 16300
23:32:48 Annoy recall = 100%
23:32:59 Commencing smooth kNN distance calibration using 1 thread
23:33:21 Initializing from normalized Laplacian + noise
23:33:21 Commencing optimization for 500 epochs, with 218016 positive edges
23:33:36 Optimization finished
[1] "163 0.13"23:33:36 UMAP embedding parameters a = 1.478 b = 0.9272
23:33:37 Read 1203 rows and found 38 numeric columns
23:33:37 Using Annoy for neighbor search, n_neighbors = 163
23:33:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:33:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721eb7b68
23:33:37 Searching Annoy index using 1 thread, search_k = 16300
23:33:38 Annoy recall = 100%
23:33:50 Commencing smooth kNN distance calibration using 1 thread
23:34:11 Initializing from normalized Laplacian + noise
23:34:11 Commencing optimization for 500 epochs, with 218016 positive edges
23:34:27 Optimization finished
[1] "163 0.14"23:34:27 UMAP embedding parameters a = 1.446 b = 0.938
23:34:27 Read 1203 rows and found 38 numeric columns
23:34:27 Using Annoy for neighbor search, n_neighbors = 163
23:34:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:34:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775909b4e
23:34:28 Searching Annoy index using 1 thread, search_k = 16300
23:34:29 Annoy recall = 100%
23:34:40 Commencing smooth kNN distance calibration using 1 thread
23:35:02 Initializing from normalized Laplacian + noise
23:35:02 Commencing optimization for 500 epochs, with 218016 positive edges
23:35:18 Optimization finished
[1] "163 0.15"23:35:18 UMAP embedding parameters a = 1.414 b = 0.9488
23:35:18 Read 1203 rows and found 38 numeric columns
23:35:18 Using Annoy for neighbor search, n_neighbors = 163
23:35:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:35:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876505480a
23:35:19 Searching Annoy index using 1 thread, search_k = 16300
23:35:20 Annoy recall = 100%
23:35:31 Commencing smooth kNN distance calibration using 1 thread
23:35:53 Initializing from normalized Laplacian + noise
23:35:53 Commencing optimization for 500 epochs, with 218016 positive edges
23:36:08 Optimization finished
[1] "163 0.16"23:36:09 UMAP embedding parameters a = 1.383 b = 0.9596
23:36:09 Read 1203 rows and found 38 numeric columns
23:36:09 Using Annoy for neighbor search, n_neighbors = 163
23:36:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:36:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87518253d8
23:36:09 Searching Annoy index using 1 thread, search_k = 16300
23:36:11 Annoy recall = 100%
23:36:22 Commencing smooth kNN distance calibration using 1 thread
23:36:44 Initializing from normalized Laplacian + noise
23:36:44 Commencing optimization for 500 epochs, with 218016 positive edges
23:36:59 Optimization finished
[1] "163 0.17"23:37:00 UMAP embedding parameters a = 1.352 b = 0.9704
23:37:00 Read 1203 rows and found 38 numeric columns
23:37:00 Using Annoy for neighbor search, n_neighbors = 163
23:37:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:37:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c97a3c1
23:37:00 Searching Annoy index using 1 thread, search_k = 16300
23:37:02 Annoy recall = 100%
23:37:12 Commencing smooth kNN distance calibration using 1 thread
23:37:35 Initializing from normalized Laplacian + noise
23:37:35 Commencing optimization for 500 epochs, with 218016 positive edges
23:37:50 Optimization finished
[1] "163 0.18"23:37:50 UMAP embedding parameters a = 1.321 b = 0.9813
23:37:50 Read 1203 rows and found 38 numeric columns
23:37:50 Using Annoy for neighbor search, n_neighbors = 163
23:37:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:37:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fc21768
23:37:51 Searching Annoy index using 1 thread, search_k = 16300
23:37:52 Annoy recall = 100%
23:38:03 Commencing smooth kNN distance calibration using 1 thread
23:38:25 Initializing from normalized Laplacian + noise
23:38:26 Commencing optimization for 500 epochs, with 218016 positive edges
23:38:41 Optimization finished
[1] "163 0.19"23:38:41 UMAP embedding parameters a = 1.292 b = 0.9921
23:38:41 Read 1203 rows and found 38 numeric columns
23:38:41 Using Annoy for neighbor search, n_neighbors = 163
23:38:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:38:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f465489
23:38:42 Searching Annoy index using 1 thread, search_k = 16300
23:38:43 Annoy recall = 100%
23:38:54 Commencing smooth kNN distance calibration using 1 thread
23:39:16 Initializing from normalized Laplacian + noise
23:39:17 Commencing optimization for 500 epochs, with 218016 positive edges
23:39:32 Optimization finished
[1] "163 0.2"23:39:32 UMAP embedding parameters a = 1.262 b = 1.003
23:39:32 Read 1203 rows and found 38 numeric columns
23:39:32 Using Annoy for neighbor search, n_neighbors = 163
23:39:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:39:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875619a9a1
23:39:33 Searching Annoy index using 1 thread, search_k = 16300
23:39:34 Annoy recall = 100%
23:39:45 Commencing smooth kNN distance calibration using 1 thread
23:40:07 Initializing from normalized Laplacian + noise
23:40:08 Commencing optimization for 500 epochs, with 218016 positive edges
23:40:23 Optimization finished
[1] "164 0"23:40:23 UMAP embedding parameters a = 1.933 b = 0.7905
23:40:23 Read 1203 rows and found 38 numeric columns
23:40:23 Using Annoy for neighbor search, n_neighbors = 164
23:40:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:40:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a4d14db
23:40:24 Searching Annoy index using 1 thread, search_k = 16400
23:40:25 Annoy recall = 100%
23:40:36 Commencing smooth kNN distance calibration using 1 thread
23:40:58 Initializing from normalized Laplacian + noise
23:40:58 Commencing optimization for 500 epochs, with 219192 positive edges
23:41:14 Optimization finished
[1] "164 0.01"23:41:14 UMAP embedding parameters a = 1.896 b = 0.8006
23:41:14 Read 1203 rows and found 38 numeric columns
23:41:14 Using Annoy for neighbor search, n_neighbors = 164
23:41:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:41:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768657a1c
23:41:15 Searching Annoy index using 1 thread, search_k = 16400
23:41:16 Annoy recall = 100%
23:41:27 Commencing smooth kNN distance calibration using 1 thread
23:41:49 Initializing from normalized Laplacian + noise
23:41:49 Commencing optimization for 500 epochs, with 219192 positive edges
23:42:05 Optimization finished
[1] "164 0.02"23:42:05 UMAP embedding parameters a = 1.859 b = 0.8109
23:42:05 Read 1203 rows and found 38 numeric columns
23:42:05 Using Annoy for neighbor search, n_neighbors = 164
23:42:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:42:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749b9eced
23:42:06 Searching Annoy index using 1 thread, search_k = 16400
23:42:07 Annoy recall = 100%
23:42:18 Commencing smooth kNN distance calibration using 1 thread
23:42:40 Initializing from normalized Laplacian + noise
23:42:40 Commencing optimization for 500 epochs, with 219192 positive edges
23:42:55 Optimization finished
[1] "164 0.03"23:42:56 UMAP embedding parameters a = 1.822 b = 0.8212
23:42:56 Read 1203 rows and found 38 numeric columns
23:42:56 Using Annoy for neighbor search, n_neighbors = 164
23:42:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:42:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719f607f7
23:42:56 Searching Annoy index using 1 thread, search_k = 16400
23:42:58 Annoy recall = 100%
23:43:09 Commencing smooth kNN distance calibration using 1 thread
23:43:31 Initializing from normalized Laplacian + noise
23:43:31 Commencing optimization for 500 epochs, with 219192 positive edges
23:43:46 Optimization finished
[1] "164 0.04"23:43:46 UMAP embedding parameters a = 1.786 b = 0.8316
23:43:46 Read 1203 rows and found 38 numeric columns
23:43:46 Using Annoy for neighbor search, n_neighbors = 164
23:43:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:43:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732c5d145
23:43:47 Searching Annoy index using 1 thread, search_k = 16400
23:43:48 Annoy recall = 100%
23:43:59 Commencing smooth kNN distance calibration using 1 thread
23:44:22 Initializing from normalized Laplacian + noise
23:44:22 Commencing optimization for 500 epochs, with 219192 positive edges
23:44:37 Optimization finished
[1] "164 0.05"23:44:37 UMAP embedding parameters a = 1.75 b = 0.8421
23:44:37 Read 1203 rows and found 38 numeric columns
23:44:37 Using Annoy for neighbor search, n_neighbors = 164
23:44:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:44:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874870b975
23:44:38 Searching Annoy index using 1 thread, search_k = 16400
23:44:39 Annoy recall = 100%
23:44:50 Commencing smooth kNN distance calibration using 1 thread
23:45:13 Initializing from normalized Laplacian + noise
23:45:13 Commencing optimization for 500 epochs, with 219192 positive edges
23:45:28 Optimization finished
[1] "164 0.06"23:45:28 UMAP embedding parameters a = 1.715 b = 0.8526
23:45:28 Read 1203 rows and found 38 numeric columns
23:45:28 Using Annoy for neighbor search, n_neighbors = 164
23:45:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:45:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8749953
23:45:29 Searching Annoy index using 1 thread, search_k = 16400
23:45:30 Annoy recall = 100%
23:45:41 Commencing smooth kNN distance calibration using 1 thread
23:46:03 Initializing from normalized Laplacian + noise
23:46:03 Commencing optimization for 500 epochs, with 219192 positive edges
23:46:19 Optimization finished
[1] "164 0.07"23:46:19 UMAP embedding parameters a = 1.68 b = 0.8631
23:46:19 Read 1203 rows and found 38 numeric columns
23:46:19 Using Annoy for neighbor search, n_neighbors = 164
23:46:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:46:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746c3831e
23:46:20 Searching Annoy index using 1 thread, search_k = 16400
23:46:21 Annoy recall = 100%
23:46:32 Commencing smooth kNN distance calibration using 1 thread
23:46:54 Initializing from normalized Laplacian + noise
23:46:54 Commencing optimization for 500 epochs, with 219192 positive edges
23:47:10 Optimization finished
[1] "164 0.08"23:47:10 UMAP embedding parameters a = 1.645 b = 0.8737
23:47:10 Read 1203 rows and found 38 numeric columns
23:47:10 Using Annoy for neighbor search, n_neighbors = 164
23:47:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:47:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752152854
23:47:11 Searching Annoy index using 1 thread, search_k = 16400
23:47:12 Annoy recall = 100%
23:47:23 Commencing smooth kNN distance calibration using 1 thread
23:47:45 Initializing from normalized Laplacian + noise
23:47:45 Commencing optimization for 500 epochs, with 219192 positive edges
23:48:00 Optimization finished
[1] "164 0.09"23:48:01 UMAP embedding parameters a = 1.611 b = 0.8844
23:48:01 Read 1203 rows and found 38 numeric columns
23:48:01 Using Annoy for neighbor search, n_neighbors = 164
23:48:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:48:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760b1f277
23:48:02 Searching Annoy index using 1 thread, search_k = 16400
23:48:03 Annoy recall = 100%
23:48:14 Commencing smooth kNN distance calibration using 1 thread
23:48:36 Initializing from normalized Laplacian + noise
23:48:36 Commencing optimization for 500 epochs, with 219192 positive edges
23:48:52 Optimization finished
[1] "164 0.1"23:48:52 UMAP embedding parameters a = 1.577 b = 0.8951
23:48:52 Read 1203 rows and found 38 numeric columns
23:48:52 Using Annoy for neighbor search, n_neighbors = 164
23:48:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:48:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770da51ff
23:48:53 Searching Annoy index using 1 thread, search_k = 16400
23:48:54 Annoy recall = 100%
23:49:05 Commencing smooth kNN distance calibration using 1 thread
23:49:27 Initializing from normalized Laplacian + noise
23:49:27 Commencing optimization for 500 epochs, with 219192 positive edges
23:49:43 Optimization finished
[1] "164 0.11"23:49:43 UMAP embedding parameters a = 1.544 b = 0.9058
23:49:43 Read 1203 rows and found 38 numeric columns
23:49:43 Using Annoy for neighbor search, n_neighbors = 164
23:49:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747c23694
23:49:44 Searching Annoy index using 1 thread, search_k = 16400
23:49:45 Annoy recall = 100%
23:49:56 Commencing smooth kNN distance calibration using 1 thread
23:50:18 Initializing from normalized Laplacian + noise
23:50:18 Commencing optimization for 500 epochs, with 219192 positive edges
23:50:34 Optimization finished
[1] "164 0.12"23:50:34 UMAP embedding parameters a = 1.51 b = 0.9165
23:50:34 Read 1203 rows and found 38 numeric columns
23:50:34 Using Annoy for neighbor search, n_neighbors = 164
23:50:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:50:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87de29519
23:50:35 Searching Annoy index using 1 thread, search_k = 16400
23:50:36 Annoy recall = 100%
23:50:47 Commencing smooth kNN distance calibration using 1 thread
23:51:09 Initializing from normalized Laplacian + noise
23:51:09 Commencing optimization for 500 epochs, with 219192 positive edges
23:51:25 Optimization finished
[1] "164 0.13"23:51:25 UMAP embedding parameters a = 1.478 b = 0.9272
23:51:25 Read 1203 rows and found 38 numeric columns
23:51:25 Using Annoy for neighbor search, n_neighbors = 164
23:51:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:51:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b46780
23:51:26 Searching Annoy index using 1 thread, search_k = 16400
23:51:27 Annoy recall = 100%
23:51:38 Commencing smooth kNN distance calibration using 1 thread
23:52:00 Initializing from normalized Laplacian + noise
23:52:00 Commencing optimization for 500 epochs, with 219192 positive edges
23:52:16 Optimization finished
[1] "164 0.14"23:52:16 UMAP embedding parameters a = 1.446 b = 0.938
23:52:16 Read 1203 rows and found 38 numeric columns
23:52:16 Using Annoy for neighbor search, n_neighbors = 164
23:52:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:52:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f0180b9
23:52:17 Searching Annoy index using 1 thread, search_k = 16400
23:52:18 Annoy recall = 100%
23:52:29 Commencing smooth kNN distance calibration using 1 thread
23:52:51 Initializing from normalized Laplacian + noise
23:52:51 Commencing optimization for 500 epochs, with 219192 positive edges
23:53:07 Optimization finished
[1] "164 0.15"23:53:07 UMAP embedding parameters a = 1.414 b = 0.9488
23:53:07 Read 1203 rows and found 38 numeric columns
23:53:07 Using Annoy for neighbor search, n_neighbors = 164
23:53:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:53:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a4bfab2
23:53:08 Searching Annoy index using 1 thread, search_k = 16400
23:53:09 Annoy recall = 100%
23:53:20 Commencing smooth kNN distance calibration using 1 thread
23:53:42 Initializing from normalized Laplacian + noise
23:53:42 Commencing optimization for 500 epochs, with 219192 positive edges
23:53:58 Optimization finished
[1] "164 0.16"23:53:58 UMAP embedding parameters a = 1.383 b = 0.9596
23:53:58 Read 1203 rows and found 38 numeric columns
23:53:58 Using Annoy for neighbor search, n_neighbors = 164
23:53:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:53:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748739220
23:53:59 Searching Annoy index using 1 thread, search_k = 16400
23:54:00 Annoy recall = 100%
23:54:11 Commencing smooth kNN distance calibration using 1 thread
23:54:33 Initializing from normalized Laplacian + noise
23:54:34 Commencing optimization for 500 epochs, with 219192 positive edges
23:54:49 Optimization finished
[1] "164 0.17"23:54:49 UMAP embedding parameters a = 1.352 b = 0.9704
23:54:49 Read 1203 rows and found 38 numeric columns
23:54:49 Using Annoy for neighbor search, n_neighbors = 164
23:54:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:54:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772e0b16f
23:54:50 Searching Annoy index using 1 thread, search_k = 16400
23:54:51 Annoy recall = 100%
23:55:02 Commencing smooth kNN distance calibration using 1 thread
23:55:25 Initializing from normalized Laplacian + noise
23:55:25 Commencing optimization for 500 epochs, with 219192 positive edges
23:55:40 Optimization finished
[1] "164 0.18"23:55:40 UMAP embedding parameters a = 1.321 b = 0.9813
23:55:40 Read 1203 rows and found 38 numeric columns
23:55:40 Using Annoy for neighbor search, n_neighbors = 164
23:55:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:55:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743499e49
23:55:41 Searching Annoy index using 1 thread, search_k = 16400
23:55:42 Annoy recall = 100%
23:55:53 Commencing smooth kNN distance calibration using 1 thread
23:56:16 Initializing from normalized Laplacian + noise
23:56:16 Commencing optimization for 500 epochs, with 219192 positive edges
23:56:31 Optimization finished
[1] "164 0.19"23:56:31 UMAP embedding parameters a = 1.292 b = 0.9921
23:56:31 Read 1203 rows and found 38 numeric columns
23:56:31 Using Annoy for neighbor search, n_neighbors = 164
23:56:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:56:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711d438c5
23:56:32 Searching Annoy index using 1 thread, search_k = 16400
23:56:33 Annoy recall = 100%
23:56:45 Commencing smooth kNN distance calibration using 1 thread
23:57:07 Initializing from normalized Laplacian + noise
23:57:07 Commencing optimization for 500 epochs, with 219192 positive edges
23:57:22 Optimization finished
[1] "164 0.2"23:57:23 UMAP embedding parameters a = 1.262 b = 1.003
23:57:23 Read 1203 rows and found 38 numeric columns
23:57:23 Using Annoy for neighbor search, n_neighbors = 164
23:57:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:57:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fdb1c48
23:57:24 Searching Annoy index using 1 thread, search_k = 16400
23:57:25 Annoy recall = 100%
23:57:36 Commencing smooth kNN distance calibration using 1 thread
23:57:58 Initializing from normalized Laplacian + noise
23:57:58 Commencing optimization for 500 epochs, with 219192 positive edges
23:58:14 Optimization finished
[1] "165 0"23:58:14 UMAP embedding parameters a = 1.933 b = 0.7905
23:58:14 Read 1203 rows and found 38 numeric columns
23:58:14 Using Annoy for neighbor search, n_neighbors = 165
23:58:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:58:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fdc9bfb
23:58:15 Searching Annoy index using 1 thread, search_k = 16500
23:58:16 Annoy recall = 100%
23:58:27 Commencing smooth kNN distance calibration using 1 thread
23:58:49 Initializing from normalized Laplacian + noise
23:58:49 Commencing optimization for 500 epochs, with 220376 positive edges
23:59:05 Optimization finished
[1] "165 0.01"23:59:05 UMAP embedding parameters a = 1.896 b = 0.8006
23:59:05 Read 1203 rows and found 38 numeric columns
23:59:05 Using Annoy for neighbor search, n_neighbors = 165
23:59:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:59:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878b43a99
23:59:06 Searching Annoy index using 1 thread, search_k = 16500
23:59:07 Annoy recall = 100%
23:59:18 Commencing smooth kNN distance calibration using 1 thread
23:59:40 Initializing from normalized Laplacian + noise
23:59:41 Commencing optimization for 500 epochs, with 220376 positive edges
23:59:56 Optimization finished
[1] "165 0.02"23:59:56 UMAP embedding parameters a = 1.859 b = 0.8109
23:59:56 Read 1203 rows and found 38 numeric columns
23:59:56 Using Annoy for neighbor search, n_neighbors = 165
23:59:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:59:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771c697b0
23:59:57 Searching Annoy index using 1 thread, search_k = 16500
23:59:58 Annoy recall = 100%
00:00:09 Commencing smooth kNN distance calibration using 1 thread
00:00:32 Initializing from normalized Laplacian + noise
00:00:32 Commencing optimization for 500 epochs, with 220376 positive edges
00:00:47 Optimization finished
[1] "165 0.03"00:00:47 UMAP embedding parameters a = 1.822 b = 0.8212
00:00:47 Read 1203 rows and found 38 numeric columns
00:00:47 Using Annoy for neighbor search, n_neighbors = 165
00:00:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:00:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756d3749
00:00:48 Searching Annoy index using 1 thread, search_k = 16500
00:00:49 Annoy recall = 100%
00:01:01 Commencing smooth kNN distance calibration using 1 thread
00:01:23 Initializing from normalized Laplacian + noise
00:01:23 Commencing optimization for 500 epochs, with 220376 positive edges
00:01:39 Optimization finished
[1] "165 0.04"00:01:39 UMAP embedding parameters a = 1.786 b = 0.8316
00:01:39 Read 1203 rows and found 38 numeric columns
00:01:39 Using Annoy for neighbor search, n_neighbors = 165
00:01:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:01:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876db982a3
00:01:40 Searching Annoy index using 1 thread, search_k = 16500
00:01:41 Annoy recall = 100%
00:01:52 Commencing smooth kNN distance calibration using 1 thread
00:02:14 Initializing from normalized Laplacian + noise
00:02:14 Commencing optimization for 500 epochs, with 220376 positive edges
00:02:30 Optimization finished
[1] "165 0.05"00:02:30 UMAP embedding parameters a = 1.75 b = 0.8421
00:02:30 Read 1203 rows and found 38 numeric columns
00:02:30 Using Annoy for neighbor search, n_neighbors = 165
00:02:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:02:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874348eb89
00:02:31 Searching Annoy index using 1 thread, search_k = 16500
00:02:32 Annoy recall = 100%
00:02:43 Commencing smooth kNN distance calibration using 1 thread
00:03:05 Initializing from normalized Laplacian + noise
00:03:05 Commencing optimization for 500 epochs, with 220376 positive edges
00:03:21 Optimization finished
[1] "165 0.06"00:03:21 UMAP embedding parameters a = 1.715 b = 0.8526
00:03:21 Read 1203 rows and found 38 numeric columns
00:03:21 Using Annoy for neighbor search, n_neighbors = 165
00:03:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:03:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872204db0a
00:03:22 Searching Annoy index using 1 thread, search_k = 16500
00:03:23 Annoy recall = 100%
00:03:34 Commencing smooth kNN distance calibration using 1 thread
00:03:57 Initializing from normalized Laplacian + noise
00:03:57 Commencing optimization for 500 epochs, with 220376 positive edges
00:04:12 Optimization finished
[1] "165 0.07"00:04:12 UMAP embedding parameters a = 1.68 b = 0.8631
00:04:12 Read 1203 rows and found 38 numeric columns
00:04:12 Using Annoy for neighbor search, n_neighbors = 165
00:04:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:04:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d7b9a0b
00:04:13 Searching Annoy index using 1 thread, search_k = 16500
00:04:14 Annoy recall = 100%
00:04:26 Commencing smooth kNN distance calibration using 1 thread
00:04:48 Initializing from normalized Laplacian + noise
00:04:48 Commencing optimization for 500 epochs, with 220376 positive edges
00:05:03 Optimization finished
[1] "165 0.08"00:05:04 UMAP embedding parameters a = 1.645 b = 0.8737
00:05:04 Read 1203 rows and found 38 numeric columns
00:05:04 Using Annoy for neighbor search, n_neighbors = 165
00:05:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:05:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728f4012
00:05:05 Searching Annoy index using 1 thread, search_k = 16500
00:05:06 Annoy recall = 100%
00:05:17 Commencing smooth kNN distance calibration using 1 thread
00:05:39 Initializing from normalized Laplacian + noise
00:05:39 Commencing optimization for 500 epochs, with 220376 positive edges
00:05:55 Optimization finished
[1] "165 0.09"00:05:55 UMAP embedding parameters a = 1.611 b = 0.8844
00:05:55 Read 1203 rows and found 38 numeric columns
00:05:55 Using Annoy for neighbor search, n_neighbors = 165
00:05:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:05:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87781e84ab
00:05:56 Searching Annoy index using 1 thread, search_k = 16500
00:05:57 Annoy recall = 100%
00:06:08 Commencing smooth kNN distance calibration using 1 thread
00:06:31 Initializing from normalized Laplacian + noise
00:06:31 Commencing optimization for 500 epochs, with 220376 positive edges
00:06:46 Optimization finished
[1] "165 0.1"00:06:46 UMAP embedding parameters a = 1.577 b = 0.8951
00:06:47 Read 1203 rows and found 38 numeric columns
00:06:47 Using Annoy for neighbor search, n_neighbors = 165
00:06:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:06:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757c8aee7
00:06:47 Searching Annoy index using 1 thread, search_k = 16500
00:06:48 Annoy recall = 100%
00:07:00 Commencing smooth kNN distance calibration using 1 thread
00:07:22 Initializing from normalized Laplacian + noise
00:07:22 Commencing optimization for 500 epochs, with 220376 positive edges
00:07:37 Optimization finished
[1] "165 0.11"00:07:38 UMAP embedding parameters a = 1.544 b = 0.9058
00:07:38 Read 1203 rows and found 38 numeric columns
00:07:38 Using Annoy for neighbor search, n_neighbors = 165
00:07:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:07:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876af4ba2f
00:07:39 Searching Annoy index using 1 thread, search_k = 16500
00:07:40 Annoy recall = 100%
00:07:51 Commencing smooth kNN distance calibration using 1 thread
00:08:13 Initializing from normalized Laplacian + noise
00:08:13 Commencing optimization for 500 epochs, with 220376 positive edges
00:08:29 Optimization finished
[1] "165 0.12"00:08:29 UMAP embedding parameters a = 1.51 b = 0.9165
00:08:29 Read 1203 rows and found 38 numeric columns
00:08:29 Using Annoy for neighbor search, n_neighbors = 165
00:08:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:08:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741d87198
00:08:30 Searching Annoy index using 1 thread, search_k = 16500
00:08:31 Annoy recall = 100%
00:08:42 Commencing smooth kNN distance calibration using 1 thread
00:09:05 Initializing from normalized Laplacian + noise
00:09:05 Commencing optimization for 500 epochs, with 220376 positive edges
00:09:20 Optimization finished
[1] "165 0.13"00:09:20 UMAP embedding parameters a = 1.478 b = 0.9272
00:09:20 Read 1203 rows and found 38 numeric columns
00:09:20 Using Annoy for neighbor search, n_neighbors = 165
00:09:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:09:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771beb6de
00:09:21 Searching Annoy index using 1 thread, search_k = 16500
00:09:22 Annoy recall = 100%
00:09:33 Commencing smooth kNN distance calibration using 1 thread
00:09:56 Initializing from normalized Laplacian + noise
00:09:56 Commencing optimization for 500 epochs, with 220376 positive edges
00:10:12 Optimization finished
[1] "165 0.14"00:10:12 UMAP embedding parameters a = 1.446 b = 0.938
00:10:12 Read 1203 rows and found 38 numeric columns
00:10:12 Using Annoy for neighbor search, n_neighbors = 165
00:10:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:10:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871dba8b74
00:10:13 Searching Annoy index using 1 thread, search_k = 16500
00:10:14 Annoy recall = 100%
00:10:25 Commencing smooth kNN distance calibration using 1 thread
00:10:47 Initializing from normalized Laplacian + noise
00:10:47 Commencing optimization for 500 epochs, with 220376 positive edges
00:11:03 Optimization finished
[1] "165 0.15"00:11:03 UMAP embedding parameters a = 1.414 b = 0.9488
00:11:03 Read 1203 rows and found 38 numeric columns
00:11:03 Using Annoy for neighbor search, n_neighbors = 165
00:11:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:11:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a492b0d
00:11:04 Searching Annoy index using 1 thread, search_k = 16500
00:11:05 Annoy recall = 100%
00:11:16 Commencing smooth kNN distance calibration using 1 thread
00:11:39 Initializing from normalized Laplacian + noise
00:11:39 Commencing optimization for 500 epochs, with 220376 positive edges
00:11:54 Optimization finished
[1] "165 0.16"00:11:55 UMAP embedding parameters a = 1.383 b = 0.9596
00:11:55 Read 1203 rows and found 38 numeric columns
00:11:55 Using Annoy for neighbor search, n_neighbors = 165
00:11:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:11:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771c35031
00:11:55 Searching Annoy index using 1 thread, search_k = 16500
00:11:57 Annoy recall = 100%
00:12:08 Commencing smooth kNN distance calibration using 1 thread
00:12:30 Initializing from normalized Laplacian + noise
00:12:30 Commencing optimization for 500 epochs, with 220376 positive edges
00:12:46 Optimization finished
[1] "165 0.17"00:12:46 UMAP embedding parameters a = 1.352 b = 0.9704
00:12:46 Read 1203 rows and found 38 numeric columns
00:12:46 Using Annoy for neighbor search, n_neighbors = 165
00:12:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:12:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87647e0e93
00:12:47 Searching Annoy index using 1 thread, search_k = 16500
00:12:48 Annoy recall = 100%
00:12:59 Commencing smooth kNN distance calibration using 1 thread
00:13:22 Initializing from normalized Laplacian + noise
00:13:22 Commencing optimization for 500 epochs, with 220376 positive edges
00:13:37 Optimization finished
[1] "165 0.18"00:13:37 UMAP embedding parameters a = 1.321 b = 0.9813
00:13:37 Read 1203 rows and found 38 numeric columns
00:13:37 Using Annoy for neighbor search, n_neighbors = 165
00:13:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:13:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c5e5361
00:13:38 Searching Annoy index using 1 thread, search_k = 16500
00:13:39 Annoy recall = 100%
00:13:50 Commencing smooth kNN distance calibration using 1 thread
00:14:13 Initializing from normalized Laplacian + noise
00:14:13 Commencing optimization for 500 epochs, with 220376 positive edges
00:14:29 Optimization finished
[1] "165 0.19"00:14:29 UMAP embedding parameters a = 1.292 b = 0.9921
00:14:29 Read 1203 rows and found 38 numeric columns
00:14:29 Using Annoy for neighbor search, n_neighbors = 165
00:14:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:14:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87527542a8
00:14:30 Searching Annoy index using 1 thread, search_k = 16500
00:14:31 Annoy recall = 100%
00:14:42 Commencing smooth kNN distance calibration using 1 thread
00:15:04 Initializing from normalized Laplacian + noise
00:15:04 Commencing optimization for 500 epochs, with 220376 positive edges
00:15:20 Optimization finished
[1] "165 0.2"00:15:20 UMAP embedding parameters a = 1.262 b = 1.003
00:15:20 Read 1203 rows and found 38 numeric columns
00:15:20 Using Annoy for neighbor search, n_neighbors = 165
00:15:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:15:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755586092
00:15:21 Searching Annoy index using 1 thread, search_k = 16500
00:15:22 Annoy recall = 100%
00:15:33 Commencing smooth kNN distance calibration using 1 thread
00:15:56 Initializing from normalized Laplacian + noise
00:15:56 Commencing optimization for 500 epochs, with 220376 positive edges
00:16:11 Optimization finished
[1] "166 0"00:16:12 UMAP embedding parameters a = 1.933 b = 0.7905
00:16:12 Read 1203 rows and found 38 numeric columns
00:16:12 Using Annoy for neighbor search, n_neighbors = 166
00:16:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:16:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87242089f5
00:16:13 Searching Annoy index using 1 thread, search_k = 16600
00:16:14 Annoy recall = 100%
00:16:25 Commencing smooth kNN distance calibration using 1 thread
00:16:47 Initializing from normalized Laplacian + noise
00:16:47 Commencing optimization for 500 epochs, with 221596 positive edges
00:17:03 Optimization finished
[1] "166 0.01"00:17:03 UMAP embedding parameters a = 1.896 b = 0.8006
00:17:03 Read 1203 rows and found 38 numeric columns
00:17:03 Using Annoy for neighbor search, n_neighbors = 166
00:17:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:17:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876057d7c2
00:17:04 Searching Annoy index using 1 thread, search_k = 16600
00:17:05 Annoy recall = 100%
00:17:16 Commencing smooth kNN distance calibration using 1 thread
00:17:39 Initializing from normalized Laplacian + noise
00:17:39 Commencing optimization for 500 epochs, with 221596 positive edges
00:17:54 Optimization finished
[1] "166 0.02"00:17:55 UMAP embedding parameters a = 1.859 b = 0.8109
00:17:55 Read 1203 rows and found 38 numeric columns
00:17:55 Using Annoy for neighbor search, n_neighbors = 166
00:17:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:17:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a0cc812
00:17:55 Searching Annoy index using 1 thread, search_k = 16600
00:17:57 Annoy recall = 100%
00:18:08 Commencing smooth kNN distance calibration using 1 thread
00:18:30 Initializing from normalized Laplacian + noise
00:18:31 Commencing optimization for 500 epochs, with 221596 positive edges
00:18:46 Optimization finished
[1] "166 0.03"00:18:46 UMAP embedding parameters a = 1.822 b = 0.8212
00:18:46 Read 1203 rows and found 38 numeric columns
00:18:46 Using Annoy for neighbor search, n_neighbors = 166
00:18:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:18:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723220aae
00:18:47 Searching Annoy index using 1 thread, search_k = 16600
00:18:48 Annoy recall = 100%
00:18:59 Commencing smooth kNN distance calibration using 1 thread
00:19:22 Initializing from normalized Laplacian + noise
00:19:22 Commencing optimization for 500 epochs, with 221596 positive edges
00:19:38 Optimization finished
[1] "166 0.04"00:19:38 UMAP embedding parameters a = 1.786 b = 0.8316
00:19:38 Read 1203 rows and found 38 numeric columns
00:19:38 Using Annoy for neighbor search, n_neighbors = 166
00:19:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:19:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872aa3d274
00:19:39 Searching Annoy index using 1 thread, search_k = 16600
00:19:40 Annoy recall = 100%
00:19:51 Commencing smooth kNN distance calibration using 1 thread
00:20:13 Initializing from normalized Laplacian + noise
00:20:13 Commencing optimization for 500 epochs, with 221596 positive edges
00:20:29 Optimization finished
[1] "166 0.05"00:20:29 UMAP embedding parameters a = 1.75 b = 0.8421
00:20:29 Read 1203 rows and found 38 numeric columns
00:20:29 Using Annoy for neighbor search, n_neighbors = 166
00:20:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:20:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722805a32
00:20:30 Searching Annoy index using 1 thread, search_k = 16600
00:20:31 Annoy recall = 100%
00:20:42 Commencing smooth kNN distance calibration using 1 thread
00:21:05 Initializing from normalized Laplacian + noise
00:21:05 Commencing optimization for 500 epochs, with 221596 positive edges
00:21:20 Optimization finished
[1] "166 0.06"00:21:21 UMAP embedding parameters a = 1.715 b = 0.8526
00:21:21 Read 1203 rows and found 38 numeric columns
00:21:21 Using Annoy for neighbor search, n_neighbors = 166
00:21:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:21:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871602bc1e
00:21:22 Searching Annoy index using 1 thread, search_k = 16600
00:21:23 Annoy recall = 100%
00:21:34 Commencing smooth kNN distance calibration using 1 thread
00:21:57 Initializing from normalized Laplacian + noise
00:21:57 Commencing optimization for 500 epochs, with 221596 positive edges
00:22:12 Optimization finished
[1] "166 0.07"00:22:12 UMAP embedding parameters a = 1.68 b = 0.8631
00:22:12 Read 1203 rows and found 38 numeric columns
00:22:12 Using Annoy for neighbor search, n_neighbors = 166
00:22:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:22:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ded70bd
00:22:13 Searching Annoy index using 1 thread, search_k = 16600
00:22:14 Annoy recall = 100%
00:22:25 Commencing smooth kNN distance calibration using 1 thread
00:22:48 Initializing from normalized Laplacian + noise
00:22:48 Commencing optimization for 500 epochs, with 221596 positive edges
00:23:04 Optimization finished
[1] "166 0.08"00:23:04 UMAP embedding parameters a = 1.645 b = 0.8737
00:23:04 Read 1203 rows and found 38 numeric columns
00:23:04 Using Annoy for neighbor search, n_neighbors = 166
00:23:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:23:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87345492f7
00:23:05 Searching Annoy index using 1 thread, search_k = 16600
00:23:06 Annoy recall = 100%
00:23:17 Commencing smooth kNN distance calibration using 1 thread
00:23:40 Initializing from normalized Laplacian + noise
00:23:40 Commencing optimization for 500 epochs, with 221596 positive edges
00:23:55 Optimization finished
[1] "166 0.09"00:23:56 UMAP embedding parameters a = 1.611 b = 0.8844
00:23:56 Read 1203 rows and found 38 numeric columns
00:23:56 Using Annoy for neighbor search, n_neighbors = 166
00:23:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:23:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765ddd866
00:23:56 Searching Annoy index using 1 thread, search_k = 16600
00:23:58 Annoy recall = 100%
00:24:09 Commencing smooth kNN distance calibration using 1 thread
00:24:31 Initializing from normalized Laplacian + noise
00:24:31 Commencing optimization for 500 epochs, with 221596 positive edges
00:24:47 Optimization finished
[1] "166 0.1"00:24:47 UMAP embedding parameters a = 1.577 b = 0.8951
00:24:47 Read 1203 rows and found 38 numeric columns
00:24:47 Using Annoy for neighbor search, n_neighbors = 166
00:24:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:24:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877dca0cb8
00:24:48 Searching Annoy index using 1 thread, search_k = 16600
00:24:49 Annoy recall = 100%
00:25:00 Commencing smooth kNN distance calibration using 1 thread
00:25:23 Initializing from normalized Laplacian + noise
00:25:23 Commencing optimization for 500 epochs, with 221596 positive edges
00:25:38 Optimization finished
[1] "166 0.11"00:25:39 UMAP embedding parameters a = 1.544 b = 0.9058
00:25:39 Read 1203 rows and found 38 numeric columns
00:25:39 Using Annoy for neighbor search, n_neighbors = 166
00:25:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d08cd90
00:25:39 Searching Annoy index using 1 thread, search_k = 16600
00:25:41 Annoy recall = 100%
00:25:52 Commencing smooth kNN distance calibration using 1 thread
00:26:15 Initializing from normalized Laplacian + noise
00:26:15 Commencing optimization for 500 epochs, with 221596 positive edges
00:26:30 Optimization finished
[1] "166 0.12"00:26:30 UMAP embedding parameters a = 1.51 b = 0.9165
00:26:30 Read 1203 rows and found 38 numeric columns
00:26:30 Using Annoy for neighbor search, n_neighbors = 166
00:26:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:26:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757a47016
00:26:31 Searching Annoy index using 1 thread, search_k = 16600
00:26:32 Annoy recall = 100%
00:26:44 Commencing smooth kNN distance calibration using 1 thread
00:27:06 Initializing from normalized Laplacian + noise
00:27:06 Commencing optimization for 500 epochs, with 221596 positive edges
00:27:22 Optimization finished
[1] "166 0.13"00:27:22 UMAP embedding parameters a = 1.478 b = 0.9272
00:27:22 Read 1203 rows and found 38 numeric columns
00:27:22 Using Annoy for neighbor search, n_neighbors = 166
00:27:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:27:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873374401
00:27:23 Searching Annoy index using 1 thread, search_k = 16600
00:27:24 Annoy recall = 100%
00:27:35 Commencing smooth kNN distance calibration using 1 thread
00:27:58 Initializing from normalized Laplacian + noise
00:27:58 Commencing optimization for 500 epochs, with 221596 positive edges
00:28:14 Optimization finished
[1] "166 0.14"00:28:14 UMAP embedding parameters a = 1.446 b = 0.938
00:28:14 Read 1203 rows and found 38 numeric columns
00:28:14 Using Annoy for neighbor search, n_neighbors = 166
00:28:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:28:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ac25033
00:28:15 Searching Annoy index using 1 thread, search_k = 16600
00:28:16 Annoy recall = 100%
00:28:27 Commencing smooth kNN distance calibration using 1 thread
00:28:49 Initializing from normalized Laplacian + noise
00:28:49 Commencing optimization for 500 epochs, with 221596 positive edges
00:29:05 Optimization finished
[1] "166 0.15"00:29:05 UMAP embedding parameters a = 1.414 b = 0.9488
00:29:05 Read 1203 rows and found 38 numeric columns
00:29:05 Using Annoy for neighbor search, n_neighbors = 166
00:29:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:29:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871aed5b9f
00:29:06 Searching Annoy index using 1 thread, search_k = 16600
00:29:07 Annoy recall = 100%
00:29:19 Commencing smooth kNN distance calibration using 1 thread
00:29:41 Initializing from normalized Laplacian + noise
00:29:41 Commencing optimization for 500 epochs, with 221596 positive edges
00:29:57 Optimization finished
[1] "166 0.16"00:29:57 UMAP embedding parameters a = 1.383 b = 0.9596
00:29:57 Read 1203 rows and found 38 numeric columns
00:29:57 Using Annoy for neighbor search, n_neighbors = 166
00:29:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:29:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87253c1f0b
00:29:58 Searching Annoy index using 1 thread, search_k = 16600
00:29:59 Annoy recall = 100%
00:30:10 Commencing smooth kNN distance calibration using 1 thread
00:30:33 Initializing from normalized Laplacian + noise
00:30:33 Commencing optimization for 500 epochs, with 221596 positive edges
00:30:48 Optimization finished
[1] "166 0.17"00:30:49 UMAP embedding parameters a = 1.352 b = 0.9704
00:30:49 Read 1203 rows and found 38 numeric columns
00:30:49 Using Annoy for neighbor search, n_neighbors = 166
00:30:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:30:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87783dea3f
00:30:49 Searching Annoy index using 1 thread, search_k = 16600
00:30:51 Annoy recall = 100%
00:31:02 Commencing smooth kNN distance calibration using 1 thread
00:31:25 Initializing from normalized Laplacian + noise
00:31:25 Commencing optimization for 500 epochs, with 221596 positive edges
00:31:40 Optimization finished
[1] "166 0.18"00:31:40 UMAP embedding parameters a = 1.321 b = 0.9813
00:31:40 Read 1203 rows and found 38 numeric columns
00:31:40 Using Annoy for neighbor search, n_neighbors = 166
00:31:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:31:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d7c9bb2
00:31:41 Searching Annoy index using 1 thread, search_k = 16600
00:31:42 Annoy recall = 100%
00:31:54 Commencing smooth kNN distance calibration using 1 thread
00:32:16 Initializing from normalized Laplacian + noise
00:32:16 Commencing optimization for 500 epochs, with 221596 positive edges
00:32:32 Optimization finished
[1] "166 0.19"00:32:32 UMAP embedding parameters a = 1.292 b = 0.9921
00:32:32 Read 1203 rows and found 38 numeric columns
00:32:32 Using Annoy for neighbor search, n_neighbors = 166
00:32:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:32:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d5aa3b6
00:32:33 Searching Annoy index using 1 thread, search_k = 16600
00:32:34 Annoy recall = 100%
00:32:45 Commencing smooth kNN distance calibration using 1 thread
00:33:08 Initializing from normalized Laplacian + noise
00:33:08 Commencing optimization for 500 epochs, with 221596 positive edges
00:33:24 Optimization finished
[1] "166 0.2"00:33:24 UMAP embedding parameters a = 1.262 b = 1.003
00:33:24 Read 1203 rows and found 38 numeric columns
00:33:24 Using Annoy for neighbor search, n_neighbors = 166
00:33:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:33:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750069926
00:33:25 Searching Annoy index using 1 thread, search_k = 16600
00:33:26 Annoy recall = 100%
00:33:37 Commencing smooth kNN distance calibration using 1 thread
00:34:00 Initializing from normalized Laplacian + noise
00:34:00 Commencing optimization for 500 epochs, with 221596 positive edges
00:34:15 Optimization finished
[1] "167 0"00:34:16 UMAP embedding parameters a = 1.933 b = 0.7905
00:34:16 Read 1203 rows and found 38 numeric columns
00:34:16 Using Annoy for neighbor search, n_neighbors = 167
00:34:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:34:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8787155e1
00:34:16 Searching Annoy index using 1 thread, search_k = 16700
00:34:18 Annoy recall = 100%
00:34:29 Commencing smooth kNN distance calibration using 1 thread
00:34:52 Initializing from normalized Laplacian + noise
00:34:52 Commencing optimization for 500 epochs, with 222798 positive edges
00:35:07 Optimization finished
[1] "167 0.01"00:35:07 UMAP embedding parameters a = 1.896 b = 0.8006
00:35:07 Read 1203 rows and found 38 numeric columns
00:35:07 Using Annoy for neighbor search, n_neighbors = 167
00:35:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:35:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f33154e
00:35:08 Searching Annoy index using 1 thread, search_k = 16700
00:35:09 Annoy recall = 100%
00:35:21 Commencing smooth kNN distance calibration using 1 thread
00:35:43 Initializing from normalized Laplacian + noise
00:35:43 Commencing optimization for 500 epochs, with 222798 positive edges
00:35:59 Optimization finished
[1] "167 0.02"00:35:59 UMAP embedding parameters a = 1.859 b = 0.8109
00:35:59 Read 1203 rows and found 38 numeric columns
00:35:59 Using Annoy for neighbor search, n_neighbors = 167
00:35:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:36:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8741c55004
00:36:00 Searching Annoy index using 1 thread, search_k = 16700
00:36:01 Annoy recall = 100%
00:36:12 Commencing smooth kNN distance calibration using 1 thread
00:36:35 Initializing from normalized Laplacian + noise
00:36:35 Commencing optimization for 500 epochs, with 222798 positive edges
00:36:51 Optimization finished
[1] "167 0.03"00:36:51 UMAP embedding parameters a = 1.822 b = 0.8212
00:36:51 Read 1203 rows and found 38 numeric columns
00:36:51 Using Annoy for neighbor search, n_neighbors = 167
00:36:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:36:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87262be155
00:36:52 Searching Annoy index using 1 thread, search_k = 16700
00:36:53 Annoy recall = 100%
00:37:04 Commencing smooth kNN distance calibration using 1 thread
00:37:27 Initializing from normalized Laplacian + noise
00:37:27 Commencing optimization for 500 epochs, with 222798 positive edges
00:37:43 Optimization finished
[1] "167 0.04"00:37:43 UMAP embedding parameters a = 1.786 b = 0.8316
00:37:43 Read 1203 rows and found 38 numeric columns
00:37:43 Using Annoy for neighbor search, n_neighbors = 167
00:37:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:37:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87697c405b
00:37:44 Searching Annoy index using 1 thread, search_k = 16700
00:37:45 Annoy recall = 100%
00:37:56 Commencing smooth kNN distance calibration using 1 thread
00:38:19 Initializing from normalized Laplacian + noise
00:38:19 Commencing optimization for 500 epochs, with 222798 positive edges
00:38:34 Optimization finished
[1] "167 0.05"00:38:35 UMAP embedding parameters a = 1.75 b = 0.8421
00:38:35 Read 1203 rows and found 38 numeric columns
00:38:35 Using Annoy for neighbor search, n_neighbors = 167
00:38:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:38:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873388a035
00:38:35 Searching Annoy index using 1 thread, search_k = 16700
00:38:37 Annoy recall = 100%
00:38:48 Commencing smooth kNN distance calibration using 1 thread
00:39:11 Initializing from normalized Laplacian + noise
00:39:11 Commencing optimization for 500 epochs, with 222798 positive edges
00:39:26 Optimization finished
[1] "167 0.06"00:39:26 UMAP embedding parameters a = 1.715 b = 0.8526
00:39:26 Read 1203 rows and found 38 numeric columns
00:39:27 Using Annoy for neighbor search, n_neighbors = 167
00:39:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:39:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87aa9efe8
00:39:27 Searching Annoy index using 1 thread, search_k = 16700
00:39:28 Annoy recall = 100%
00:39:40 Commencing smooth kNN distance calibration using 1 thread
00:40:03 Initializing from normalized Laplacian + noise
00:40:03 Commencing optimization for 500 epochs, with 222798 positive edges
00:40:18 Optimization finished
[1] "167 0.07"00:40:18 UMAP embedding parameters a = 1.68 b = 0.8631
00:40:18 Read 1203 rows and found 38 numeric columns
00:40:18 Using Annoy for neighbor search, n_neighbors = 167
00:40:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:40:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8745da93bd
00:40:19 Searching Annoy index using 1 thread, search_k = 16700
00:40:20 Annoy recall = 100%
00:40:32 Commencing smooth kNN distance calibration using 1 thread
00:40:54 Initializing from normalized Laplacian + noise
00:40:55 Commencing optimization for 500 epochs, with 222798 positive edges
00:41:10 Optimization finished
[1] "167 0.08"00:41:11 UMAP embedding parameters a = 1.645 b = 0.8737
00:41:11 Read 1203 rows and found 38 numeric columns
00:41:11 Using Annoy for neighbor search, n_neighbors = 167
00:41:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:41:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fde2dd
00:41:11 Searching Annoy index using 1 thread, search_k = 16700
00:41:13 Annoy recall = 100%
00:41:24 Commencing smooth kNN distance calibration using 1 thread
00:41:46 Initializing from normalized Laplacian + noise
00:41:46 Commencing optimization for 500 epochs, with 222798 positive edges
00:42:02 Optimization finished
[1] "167 0.09"00:42:02 UMAP embedding parameters a = 1.611 b = 0.8844
00:42:02 Read 1203 rows and found 38 numeric columns
00:42:02 Using Annoy for neighbor search, n_neighbors = 167
00:42:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:42:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876002507a
00:42:03 Searching Annoy index using 1 thread, search_k = 16700
00:42:04 Annoy recall = 100%
00:42:16 Commencing smooth kNN distance calibration using 1 thread
00:42:38 Initializing from normalized Laplacian + noise
00:42:38 Commencing optimization for 500 epochs, with 222798 positive edges
00:42:54 Optimization finished
[1] "167 0.1"00:42:54 UMAP embedding parameters a = 1.577 b = 0.8951
00:42:54 Read 1203 rows and found 38 numeric columns
00:42:54 Using Annoy for neighbor search, n_neighbors = 167
00:42:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:42:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769fb1db2
00:42:55 Searching Annoy index using 1 thread, search_k = 16700
00:42:56 Annoy recall = 100%
00:43:08 Commencing smooth kNN distance calibration using 1 thread
00:43:30 Initializing from normalized Laplacian + noise
00:43:30 Commencing optimization for 500 epochs, with 222798 positive edges
00:43:46 Optimization finished
[1] "167 0.11"00:43:46 UMAP embedding parameters a = 1.544 b = 0.9058
00:43:46 Read 1203 rows and found 38 numeric columns
00:43:46 Using Annoy for neighbor search, n_neighbors = 167
00:43:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:43:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876655ba9f
00:43:47 Searching Annoy index using 1 thread, search_k = 16700
00:43:48 Annoy recall = 100%
00:43:59 Commencing smooth kNN distance calibration using 1 thread
00:44:22 Initializing from normalized Laplacian + noise
00:44:22 Commencing optimization for 500 epochs, with 222798 positive edges
00:44:38 Optimization finished
[1] "167 0.12"00:44:38 UMAP embedding parameters a = 1.51 b = 0.9165
00:44:38 Read 1203 rows and found 38 numeric columns
00:44:38 Using Annoy for neighbor search, n_neighbors = 167
00:44:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:44:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a0f188c
00:44:39 Searching Annoy index using 1 thread, search_k = 16700
00:44:40 Annoy recall = 100%
00:44:51 Commencing smooth kNN distance calibration using 1 thread
00:45:14 Initializing from normalized Laplacian + noise
00:45:14 Commencing optimization for 500 epochs, with 222798 positive edges
00:45:30 Optimization finished
[1] "167 0.13"00:45:30 UMAP embedding parameters a = 1.478 b = 0.9272
00:45:30 Read 1203 rows and found 38 numeric columns
00:45:30 Using Annoy for neighbor search, n_neighbors = 167
00:45:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:45:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d1d2861
00:45:31 Searching Annoy index using 1 thread, search_k = 16700
00:45:32 Annoy recall = 100%
00:45:43 Commencing smooth kNN distance calibration using 1 thread
00:46:06 Initializing from normalized Laplacian + noise
00:46:06 Commencing optimization for 500 epochs, with 222798 positive edges
00:46:22 Optimization finished
[1] "167 0.14"00:46:22 UMAP embedding parameters a = 1.446 b = 0.938
00:46:22 Read 1203 rows and found 38 numeric columns
00:46:22 Using Annoy for neighbor search, n_neighbors = 167
00:46:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:46:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710f98d14
00:46:23 Searching Annoy index using 1 thread, search_k = 16700
00:46:24 Annoy recall = 100%
00:46:35 Commencing smooth kNN distance calibration using 1 thread
00:46:58 Initializing from normalized Laplacian + noise
00:46:58 Commencing optimization for 500 epochs, with 222798 positive edges
00:47:14 Optimization finished
[1] "167 0.15"00:47:14 UMAP embedding parameters a = 1.414 b = 0.9488
00:47:14 Read 1203 rows and found 38 numeric columns
00:47:14 Using Annoy for neighbor search, n_neighbors = 167
00:47:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:47:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c8f72bf
00:47:15 Searching Annoy index using 1 thread, search_k = 16700
00:47:16 Annoy recall = 100%
00:47:27 Commencing smooth kNN distance calibration using 1 thread
00:47:50 Initializing from normalized Laplacian + noise
00:47:50 Commencing optimization for 500 epochs, with 222798 positive edges
00:48:06 Optimization finished
[1] "167 0.16"00:48:06 UMAP embedding parameters a = 1.383 b = 0.9596
00:48:06 Read 1203 rows and found 38 numeric columns
00:48:06 Using Annoy for neighbor search, n_neighbors = 167
00:48:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87231fe47f
00:48:07 Searching Annoy index using 1 thread, search_k = 16700
00:48:08 Annoy recall = 100%
00:48:19 Commencing smooth kNN distance calibration using 1 thread
00:48:42 Initializing from normalized Laplacian + noise
00:48:42 Commencing optimization for 500 epochs, with 222798 positive edges
00:48:58 Optimization finished
[1] "167 0.17"00:48:58 UMAP embedding parameters a = 1.352 b = 0.9704
00:48:58 Read 1203 rows and found 38 numeric columns
00:48:58 Using Annoy for neighbor search, n_neighbors = 167
00:48:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:48:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ee6fdd1
00:48:59 Searching Annoy index using 1 thread, search_k = 16700
00:49:00 Annoy recall = 100%
00:49:11 Commencing smooth kNN distance calibration using 1 thread
00:49:34 Initializing from normalized Laplacian + noise
00:49:34 Commencing optimization for 500 epochs, with 222798 positive edges
00:49:50 Optimization finished
[1] "167 0.18"00:49:50 UMAP embedding parameters a = 1.321 b = 0.9813
00:49:50 Read 1203 rows and found 38 numeric columns
00:49:50 Using Annoy for neighbor search, n_neighbors = 167
00:49:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:49:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710e405b6
00:49:51 Searching Annoy index using 1 thread, search_k = 16700
00:49:52 Annoy recall = 100%
00:50:03 Commencing smooth kNN distance calibration using 1 thread
00:50:26 Initializing from normalized Laplacian + noise
00:50:26 Commencing optimization for 500 epochs, with 222798 positive edges
00:50:42 Optimization finished
[1] "167 0.19"00:50:42 UMAP embedding parameters a = 1.292 b = 0.9921
00:50:42 Read 1203 rows and found 38 numeric columns
00:50:42 Using Annoy for neighbor search, n_neighbors = 167
00:50:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:50:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878fdbce5
00:50:43 Searching Annoy index using 1 thread, search_k = 16700
00:50:44 Annoy recall = 100%
00:50:55 Commencing smooth kNN distance calibration using 1 thread
00:51:18 Initializing from normalized Laplacian + noise
00:51:18 Commencing optimization for 500 epochs, with 222798 positive edges
00:51:34 Optimization finished
[1] "167 0.2"00:51:34 UMAP embedding parameters a = 1.262 b = 1.003
00:51:34 Read 1203 rows and found 38 numeric columns
00:51:34 Using Annoy for neighbor search, n_neighbors = 167
00:51:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:51:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877cb10a8a
00:51:35 Searching Annoy index using 1 thread, search_k = 16700
00:51:36 Annoy recall = 100%
00:51:48 Commencing smooth kNN distance calibration using 1 thread
00:52:10 Initializing from normalized Laplacian + noise
00:52:10 Commencing optimization for 500 epochs, with 222798 positive edges
00:52:26 Optimization finished
[1] "168 0"00:52:26 UMAP embedding parameters a = 1.933 b = 0.7905
00:52:26 Read 1203 rows and found 38 numeric columns
00:52:26 Using Annoy for neighbor search, n_neighbors = 168
00:52:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:52:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874decd347
00:52:27 Searching Annoy index using 1 thread, search_k = 16800
00:52:28 Annoy recall = 100%
00:52:40 Commencing smooth kNN distance calibration using 1 thread
00:53:02 Initializing from normalized Laplacian + noise
00:53:02 Commencing optimization for 500 epochs, with 224004 positive edges
00:53:18 Optimization finished
[1] "168 0.01"00:53:18 UMAP embedding parameters a = 1.896 b = 0.8006
00:53:18 Read 1203 rows and found 38 numeric columns
00:53:18 Using Annoy for neighbor search, n_neighbors = 168
00:53:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:53:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760a22cfb
00:53:19 Searching Annoy index using 1 thread, search_k = 16800
00:53:20 Annoy recall = 100%
00:53:32 Commencing smooth kNN distance calibration using 1 thread
00:53:54 Initializing from normalized Laplacian + noise
00:53:55 Commencing optimization for 500 epochs, with 224004 positive edges
00:54:10 Optimization finished
[1] "168 0.02"00:54:10 UMAP embedding parameters a = 1.859 b = 0.8109
00:54:10 Read 1203 rows and found 38 numeric columns
00:54:10 Using Annoy for neighbor search, n_neighbors = 168
00:54:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:54:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877fe84e8b
00:54:11 Searching Annoy index using 1 thread, search_k = 16800
00:54:12 Annoy recall = 100%
00:54:24 Commencing smooth kNN distance calibration using 1 thread
00:54:47 Initializing from normalized Laplacian + noise
00:54:47 Commencing optimization for 500 epochs, with 224004 positive edges
00:55:02 Optimization finished
[1] "168 0.03"00:55:03 UMAP embedding parameters a = 1.822 b = 0.8212
00:55:03 Read 1203 rows and found 38 numeric columns
00:55:03 Using Annoy for neighbor search, n_neighbors = 168
00:55:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:55:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778af237a
00:55:03 Searching Annoy index using 1 thread, search_k = 16800
00:55:05 Annoy recall = 100%
00:55:16 Commencing smooth kNN distance calibration using 1 thread
00:55:39 Initializing from normalized Laplacian + noise
00:55:39 Commencing optimization for 500 epochs, with 224004 positive edges
00:55:54 Optimization finished
[1] "168 0.04"00:55:55 UMAP embedding parameters a = 1.786 b = 0.8316
00:55:55 Read 1203 rows and found 38 numeric columns
00:55:55 Using Annoy for neighbor search, n_neighbors = 168
00:55:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:55:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b8f889b
00:55:56 Searching Annoy index using 1 thread, search_k = 16800
00:55:57 Annoy recall = 100%
00:56:08 Commencing smooth kNN distance calibration using 1 thread
00:56:31 Initializing from normalized Laplacian + noise
00:56:31 Commencing optimization for 500 epochs, with 224004 positive edges
00:56:47 Optimization finished
[1] "168 0.05"00:56:47 UMAP embedding parameters a = 1.75 b = 0.8421
00:56:47 Read 1203 rows and found 38 numeric columns
00:56:47 Using Annoy for neighbor search, n_neighbors = 168
00:56:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:56:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725246d97
00:56:48 Searching Annoy index using 1 thread, search_k = 16800
00:56:49 Annoy recall = 100%
00:57:00 Commencing smooth kNN distance calibration using 1 thread
00:57:23 Initializing from normalized Laplacian + noise
00:57:23 Commencing optimization for 500 epochs, with 224004 positive edges
00:57:39 Optimization finished
[1] "168 0.06"00:57:39 UMAP embedding parameters a = 1.715 b = 0.8526
00:57:39 Read 1203 rows and found 38 numeric columns
00:57:39 Using Annoy for neighbor search, n_neighbors = 168
00:57:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:57:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770ed0db9
00:57:40 Searching Annoy index using 1 thread, search_k = 16800
00:57:41 Annoy recall = 100%
00:57:52 Commencing smooth kNN distance calibration using 1 thread
00:58:15 Initializing from normalized Laplacian + noise
00:58:15 Commencing optimization for 500 epochs, with 224004 positive edges
00:58:31 Optimization finished
[1] "168 0.07"00:58:31 UMAP embedding parameters a = 1.68 b = 0.8631
00:58:31 Read 1203 rows and found 38 numeric columns
00:58:31 Using Annoy for neighbor search, n_neighbors = 168
00:58:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:58:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87190c244d
00:58:32 Searching Annoy index using 1 thread, search_k = 16800
00:58:33 Annoy recall = 100%
00:58:45 Commencing smooth kNN distance calibration using 1 thread
00:59:07 Initializing from normalized Laplacian + noise
00:59:08 Commencing optimization for 500 epochs, with 224004 positive edges
00:59:23 Optimization finished
[1] "168 0.08"00:59:23 UMAP embedding parameters a = 1.645 b = 0.8737
00:59:23 Read 1203 rows and found 38 numeric columns
00:59:23 Using Annoy for neighbor search, n_neighbors = 168
00:59:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:59:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87427f114d
00:59:24 Searching Annoy index using 1 thread, search_k = 16800
00:59:25 Annoy recall = 100%
00:59:37 Commencing smooth kNN distance calibration using 1 thread
01:00:00 Initializing from normalized Laplacian + noise
01:00:00 Commencing optimization for 500 epochs, with 224004 positive edges
01:00:15 Optimization finished
[1] "168 0.09"01:00:16 UMAP embedding parameters a = 1.611 b = 0.8844
01:00:16 Read 1203 rows and found 38 numeric columns
01:00:16 Using Annoy for neighbor search, n_neighbors = 168
01:00:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:00:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740f3a6df
01:00:17 Searching Annoy index using 1 thread, search_k = 16800
01:00:18 Annoy recall = 100%
01:00:29 Commencing smooth kNN distance calibration using 1 thread
01:00:52 Initializing from normalized Laplacian + noise
01:00:52 Commencing optimization for 500 epochs, with 224004 positive edges
01:01:08 Optimization finished
[1] "168 0.1"01:01:08 UMAP embedding parameters a = 1.577 b = 0.8951
01:01:08 Read 1203 rows and found 38 numeric columns
01:01:08 Using Annoy for neighbor search, n_neighbors = 168
01:01:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:01:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87217d7a2e
01:01:09 Searching Annoy index using 1 thread, search_k = 16800
01:01:10 Annoy recall = 100%
01:01:21 Commencing smooth kNN distance calibration using 1 thread
01:01:44 Initializing from normalized Laplacian + noise
01:01:44 Commencing optimization for 500 epochs, with 224004 positive edges
01:02:00 Optimization finished
[1] "168 0.11"01:02:00 UMAP embedding parameters a = 1.544 b = 0.9058
01:02:00 Read 1203 rows and found 38 numeric columns
01:02:00 Using Annoy for neighbor search, n_neighbors = 168
01:02:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:02:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721b2269c
01:02:01 Searching Annoy index using 1 thread, search_k = 16800
01:02:02 Annoy recall = 100%
01:02:14 Commencing smooth kNN distance calibration using 1 thread
01:02:36 Initializing from normalized Laplacian + noise
01:02:36 Commencing optimization for 500 epochs, with 224004 positive edges
01:02:52 Optimization finished
[1] "168 0.12"01:02:52 UMAP embedding parameters a = 1.51 b = 0.9165
01:02:52 Read 1203 rows and found 38 numeric columns
01:02:52 Using Annoy for neighbor search, n_neighbors = 168
01:02:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:02:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b8f6e3
01:02:53 Searching Annoy index using 1 thread, search_k = 16800
01:02:54 Annoy recall = 100%
01:03:06 Commencing smooth kNN distance calibration using 1 thread
01:03:29 Initializing from normalized Laplacian + noise
01:03:29 Commencing optimization for 500 epochs, with 224004 positive edges
01:03:44 Optimization finished
[1] "168 0.13"01:03:45 UMAP embedding parameters a = 1.478 b = 0.9272
01:03:45 Read 1203 rows and found 38 numeric columns
01:03:45 Using Annoy for neighbor search, n_neighbors = 168
01:03:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:03:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747a95b83
01:03:45 Searching Annoy index using 1 thread, search_k = 16800
01:03:47 Annoy recall = 100%
01:03:58 Commencing smooth kNN distance calibration using 1 thread
01:04:21 Initializing from normalized Laplacian + noise
01:04:21 Commencing optimization for 500 epochs, with 224004 positive edges
01:04:37 Optimization finished
[1] "168 0.14"01:04:37 UMAP embedding parameters a = 1.446 b = 0.938
01:04:37 Read 1203 rows and found 38 numeric columns
01:04:37 Using Annoy for neighbor search, n_neighbors = 168
01:04:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:04:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b2e66f7
01:04:38 Searching Annoy index using 1 thread, search_k = 16800
01:04:39 Annoy recall = 100%
01:04:50 Commencing smooth kNN distance calibration using 1 thread
01:05:13 Initializing from normalized Laplacian + noise
01:05:13 Commencing optimization for 500 epochs, with 224004 positive edges
01:05:29 Optimization finished
[1] "168 0.15"01:05:29 UMAP embedding parameters a = 1.414 b = 0.9488
01:05:29 Read 1203 rows and found 38 numeric columns
01:05:29 Using Annoy for neighbor search, n_neighbors = 168
01:05:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:05:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736419718
01:05:30 Searching Annoy index using 1 thread, search_k = 16800
01:05:31 Annoy recall = 100%
01:05:43 Commencing smooth kNN distance calibration using 1 thread
01:06:05 Initializing from normalized Laplacian + noise
01:06:05 Commencing optimization for 500 epochs, with 224004 positive edges
01:06:21 Optimization finished
[1] "168 0.16"01:06:21 UMAP embedding parameters a = 1.383 b = 0.9596
01:06:21 Read 1203 rows and found 38 numeric columns
01:06:21 Using Annoy for neighbor search, n_neighbors = 168
01:06:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:06:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752534b6c
01:06:22 Searching Annoy index using 1 thread, search_k = 16800
01:06:23 Annoy recall = 100%
01:06:35 Commencing smooth kNN distance calibration using 1 thread
01:06:58 Initializing from normalized Laplacian + noise
01:06:58 Commencing optimization for 500 epochs, with 224004 positive edges
01:07:13 Optimization finished
[1] "168 0.17"01:07:14 UMAP embedding parameters a = 1.352 b = 0.9704
01:07:14 Read 1203 rows and found 38 numeric columns
01:07:14 Using Annoy for neighbor search, n_neighbors = 168
01:07:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:07:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875108fab4
01:07:15 Searching Annoy index using 1 thread, search_k = 16800
01:07:16 Annoy recall = 100%
01:07:27 Commencing smooth kNN distance calibration using 1 thread
01:07:50 Initializing from normalized Laplacian + noise
01:07:50 Commencing optimization for 500 epochs, with 224004 positive edges
01:08:06 Optimization finished
[1] "168 0.18"01:08:06 UMAP embedding parameters a = 1.321 b = 0.9813
01:08:06 Read 1203 rows and found 38 numeric columns
01:08:06 Using Annoy for neighbor search, n_neighbors = 168
01:08:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:08:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c3f79f6
01:08:07 Searching Annoy index using 1 thread, search_k = 16800
01:08:08 Annoy recall = 100%
01:08:19 Commencing smooth kNN distance calibration using 1 thread
01:08:42 Initializing from normalized Laplacian + noise
01:08:43 Commencing optimization for 500 epochs, with 224004 positive edges
01:08:58 Optimization finished
[1] "168 0.19"01:08:58 UMAP embedding parameters a = 1.292 b = 0.9921
01:08:58 Read 1203 rows and found 38 numeric columns
01:08:58 Using Annoy for neighbor search, n_neighbors = 168
01:08:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:08:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732559be6
01:08:59 Searching Annoy index using 1 thread, search_k = 16800
01:09:00 Annoy recall = 100%
01:09:12 Commencing smooth kNN distance calibration using 1 thread
01:09:35 Initializing from normalized Laplacian + noise
01:09:35 Commencing optimization for 500 epochs, with 224004 positive edges
01:09:51 Optimization finished
[1] "168 0.2"01:09:51 UMAP embedding parameters a = 1.262 b = 1.003
01:09:51 Read 1203 rows and found 38 numeric columns
01:09:51 Using Annoy for neighbor search, n_neighbors = 168
01:09:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:09:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b041867
01:09:52 Searching Annoy index using 1 thread, search_k = 16800
01:09:53 Annoy recall = 100%
01:10:04 Commencing smooth kNN distance calibration using 1 thread
01:10:27 Initializing from normalized Laplacian + noise
01:10:27 Commencing optimization for 500 epochs, with 224004 positive edges
01:10:43 Optimization finished
[1] "169 0"01:10:43 UMAP embedding parameters a = 1.933 b = 0.7905
01:10:43 Read 1203 rows and found 38 numeric columns
01:10:43 Using Annoy for neighbor search, n_neighbors = 169
01:10:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:10:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722953495
01:10:44 Searching Annoy index using 1 thread, search_k = 16900
01:10:45 Annoy recall = 100%
01:10:57 Commencing smooth kNN distance calibration using 1 thread
01:11:19 Initializing from normalized Laplacian + noise
01:11:19 Commencing optimization for 500 epochs, with 225222 positive edges
01:11:35 Optimization finished
[1] "169 0.01"01:11:35 UMAP embedding parameters a = 1.896 b = 0.8006
01:11:35 Read 1203 rows and found 38 numeric columns
01:11:35 Using Annoy for neighbor search, n_neighbors = 169
01:11:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:11:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c64b473
01:11:36 Searching Annoy index using 1 thread, search_k = 16900
01:11:37 Annoy recall = 100%
01:11:49 Commencing smooth kNN distance calibration using 1 thread
01:12:12 Initializing from normalized Laplacian + noise
01:12:12 Commencing optimization for 500 epochs, with 225222 positive edges
01:12:27 Optimization finished
[1] "169 0.02"01:12:28 UMAP embedding parameters a = 1.859 b = 0.8109
01:12:28 Read 1203 rows and found 38 numeric columns
01:12:28 Using Annoy for neighbor search, n_neighbors = 169
01:12:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:12:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87482140c8
01:12:29 Searching Annoy index using 1 thread, search_k = 16900
01:12:30 Annoy recall = 100%
01:12:41 Commencing smooth kNN distance calibration using 1 thread
01:13:04 Initializing from normalized Laplacian + noise
01:13:04 Commencing optimization for 500 epochs, with 225222 positive edges
01:13:20 Optimization finished
[1] "169 0.03"01:13:20 UMAP embedding parameters a = 1.822 b = 0.8212
01:13:20 Read 1203 rows and found 38 numeric columns
01:13:20 Using Annoy for neighbor search, n_neighbors = 169
01:13:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:13:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87338ec1a9
01:13:21 Searching Annoy index using 1 thread, search_k = 16900
01:13:22 Annoy recall = 100%
01:13:34 Commencing smooth kNN distance calibration using 1 thread
01:13:57 Initializing from normalized Laplacian + noise
01:13:57 Commencing optimization for 500 epochs, with 225222 positive edges
01:14:13 Optimization finished
[1] "169 0.04"01:14:13 UMAP embedding parameters a = 1.786 b = 0.8316
01:14:13 Read 1203 rows and found 38 numeric columns
01:14:13 Using Annoy for neighbor search, n_neighbors = 169
01:14:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:14:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748f42732
01:14:14 Searching Annoy index using 1 thread, search_k = 16900
01:14:15 Annoy recall = 100%
01:14:26 Commencing smooth kNN distance calibration using 1 thread
01:14:49 Initializing from normalized Laplacian + noise
01:14:49 Commencing optimization for 500 epochs, with 225222 positive edges
01:15:05 Optimization finished
[1] "169 0.05"01:15:05 UMAP embedding parameters a = 1.75 b = 0.8421
01:15:05 Read 1203 rows and found 38 numeric columns
01:15:05 Using Annoy for neighbor search, n_neighbors = 169
01:15:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:15:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b412547
01:15:06 Searching Annoy index using 1 thread, search_k = 16900
01:15:07 Annoy recall = 100%
01:15:19 Commencing smooth kNN distance calibration using 1 thread
01:15:41 Initializing from normalized Laplacian + noise
01:15:41 Commencing optimization for 500 epochs, with 225222 positive edges
01:15:57 Optimization finished
[1] "169 0.06"01:15:58 UMAP embedding parameters a = 1.715 b = 0.8526
01:15:58 Read 1203 rows and found 38 numeric columns
01:15:58 Using Annoy for neighbor search, n_neighbors = 169
01:15:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:15:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873275bf7b
01:15:58 Searching Annoy index using 1 thread, search_k = 16900
01:16:00 Annoy recall = 100%
01:16:11 Commencing smooth kNN distance calibration using 1 thread
01:16:34 Initializing from normalized Laplacian + noise
01:16:34 Commencing optimization for 500 epochs, with 225222 positive edges
01:16:50 Optimization finished
[1] "169 0.07"01:16:50 UMAP embedding parameters a = 1.68 b = 0.8631
01:16:50 Read 1203 rows and found 38 numeric columns
01:16:50 Using Annoy for neighbor search, n_neighbors = 169
01:16:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:16:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759d82ce8
01:16:51 Searching Annoy index using 1 thread, search_k = 16900
01:16:52 Annoy recall = 100%
01:17:04 Commencing smooth kNN distance calibration using 1 thread
01:17:27 Initializing from normalized Laplacian + noise
01:17:27 Commencing optimization for 500 epochs, with 225222 positive edges
01:17:42 Optimization finished
[1] "169 0.08"01:17:42 UMAP embedding parameters a = 1.645 b = 0.8737
01:17:42 Read 1203 rows and found 38 numeric columns
01:17:42 Using Annoy for neighbor search, n_neighbors = 169
01:17:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:17:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87743ee22c
01:17:43 Searching Annoy index using 1 thread, search_k = 16900
01:17:44 Annoy recall = 100%
01:17:56 Commencing smooth kNN distance calibration using 1 thread
01:18:19 Initializing from normalized Laplacian + noise
01:18:19 Commencing optimization for 500 epochs, with 225222 positive edges
01:18:35 Optimization finished
[1] "169 0.09"01:18:35 UMAP embedding parameters a = 1.611 b = 0.8844
01:18:35 Read 1203 rows and found 38 numeric columns
01:18:35 Using Annoy for neighbor search, n_neighbors = 169
01:18:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:18:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f26ca05
01:18:36 Searching Annoy index using 1 thread, search_k = 16900
01:18:37 Annoy recall = 100%
01:18:48 Commencing smooth kNN distance calibration using 1 thread
01:19:11 Initializing from normalized Laplacian + noise
01:19:12 Commencing optimization for 500 epochs, with 225222 positive edges
01:19:27 Optimization finished
[1] "169 0.1"01:19:28 UMAP embedding parameters a = 1.577 b = 0.8951
01:19:28 Read 1203 rows and found 38 numeric columns
01:19:28 Using Annoy for neighbor search, n_neighbors = 169
01:19:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:19:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727c5002f
01:19:29 Searching Annoy index using 1 thread, search_k = 16900
01:19:30 Annoy recall = 100%
01:19:41 Commencing smooth kNN distance calibration using 1 thread
01:20:04 Initializing from normalized Laplacian + noise
01:20:04 Commencing optimization for 500 epochs, with 225222 positive edges
01:20:20 Optimization finished
[1] "169 0.11"01:20:20 UMAP embedding parameters a = 1.544 b = 0.9058
01:20:20 Read 1203 rows and found 38 numeric columns
01:20:20 Using Annoy for neighbor search, n_neighbors = 169
01:20:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:20:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754e10f27
01:20:21 Searching Annoy index using 1 thread, search_k = 16900
01:20:22 Annoy recall = 100%
01:20:34 Commencing smooth kNN distance calibration using 1 thread
01:20:56 Initializing from normalized Laplacian + noise
01:20:57 Commencing optimization for 500 epochs, with 225222 positive edges
01:21:12 Optimization finished
[1] "169 0.12"01:21:13 UMAP embedding parameters a = 1.51 b = 0.9165
01:21:13 Read 1203 rows and found 38 numeric columns
01:21:13 Using Annoy for neighbor search, n_neighbors = 169
01:21:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:21:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f0f1890
01:21:14 Searching Annoy index using 1 thread, search_k = 16900
01:21:15 Annoy recall = 100%
01:21:26 Commencing smooth kNN distance calibration using 1 thread
01:21:49 Initializing from normalized Laplacian + noise
01:21:49 Commencing optimization for 500 epochs, with 225222 positive edges
01:22:05 Optimization finished
[1] "169 0.13"01:22:05 UMAP embedding parameters a = 1.478 b = 0.9272
01:22:05 Read 1203 rows and found 38 numeric columns
01:22:05 Using Annoy for neighbor search, n_neighbors = 169
01:22:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:22:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87207423aa
01:22:06 Searching Annoy index using 1 thread, search_k = 16900
01:22:07 Annoy recall = 100%
01:22:19 Commencing smooth kNN distance calibration using 1 thread
01:22:42 Initializing from normalized Laplacian + noise
01:22:42 Commencing optimization for 500 epochs, with 225222 positive edges
01:22:57 Optimization finished
[1] "169 0.14"01:22:58 UMAP embedding parameters a = 1.446 b = 0.938
01:22:58 Read 1203 rows and found 38 numeric columns
01:22:58 Using Annoy for neighbor search, n_neighbors = 169
01:22:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:22:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87507097c2
01:22:59 Searching Annoy index using 1 thread, search_k = 16900
01:23:00 Annoy recall = 100%
01:23:11 Commencing smooth kNN distance calibration using 1 thread
01:23:34 Initializing from normalized Laplacian + noise
01:23:34 Commencing optimization for 500 epochs, with 225222 positive edges
01:23:50 Optimization finished
[1] "169 0.15"01:23:50 UMAP embedding parameters a = 1.414 b = 0.9488
01:23:50 Read 1203 rows and found 38 numeric columns
01:23:51 Using Annoy for neighbor search, n_neighbors = 169
01:23:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:23:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754338627
01:23:51 Searching Annoy index using 1 thread, search_k = 16900
01:23:53 Annoy recall = 100%
01:24:04 Commencing smooth kNN distance calibration using 1 thread
01:24:27 Initializing from normalized Laplacian + noise
01:24:27 Commencing optimization for 500 epochs, with 225222 positive edges
01:24:43 Optimization finished
[1] "169 0.16"01:24:43 UMAP embedding parameters a = 1.383 b = 0.9596
01:24:43 Read 1203 rows and found 38 numeric columns
01:24:43 Using Annoy for neighbor search, n_neighbors = 169
01:24:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:24:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711613163
01:24:44 Searching Annoy index using 1 thread, search_k = 16900
01:24:45 Annoy recall = 100%
01:24:56 Commencing smooth kNN distance calibration using 1 thread
01:25:19 Initializing from normalized Laplacian + noise
01:25:19 Commencing optimization for 500 epochs, with 225222 positive edges
01:25:35 Optimization finished
[1] "169 0.17"01:25:36 UMAP embedding parameters a = 1.352 b = 0.9704
01:25:36 Read 1203 rows and found 38 numeric columns
01:25:36 Using Annoy for neighbor search, n_neighbors = 169
01:25:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:25:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87697cbc0f
01:25:36 Searching Annoy index using 1 thread, search_k = 16900
01:25:38 Annoy recall = 100%
01:25:49 Commencing smooth kNN distance calibration using 1 thread
01:26:12 Initializing from normalized Laplacian + noise
01:26:12 Commencing optimization for 500 epochs, with 225222 positive edges
01:26:28 Optimization finished
[1] "169 0.18"01:26:28 UMAP embedding parameters a = 1.321 b = 0.9813
01:26:28 Read 1203 rows and found 38 numeric columns
01:26:28 Using Annoy for neighbor search, n_neighbors = 169
01:26:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:26:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716b29775
01:26:29 Searching Annoy index using 1 thread, search_k = 16900
01:26:30 Annoy recall = 100%
01:26:42 Commencing smooth kNN distance calibration using 1 thread
01:27:05 Initializing from normalized Laplacian + noise
01:27:05 Commencing optimization for 500 epochs, with 225222 positive edges
01:27:20 Optimization finished
[1] "169 0.19"01:27:21 UMAP embedding parameters a = 1.292 b = 0.9921
01:27:21 Read 1203 rows and found 38 numeric columns
01:27:21 Using Annoy for neighbor search, n_neighbors = 169
01:27:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:27:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875254d843
01:27:22 Searching Annoy index using 1 thread, search_k = 16900
01:27:23 Annoy recall = 100%
01:27:34 Commencing smooth kNN distance calibration using 1 thread
01:27:57 Initializing from normalized Laplacian + noise
01:27:57 Commencing optimization for 500 epochs, with 225222 positive edges
01:28:13 Optimization finished
[1] "169 0.2"01:28:13 UMAP embedding parameters a = 1.262 b = 1.003
01:28:13 Read 1203 rows and found 38 numeric columns
01:28:13 Using Annoy for neighbor search, n_neighbors = 169
01:28:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:28:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87afa363d
01:28:14 Searching Annoy index using 1 thread, search_k = 16900
01:28:15 Annoy recall = 100%
01:28:27 Commencing smooth kNN distance calibration using 1 thread
01:28:50 Initializing from normalized Laplacian + noise
01:28:50 Commencing optimization for 500 epochs, with 225222 positive edges
01:29:06 Optimization finished
[1] "170 0"01:29:06 UMAP embedding parameters a = 1.933 b = 0.7905
01:29:06 Read 1203 rows and found 38 numeric columns
01:29:06 Using Annoy for neighbor search, n_neighbors = 170
01:29:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:29:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873864be11
01:29:07 Searching Annoy index using 1 thread, search_k = 17000
01:29:08 Annoy recall = 100%
01:29:19 Commencing smooth kNN distance calibration using 1 thread
01:29:43 Initializing from normalized Laplacian + noise
01:29:43 Commencing optimization for 500 epochs, with 226434 positive edges
01:29:59 Optimization finished
[1] "170 0.01"01:29:59 UMAP embedding parameters a = 1.896 b = 0.8006
01:29:59 Read 1203 rows and found 38 numeric columns
01:29:59 Using Annoy for neighbor search, n_neighbors = 170
01:29:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:30:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87550dcf26
01:30:00 Searching Annoy index using 1 thread, search_k = 17000
01:30:01 Annoy recall = 100%
01:30:12 Commencing smooth kNN distance calibration using 1 thread
01:30:35 Initializing from normalized Laplacian + noise
01:30:35 Commencing optimization for 500 epochs, with 226434 positive edges
01:30:51 Optimization finished
[1] "170 0.02"01:30:51 UMAP embedding parameters a = 1.859 b = 0.8109
01:30:51 Read 1203 rows and found 38 numeric columns
01:30:51 Using Annoy for neighbor search, n_neighbors = 170
01:30:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:30:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8752a391c1
01:30:52 Searching Annoy index using 1 thread, search_k = 17000
01:30:53 Annoy recall = 100%
01:31:05 Commencing smooth kNN distance calibration using 1 thread
01:31:28 Initializing from normalized Laplacian + noise
01:31:28 Commencing optimization for 500 epochs, with 226434 positive edges
01:31:44 Optimization finished
[1] "170 0.03"01:31:44 UMAP embedding parameters a = 1.822 b = 0.8212
01:31:44 Read 1203 rows and found 38 numeric columns
01:31:44 Using Annoy for neighbor search, n_neighbors = 170
01:31:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:31:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743932508
01:31:45 Searching Annoy index using 1 thread, search_k = 17000
01:31:46 Annoy recall = 100%
01:31:58 Commencing smooth kNN distance calibration using 1 thread
01:32:21 Initializing from normalized Laplacian + noise
01:32:21 Commencing optimization for 500 epochs, with 226434 positive edges
01:32:37 Optimization finished
[1] "170 0.04"01:32:37 UMAP embedding parameters a = 1.786 b = 0.8316
01:32:37 Read 1203 rows and found 38 numeric columns
01:32:37 Using Annoy for neighbor search, n_neighbors = 170
01:32:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:32:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b4f663f
01:32:38 Searching Annoy index using 1 thread, search_k = 17000
01:32:39 Annoy recall = 100%
01:32:50 Commencing smooth kNN distance calibration using 1 thread
01:33:14 Initializing from normalized Laplacian + noise
01:33:14 Commencing optimization for 500 epochs, with 226434 positive edges
01:33:29 Optimization finished
[1] "170 0.05"01:33:30 UMAP embedding parameters a = 1.75 b = 0.8421
01:33:30 Read 1203 rows and found 38 numeric columns
01:33:30 Using Annoy for neighbor search, n_neighbors = 170
01:33:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:33:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724f6dd2d
01:33:31 Searching Annoy index using 1 thread, search_k = 17000
01:33:32 Annoy recall = 100%
01:33:43 Commencing smooth kNN distance calibration using 1 thread
01:34:06 Initializing from normalized Laplacian + noise
01:34:06 Commencing optimization for 500 epochs, with 226434 positive edges
01:34:22 Optimization finished
[1] "170 0.06"01:34:23 UMAP embedding parameters a = 1.715 b = 0.8526
01:34:23 Read 1203 rows and found 38 numeric columns
01:34:23 Using Annoy for neighbor search, n_neighbors = 170
01:34:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:34:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87149c1fbd
01:34:23 Searching Annoy index using 1 thread, search_k = 17000
01:34:25 Annoy recall = 100%
01:34:36 Commencing smooth kNN distance calibration using 1 thread
01:34:59 Initializing from normalized Laplacian + noise
01:34:59 Commencing optimization for 500 epochs, with 226434 positive edges
01:35:15 Optimization finished
[1] "170 0.07"01:35:15 UMAP embedding parameters a = 1.68 b = 0.8631
01:35:15 Read 1203 rows and found 38 numeric columns
01:35:15 Using Annoy for neighbor search, n_neighbors = 170
01:35:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:35:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87478ee035
01:35:16 Searching Annoy index using 1 thread, search_k = 17000
01:35:17 Annoy recall = 100%
01:35:29 Commencing smooth kNN distance calibration using 1 thread
01:35:52 Initializing from normalized Laplacian + noise
01:35:52 Commencing optimization for 500 epochs, with 226434 positive edges
01:36:08 Optimization finished
[1] "170 0.08"01:36:08 UMAP embedding parameters a = 1.645 b = 0.8737
01:36:08 Read 1203 rows and found 38 numeric columns
01:36:08 Using Annoy for neighbor search, n_neighbors = 170
01:36:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:36:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87574c7913
01:36:09 Searching Annoy index using 1 thread, search_k = 17000
01:36:10 Annoy recall = 100%
01:36:21 Commencing smooth kNN distance calibration using 1 thread
01:36:45 Initializing from normalized Laplacian + noise
01:36:45 Commencing optimization for 500 epochs, with 226434 positive edges
01:37:00 Optimization finished
[1] "170 0.09"01:37:01 UMAP embedding parameters a = 1.611 b = 0.8844
01:37:01 Read 1203 rows and found 38 numeric columns
01:37:01 Using Annoy for neighbor search, n_neighbors = 170
01:37:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:37:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fa03824
01:37:02 Searching Annoy index using 1 thread, search_k = 17000
01:37:03 Annoy recall = 100%
01:37:14 Commencing smooth kNN distance calibration using 1 thread
01:37:38 Initializing from normalized Laplacian + noise
01:37:38 Commencing optimization for 500 epochs, with 226434 positive edges
01:37:53 Optimization finished
[1] "170 0.1"01:37:54 UMAP embedding parameters a = 1.577 b = 0.8951
01:37:54 Read 1203 rows and found 38 numeric columns
01:37:54 Using Annoy for neighbor search, n_neighbors = 170
01:37:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:37:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a2414ca
01:37:55 Searching Annoy index using 1 thread, search_k = 17000
01:37:56 Annoy recall = 100%
01:38:07 Commencing smooth kNN distance calibration using 1 thread
01:38:30 Initializing from normalized Laplacian + noise
01:38:31 Commencing optimization for 500 epochs, with 226434 positive edges
01:38:46 Optimization finished
[1] "170 0.11"01:38:47 UMAP embedding parameters a = 1.544 b = 0.9058
01:38:47 Read 1203 rows and found 38 numeric columns
01:38:47 Using Annoy for neighbor search, n_neighbors = 170
01:38:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:38:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743b12d86
01:38:48 Searching Annoy index using 1 thread, search_k = 17000
01:38:49 Annoy recall = 100%
01:39:00 Commencing smooth kNN distance calibration using 1 thread
01:39:23 Initializing from normalized Laplacian + noise
01:39:23 Commencing optimization for 500 epochs, with 226434 positive edges
01:39:39 Optimization finished
[1] "170 0.12"01:39:40 UMAP embedding parameters a = 1.51 b = 0.9165
01:39:40 Read 1203 rows and found 38 numeric columns
01:39:40 Using Annoy for neighbor search, n_neighbors = 170
01:39:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:39:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717c178ec
01:39:41 Searching Annoy index using 1 thread, search_k = 17000
01:39:42 Annoy recall = 100%
01:39:53 Commencing smooth kNN distance calibration using 1 thread
01:40:16 Initializing from normalized Laplacian + noise
01:40:16 Commencing optimization for 500 epochs, with 226434 positive edges
01:40:32 Optimization finished
[1] "170 0.13"01:40:33 UMAP embedding parameters a = 1.478 b = 0.9272
01:40:33 Read 1203 rows and found 38 numeric columns
01:40:33 Using Annoy for neighbor search, n_neighbors = 170
01:40:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:40:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871db2d674
01:40:33 Searching Annoy index using 1 thread, search_k = 17000
01:40:35 Annoy recall = 100%
01:40:46 Commencing smooth kNN distance calibration using 1 thread
01:41:09 Initializing from normalized Laplacian + noise
01:41:09 Commencing optimization for 500 epochs, with 226434 positive edges
01:41:25 Optimization finished
[1] "170 0.14"01:41:25 UMAP embedding parameters a = 1.446 b = 0.938
01:41:25 Read 1203 rows and found 38 numeric columns
01:41:25 Using Annoy for neighbor search, n_neighbors = 170
01:41:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:41:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ca554b8
01:41:26 Searching Annoy index using 1 thread, search_k = 17000
01:41:28 Annoy recall = 100%
01:41:39 Commencing smooth kNN distance calibration using 1 thread
01:42:02 Initializing from normalized Laplacian + noise
01:42:02 Commencing optimization for 500 epochs, with 226434 positive edges
01:42:18 Optimization finished
[1] "170 0.15"01:42:18 UMAP embedding parameters a = 1.414 b = 0.9488
01:42:18 Read 1203 rows and found 38 numeric columns
01:42:18 Using Annoy for neighbor search, n_neighbors = 170
01:42:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:42:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873029e33
01:42:19 Searching Annoy index using 1 thread, search_k = 17000
01:42:21 Annoy recall = 100%
01:42:32 Commencing smooth kNN distance calibration using 1 thread
01:42:55 Initializing from normalized Laplacian + noise
01:42:55 Commencing optimization for 500 epochs, with 226434 positive edges
01:43:11 Optimization finished
[1] "170 0.16"01:43:11 UMAP embedding parameters a = 1.383 b = 0.9596
01:43:12 Read 1203 rows and found 38 numeric columns
01:43:12 Using Annoy for neighbor search, n_neighbors = 170
01:43:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:43:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87502895ef
01:43:12 Searching Annoy index using 1 thread, search_k = 17000
01:43:14 Annoy recall = 100%
01:43:25 Commencing smooth kNN distance calibration using 1 thread
01:43:48 Initializing from normalized Laplacian + noise
01:43:48 Commencing optimization for 500 epochs, with 226434 positive edges
01:44:04 Optimization finished
[1] "170 0.17"01:44:05 UMAP embedding parameters a = 1.352 b = 0.9704
01:44:05 Read 1203 rows and found 38 numeric columns
01:44:05 Using Annoy for neighbor search, n_neighbors = 170
01:44:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:44:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87667d81a1
01:44:06 Searching Annoy index using 1 thread, search_k = 17000
01:44:07 Annoy recall = 100%
01:44:18 Commencing smooth kNN distance calibration using 1 thread
01:44:41 Initializing from normalized Laplacian + noise
01:44:41 Commencing optimization for 500 epochs, with 226434 positive edges
01:44:57 Optimization finished
[1] "170 0.18"01:44:58 UMAP embedding parameters a = 1.321 b = 0.9813
01:44:58 Read 1203 rows and found 38 numeric columns
01:44:58 Using Annoy for neighbor search, n_neighbors = 170
01:44:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:44:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877741805f
01:44:59 Searching Annoy index using 1 thread, search_k = 17000
01:45:00 Annoy recall = 100%
01:45:11 Commencing smooth kNN distance calibration using 1 thread
01:45:34 Initializing from normalized Laplacian + noise
01:45:34 Commencing optimization for 500 epochs, with 226434 positive edges
01:45:50 Optimization finished
[1] "170 0.19"01:45:51 UMAP embedding parameters a = 1.292 b = 0.9921
01:45:51 Read 1203 rows and found 38 numeric columns
01:45:51 Using Annoy for neighbor search, n_neighbors = 170
01:45:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:45:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f4f5ff4
01:45:52 Searching Annoy index using 1 thread, search_k = 17000
01:45:53 Annoy recall = 100%
01:46:04 Commencing smooth kNN distance calibration using 1 thread
01:46:27 Initializing from normalized Laplacian + noise
01:46:28 Commencing optimization for 500 epochs, with 226434 positive edges
01:46:44 Optimization finished
[1] "170 0.2"01:46:44 UMAP embedding parameters a = 1.262 b = 1.003
01:46:44 Read 1203 rows and found 38 numeric columns
01:46:44 Using Annoy for neighbor search, n_neighbors = 170
01:46:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:46:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e4281d0
01:46:45 Searching Annoy index using 1 thread, search_k = 17000
01:46:46 Annoy recall = 100%
01:46:57 Commencing smooth kNN distance calibration using 1 thread
01:47:21 Initializing from normalized Laplacian + noise
01:47:21 Commencing optimization for 500 epochs, with 226434 positive edges
01:47:37 Optimization finished
[1] "171 0"01:47:37 UMAP embedding parameters a = 1.933 b = 0.7905
01:47:37 Read 1203 rows and found 38 numeric columns
01:47:37 Using Annoy for neighbor search, n_neighbors = 171
01:47:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:47:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c228f86
01:47:38 Searching Annoy index using 1 thread, search_k = 17100
01:47:39 Annoy recall = 100%
01:47:51 Commencing smooth kNN distance calibration using 1 thread
01:48:14 Initializing from normalized Laplacian + noise
01:48:14 Commencing optimization for 500 epochs, with 227682 positive edges
01:48:30 Optimization finished
[1] "171 0.01"01:48:30 UMAP embedding parameters a = 1.896 b = 0.8006
01:48:30 Read 1203 rows and found 38 numeric columns
01:48:30 Using Annoy for neighbor search, n_neighbors = 171
01:48:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:48:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e5e7884
01:48:31 Searching Annoy index using 1 thread, search_k = 17100
01:48:32 Annoy recall = 100%
01:48:44 Commencing smooth kNN distance calibration using 1 thread
01:49:07 Initializing from normalized Laplacian + noise
01:49:07 Commencing optimization for 500 epochs, with 227682 positive edges
01:49:23 Optimization finished
[1] "171 0.02"01:49:23 UMAP embedding parameters a = 1.859 b = 0.8109
01:49:23 Read 1203 rows and found 38 numeric columns
01:49:23 Using Annoy for neighbor search, n_neighbors = 171
01:49:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:49:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872eb6a57a
01:49:24 Searching Annoy index using 1 thread, search_k = 17100
01:49:25 Annoy recall = 100%
01:49:37 Commencing smooth kNN distance calibration using 1 thread
01:50:00 Initializing from normalized Laplacian + noise
01:50:00 Commencing optimization for 500 epochs, with 227682 positive edges
01:50:16 Optimization finished
[1] "171 0.03"01:50:16 UMAP embedding parameters a = 1.822 b = 0.8212
01:50:16 Read 1203 rows and found 38 numeric columns
01:50:16 Using Annoy for neighbor search, n_neighbors = 171
01:50:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:50:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c932749
01:50:17 Searching Annoy index using 1 thread, search_k = 17100
01:50:18 Annoy recall = 100%
01:50:30 Commencing smooth kNN distance calibration using 1 thread
01:50:53 Initializing from normalized Laplacian + noise
01:50:53 Commencing optimization for 500 epochs, with 227682 positive edges
01:51:09 Optimization finished
[1] "171 0.04"01:51:09 UMAP embedding parameters a = 1.786 b = 0.8316
01:51:09 Read 1203 rows and found 38 numeric columns
01:51:09 Using Annoy for neighbor search, n_neighbors = 171
01:51:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:51:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87291feac
01:51:10 Searching Annoy index using 1 thread, search_k = 17100
01:51:12 Annoy recall = 100%
01:51:23 Commencing smooth kNN distance calibration using 1 thread
01:51:46 Initializing from normalized Laplacian + noise
01:51:46 Commencing optimization for 500 epochs, with 227682 positive edges
01:52:02 Optimization finished
[1] "171 0.05"01:52:03 UMAP embedding parameters a = 1.75 b = 0.8421
01:52:03 Read 1203 rows and found 38 numeric columns
01:52:03 Using Annoy for neighbor search, n_neighbors = 171
01:52:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:52:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874017d6de
01:52:03 Searching Annoy index using 1 thread, search_k = 17100
01:52:05 Annoy recall = 100%
01:52:16 Commencing smooth kNN distance calibration using 1 thread
01:52:40 Initializing from normalized Laplacian + noise
01:52:40 Commencing optimization for 500 epochs, with 227682 positive edges
01:52:56 Optimization finished
[1] "171 0.06"01:52:56 UMAP embedding parameters a = 1.715 b = 0.8526
01:52:56 Read 1203 rows and found 38 numeric columns
01:52:56 Using Annoy for neighbor search, n_neighbors = 171
01:52:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:52:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760fe358
01:52:57 Searching Annoy index using 1 thread, search_k = 17100
01:52:58 Annoy recall = 100%
01:53:09 Commencing smooth kNN distance calibration using 1 thread
01:53:33 Initializing from normalized Laplacian + noise
01:53:33 Commencing optimization for 500 epochs, with 227682 positive edges
01:53:49 Optimization finished
[1] "171 0.07"01:53:49 UMAP embedding parameters a = 1.68 b = 0.8631
01:53:49 Read 1203 rows and found 38 numeric columns
01:53:49 Using Annoy for neighbor search, n_neighbors = 171
01:53:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:53:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719449621
01:53:50 Searching Annoy index using 1 thread, search_k = 17100
01:53:51 Annoy recall = 100%
01:54:03 Commencing smooth kNN distance calibration using 1 thread
01:54:26 Initializing from normalized Laplacian + noise
01:54:26 Commencing optimization for 500 epochs, with 227682 positive edges
01:54:42 Optimization finished
[1] "171 0.08"01:54:42 UMAP embedding parameters a = 1.645 b = 0.8737
01:54:42 Read 1203 rows and found 38 numeric columns
01:54:42 Using Annoy for neighbor search, n_neighbors = 171
01:54:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:54:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87126caf21
01:54:43 Searching Annoy index using 1 thread, search_k = 17100
01:54:44 Annoy recall = 100%
01:54:56 Commencing smooth kNN distance calibration using 1 thread
01:55:19 Initializing from normalized Laplacian + noise
01:55:19 Commencing optimization for 500 epochs, with 227682 positive edges
01:55:35 Optimization finished
[1] "171 0.09"01:55:36 UMAP embedding parameters a = 1.611 b = 0.8844
01:55:36 Read 1203 rows and found 38 numeric columns
01:55:36 Using Annoy for neighbor search, n_neighbors = 171
01:55:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:55:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87110a1996
01:55:36 Searching Annoy index using 1 thread, search_k = 17100
01:55:38 Annoy recall = 100%
01:55:49 Commencing smooth kNN distance calibration using 1 thread
01:56:12 Initializing from normalized Laplacian + noise
01:56:12 Commencing optimization for 500 epochs, with 227682 positive edges
01:56:28 Optimization finished
[1] "171 0.1"01:56:29 UMAP embedding parameters a = 1.577 b = 0.8951
01:56:29 Read 1203 rows and found 38 numeric columns
01:56:29 Using Annoy for neighbor search, n_neighbors = 171
01:56:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:56:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751a95432
01:56:30 Searching Annoy index using 1 thread, search_k = 17100
01:56:31 Annoy recall = 100%
01:56:42 Commencing smooth kNN distance calibration using 1 thread
01:57:06 Initializing from normalized Laplacian + noise
01:57:06 Commencing optimization for 500 epochs, with 227682 positive edges
01:57:22 Optimization finished
[1] "171 0.11"01:57:22 UMAP embedding parameters a = 1.544 b = 0.9058
01:57:22 Read 1203 rows and found 38 numeric columns
01:57:22 Using Annoy for neighbor search, n_neighbors = 171
01:57:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:57:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87677a7e47
01:57:23 Searching Annoy index using 1 thread, search_k = 17100
01:57:24 Annoy recall = 100%
01:57:36 Commencing smooth kNN distance calibration using 1 thread
01:57:59 Initializing from normalized Laplacian + noise
01:57:59 Commencing optimization for 500 epochs, with 227682 positive edges
01:58:15 Optimization finished
[1] "171 0.12"01:58:15 UMAP embedding parameters a = 1.51 b = 0.9165
01:58:15 Read 1203 rows and found 38 numeric columns
01:58:15 Using Annoy for neighbor search, n_neighbors = 171
01:58:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:58:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763adab57
01:58:16 Searching Annoy index using 1 thread, search_k = 17100
01:58:17 Annoy recall = 100%
01:58:29 Commencing smooth kNN distance calibration using 1 thread
01:58:52 Initializing from normalized Laplacian + noise
01:58:52 Commencing optimization for 500 epochs, with 227682 positive edges
01:59:08 Optimization finished
[1] "171 0.13"01:59:09 UMAP embedding parameters a = 1.478 b = 0.9272
01:59:09 Read 1203 rows and found 38 numeric columns
01:59:09 Using Annoy for neighbor search, n_neighbors = 171
01:59:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:59:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87153c793a
01:59:10 Searching Annoy index using 1 thread, search_k = 17100
01:59:11 Annoy recall = 100%
01:59:22 Commencing smooth kNN distance calibration using 1 thread
01:59:46 Initializing from normalized Laplacian + noise
01:59:46 Commencing optimization for 500 epochs, with 227682 positive edges
02:00:02 Optimization finished
[1] "171 0.14"02:00:02 UMAP embedding parameters a = 1.446 b = 0.938
02:00:02 Read 1203 rows and found 38 numeric columns
02:00:02 Using Annoy for neighbor search, n_neighbors = 171
02:00:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:00:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772c9e486
02:00:03 Searching Annoy index using 1 thread, search_k = 17100
02:00:04 Annoy recall = 100%
02:00:16 Commencing smooth kNN distance calibration using 1 thread
02:00:39 Initializing from normalized Laplacian + noise
02:00:39 Commencing optimization for 500 epochs, with 227682 positive edges
02:00:55 Optimization finished
[1] "171 0.15"02:00:55 UMAP embedding parameters a = 1.414 b = 0.9488
02:00:55 Read 1203 rows and found 38 numeric columns
02:00:55 Using Annoy for neighbor search, n_neighbors = 171
02:00:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:00:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878a48884
02:00:56 Searching Annoy index using 1 thread, search_k = 17100
02:00:57 Annoy recall = 100%
02:01:09 Commencing smooth kNN distance calibration using 1 thread
02:01:32 Initializing from normalized Laplacian + noise
02:01:32 Commencing optimization for 500 epochs, with 227682 positive edges
02:01:48 Optimization finished
[1] "171 0.16"02:01:49 UMAP embedding parameters a = 1.383 b = 0.9596
02:01:49 Read 1203 rows and found 38 numeric columns
02:01:49 Using Annoy for neighbor search, n_neighbors = 171
02:01:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:01:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729d898f7
02:01:49 Searching Annoy index using 1 thread, search_k = 17100
02:01:51 Annoy recall = 100%
02:02:02 Commencing smooth kNN distance calibration using 1 thread
02:02:26 Initializing from normalized Laplacian + noise
02:02:26 Commencing optimization for 500 epochs, with 227682 positive edges
02:02:42 Optimization finished
[1] "171 0.17"02:02:42 UMAP embedding parameters a = 1.352 b = 0.9704
02:02:42 Read 1203 rows and found 38 numeric columns
02:02:42 Using Annoy for neighbor search, n_neighbors = 171
02:02:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:02:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a58c4bb
02:02:43 Searching Annoy index using 1 thread, search_k = 17100
02:02:44 Annoy recall = 100%
02:02:56 Commencing smooth kNN distance calibration using 1 thread
02:03:19 Initializing from normalized Laplacian + noise
02:03:19 Commencing optimization for 500 epochs, with 227682 positive edges
02:03:35 Optimization finished
[1] "171 0.18"02:03:35 UMAP embedding parameters a = 1.321 b = 0.9813
02:03:35 Read 1203 rows and found 38 numeric columns
02:03:35 Using Annoy for neighbor search, n_neighbors = 171
02:03:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:03:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ff10197
02:03:36 Searching Annoy index using 1 thread, search_k = 17100
02:03:37 Annoy recall = 100%
02:03:49 Commencing smooth kNN distance calibration using 1 thread
02:04:12 Initializing from normalized Laplacian + noise
02:04:12 Commencing optimization for 500 epochs, with 227682 positive edges
02:04:28 Optimization finished
[1] "171 0.19"02:04:29 UMAP embedding parameters a = 1.292 b = 0.9921
02:04:29 Read 1203 rows and found 38 numeric columns
02:04:29 Using Annoy for neighbor search, n_neighbors = 171
02:04:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:04:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877978d11b
02:04:30 Searching Annoy index using 1 thread, search_k = 17100
02:04:31 Annoy recall = 100%
02:04:42 Commencing smooth kNN distance calibration using 1 thread
02:05:06 Initializing from normalized Laplacian + noise
02:05:06 Commencing optimization for 500 epochs, with 227682 positive edges
02:05:22 Optimization finished
[1] "171 0.2"02:05:22 UMAP embedding parameters a = 1.262 b = 1.003
02:05:22 Read 1203 rows and found 38 numeric columns
02:05:22 Using Annoy for neighbor search, n_neighbors = 171
02:05:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:05:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87247cd986
02:05:23 Searching Annoy index using 1 thread, search_k = 17100
02:05:24 Annoy recall = 100%
02:05:36 Commencing smooth kNN distance calibration using 1 thread
02:05:59 Initializing from normalized Laplacian + noise
02:05:59 Commencing optimization for 500 epochs, with 227682 positive edges
02:06:15 Optimization finished
[1] "172 0"02:06:15 UMAP embedding parameters a = 1.933 b = 0.7905
02:06:15 Read 1203 rows and found 38 numeric columns
02:06:15 Using Annoy for neighbor search, n_neighbors = 172
02:06:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:06:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723a22f1e
02:06:16 Searching Annoy index using 1 thread, search_k = 17200
02:06:17 Annoy recall = 100%
02:06:29 Commencing smooth kNN distance calibration using 1 thread
02:06:52 Initializing from normalized Laplacian + noise
02:06:52 Commencing optimization for 500 epochs, with 228864 positive edges
02:07:08 Optimization finished
[1] "172 0.01"02:07:09 UMAP embedding parameters a = 1.896 b = 0.8006
02:07:09 Read 1203 rows and found 38 numeric columns
02:07:09 Using Annoy for neighbor search, n_neighbors = 172
02:07:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:07:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87113a4a07
02:07:10 Searching Annoy index using 1 thread, search_k = 17200
02:07:11 Annoy recall = 100%
02:07:23 Commencing smooth kNN distance calibration using 1 thread
02:07:46 Initializing from normalized Laplacian + noise
02:07:46 Commencing optimization for 500 epochs, with 228864 positive edges
02:08:02 Optimization finished
[1] "172 0.02"02:08:02 UMAP embedding parameters a = 1.859 b = 0.8109
02:08:02 Read 1203 rows and found 38 numeric columns
02:08:02 Using Annoy for neighbor search, n_neighbors = 172
02:08:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:08:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87422faffa
02:08:03 Searching Annoy index using 1 thread, search_k = 17200
02:08:04 Annoy recall = 100%
02:08:16 Commencing smooth kNN distance calibration using 1 thread
02:08:39 Initializing from normalized Laplacian + noise
02:08:40 Commencing optimization for 500 epochs, with 228864 positive edges
02:08:55 Optimization finished
[1] "172 0.03"02:08:56 UMAP embedding parameters a = 1.822 b = 0.8212
02:08:56 Read 1203 rows and found 38 numeric columns
02:08:56 Using Annoy for neighbor search, n_neighbors = 172
02:08:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:08:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87304783d6
02:08:56 Searching Annoy index using 1 thread, search_k = 17200
02:08:58 Annoy recall = 100%
02:09:09 Commencing smooth kNN distance calibration using 1 thread
02:09:33 Initializing from normalized Laplacian + noise
02:09:33 Commencing optimization for 500 epochs, with 228864 positive edges
02:09:49 Optimization finished
[1] "172 0.04"02:09:49 UMAP embedding parameters a = 1.786 b = 0.8316
02:09:49 Read 1203 rows and found 38 numeric columns
02:09:49 Using Annoy for neighbor search, n_neighbors = 172
02:09:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:09:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87143ce83a
02:09:50 Searching Annoy index using 1 thread, search_k = 17200
02:09:51 Annoy recall = 100%
02:10:03 Commencing smooth kNN distance calibration using 1 thread
02:10:26 Initializing from normalized Laplacian + noise
02:10:26 Commencing optimization for 500 epochs, with 228864 positive edges
02:10:43 Optimization finished
[1] "172 0.05"02:10:43 UMAP embedding parameters a = 1.75 b = 0.8421
02:10:43 Read 1203 rows and found 38 numeric columns
02:10:43 Using Annoy for neighbor search, n_neighbors = 172
02:10:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:10:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87125845e9
02:10:44 Searching Annoy index using 1 thread, search_k = 17200
02:10:45 Annoy recall = 100%
02:10:56 Commencing smooth kNN distance calibration using 1 thread
02:11:20 Initializing from normalized Laplacian + noise
02:11:20 Commencing optimization for 500 epochs, with 228864 positive edges
02:11:36 Optimization finished
[1] "172 0.06"02:11:36 UMAP embedding parameters a = 1.715 b = 0.8526
02:11:36 Read 1203 rows and found 38 numeric columns
02:11:36 Using Annoy for neighbor search, n_neighbors = 172
02:11:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:11:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716c50577
02:11:37 Searching Annoy index using 1 thread, search_k = 17200
02:11:38 Annoy recall = 100%
02:11:50 Commencing smooth kNN distance calibration using 1 thread
02:12:13 Initializing from normalized Laplacian + noise
02:12:13 Commencing optimization for 500 epochs, with 228864 positive edges
02:12:29 Optimization finished
[1] "172 0.07"02:12:30 UMAP embedding parameters a = 1.68 b = 0.8631
02:12:30 Read 1203 rows and found 38 numeric columns
02:12:30 Using Annoy for neighbor search, n_neighbors = 172
02:12:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:12:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b7e6899
02:12:31 Searching Annoy index using 1 thread, search_k = 17200
02:12:32 Annoy recall = 100%
02:12:44 Commencing smooth kNN distance calibration using 1 thread
02:13:07 Initializing from normalized Laplacian + noise
02:13:07 Commencing optimization for 500 epochs, with 228864 positive edges
02:13:23 Optimization finished
[1] "172 0.08"02:13:23 UMAP embedding parameters a = 1.645 b = 0.8737
02:13:23 Read 1203 rows and found 38 numeric columns
02:13:23 Using Annoy for neighbor search, n_neighbors = 172
02:13:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:13:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711a7a5dd
02:13:24 Searching Annoy index using 1 thread, search_k = 17200
02:13:25 Annoy recall = 100%
02:13:37 Commencing smooth kNN distance calibration using 1 thread
02:14:01 Initializing from normalized Laplacian + noise
02:14:01 Commencing optimization for 500 epochs, with 228864 positive edges
02:14:16 Optimization finished
[1] "172 0.09"02:14:17 UMAP embedding parameters a = 1.611 b = 0.8844
02:14:17 Read 1203 rows and found 38 numeric columns
02:14:17 Using Annoy for neighbor search, n_neighbors = 172
02:14:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:14:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725078748
02:14:18 Searching Annoy index using 1 thread, search_k = 17200
02:14:19 Annoy recall = 100%
02:14:31 Commencing smooth kNN distance calibration using 1 thread
02:14:54 Initializing from normalized Laplacian + noise
02:14:54 Commencing optimization for 500 epochs, with 228864 positive edges
02:15:10 Optimization finished
[1] "172 0.1"02:15:10 UMAP embedding parameters a = 1.577 b = 0.8951
02:15:10 Read 1203 rows and found 38 numeric columns
02:15:10 Using Annoy for neighbor search, n_neighbors = 172
02:15:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:15:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757a0f820
02:15:11 Searching Annoy index using 1 thread, search_k = 17200
02:15:12 Annoy recall = 100%
02:15:24 Commencing smooth kNN distance calibration using 1 thread
02:15:48 Initializing from normalized Laplacian + noise
02:15:48 Commencing optimization for 500 epochs, with 228864 positive edges
02:16:04 Optimization finished
[1] "172 0.11"02:16:04 UMAP embedding parameters a = 1.544 b = 0.9058
02:16:04 Read 1203 rows and found 38 numeric columns
02:16:04 Using Annoy for neighbor search, n_neighbors = 172
02:16:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:16:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8740061e61
02:16:05 Searching Annoy index using 1 thread, search_k = 17200
02:16:06 Annoy recall = 100%
02:16:18 Commencing smooth kNN distance calibration using 1 thread
02:16:41 Initializing from normalized Laplacian + noise
02:16:41 Commencing optimization for 500 epochs, with 228864 positive edges
02:16:57 Optimization finished
[1] "172 0.12"02:16:58 UMAP embedding parameters a = 1.51 b = 0.9165
02:16:58 Read 1203 rows and found 38 numeric columns
02:16:58 Using Annoy for neighbor search, n_neighbors = 172
02:16:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:16:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753be2cc2
02:16:58 Searching Annoy index using 1 thread, search_k = 17200
02:17:00 Annoy recall = 100%
02:17:11 Commencing smooth kNN distance calibration using 1 thread
02:17:35 Initializing from normalized Laplacian + noise
02:17:35 Commencing optimization for 500 epochs, with 228864 positive edges
02:17:51 Optimization finished
[1] "172 0.13"02:17:51 UMAP embedding parameters a = 1.478 b = 0.9272
02:17:51 Read 1203 rows and found 38 numeric columns
02:17:51 Using Annoy for neighbor search, n_neighbors = 172
02:17:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:17:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8774341f69
02:17:52 Searching Annoy index using 1 thread, search_k = 17200
02:17:53 Annoy recall = 100%
02:18:05 Commencing smooth kNN distance calibration using 1 thread
02:18:28 Initializing from normalized Laplacian + noise
02:18:28 Commencing optimization for 500 epochs, with 228864 positive edges
02:18:44 Optimization finished
[1] "172 0.14"02:18:45 UMAP embedding parameters a = 1.446 b = 0.938
02:18:45 Read 1203 rows and found 38 numeric columns
02:18:45 Using Annoy for neighbor search, n_neighbors = 172
02:18:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:18:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742981d0d
02:18:46 Searching Annoy index using 1 thread, search_k = 17200
02:18:47 Annoy recall = 100%
02:18:59 Commencing smooth kNN distance calibration using 1 thread
02:19:22 Initializing from normalized Laplacian + noise
02:19:22 Commencing optimization for 500 epochs, with 228864 positive edges
02:19:38 Optimization finished
[1] "172 0.15"02:19:38 UMAP embedding parameters a = 1.414 b = 0.9488
02:19:38 Read 1203 rows and found 38 numeric columns
02:19:38 Using Annoy for neighbor search, n_neighbors = 172
02:19:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:19:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713d603a0
02:19:39 Searching Annoy index using 1 thread, search_k = 17200
02:19:40 Annoy recall = 100%
02:19:52 Commencing smooth kNN distance calibration using 1 thread
02:20:16 Initializing from normalized Laplacian + noise
02:20:16 Commencing optimization for 500 epochs, with 228864 positive edges
02:20:32 Optimization finished
[1] "172 0.16"02:20:32 UMAP embedding parameters a = 1.383 b = 0.9596
02:20:32 Read 1203 rows and found 38 numeric columns
02:20:32 Using Annoy for neighbor search, n_neighbors = 172
02:20:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:20:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a4402c1
02:20:33 Searching Annoy index using 1 thread, search_k = 17200
02:20:34 Annoy recall = 100%
02:20:46 Commencing smooth kNN distance calibration using 1 thread
02:21:09 Initializing from normalized Laplacian + noise
02:21:10 Commencing optimization for 500 epochs, with 228864 positive edges
02:21:26 Optimization finished
[1] "172 0.17"02:21:26 UMAP embedding parameters a = 1.352 b = 0.9704
02:21:26 Read 1203 rows and found 38 numeric columns
02:21:26 Using Annoy for neighbor search, n_neighbors = 172
02:21:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:21:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bdcb32e
02:21:27 Searching Annoy index using 1 thread, search_k = 17200
02:21:28 Annoy recall = 100%
02:21:40 Commencing smooth kNN distance calibration using 1 thread
02:22:03 Initializing from normalized Laplacian + noise
02:22:03 Commencing optimization for 500 epochs, with 228864 positive edges
02:22:19 Optimization finished
[1] "172 0.18"02:22:20 UMAP embedding parameters a = 1.321 b = 0.9813
02:22:20 Read 1203 rows and found 38 numeric columns
02:22:20 Using Annoy for neighbor search, n_neighbors = 172
02:22:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:22:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872642b2c1
02:22:21 Searching Annoy index using 1 thread, search_k = 17200
02:22:22 Annoy recall = 100%
02:22:33 Commencing smooth kNN distance calibration using 1 thread
02:22:57 Initializing from normalized Laplacian + noise
02:22:57 Commencing optimization for 500 epochs, with 228864 positive edges
02:23:13 Optimization finished
[1] "172 0.19"02:23:13 UMAP embedding parameters a = 1.292 b = 0.9921
02:23:13 Read 1203 rows and found 38 numeric columns
02:23:13 Using Annoy for neighbor search, n_neighbors = 172
02:23:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:23:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b4e1c57
02:23:14 Searching Annoy index using 1 thread, search_k = 17200
02:23:15 Annoy recall = 100%
02:23:27 Commencing smooth kNN distance calibration using 1 thread
02:23:50 Initializing from normalized Laplacian + noise
02:23:51 Commencing optimization for 500 epochs, with 228864 positive edges
02:24:07 Optimization finished
[1] "172 0.2"02:24:07 UMAP embedding parameters a = 1.262 b = 1.003
02:24:07 Read 1203 rows and found 38 numeric columns
02:24:07 Using Annoy for neighbor search, n_neighbors = 172
02:24:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:24:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d860760
02:24:08 Searching Annoy index using 1 thread, search_k = 17200
02:24:09 Annoy recall = 100%
02:24:21 Commencing smooth kNN distance calibration using 1 thread
02:24:44 Initializing from normalized Laplacian + noise
02:24:44 Commencing optimization for 500 epochs, with 228864 positive edges
02:25:00 Optimization finished
[1] "173 0"02:25:01 UMAP embedding parameters a = 1.933 b = 0.7905
02:25:01 Read 1203 rows and found 38 numeric columns
02:25:01 Using Annoy for neighbor search, n_neighbors = 173
02:25:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:25:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dbd3109
02:25:02 Searching Annoy index using 1 thread, search_k = 17300
02:25:03 Annoy recall = 100%
02:25:14 Commencing smooth kNN distance calibration using 1 thread
02:25:38 Initializing from normalized Laplacian + noise
02:25:38 Commencing optimization for 500 epochs, with 230050 positive edges
02:25:54 Optimization finished
[1] "173 0.01"02:25:54 UMAP embedding parameters a = 1.896 b = 0.8006
02:25:54 Read 1203 rows and found 38 numeric columns
02:25:54 Using Annoy for neighbor search, n_neighbors = 173
02:25:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:25:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876efbc7ae
02:25:55 Searching Annoy index using 1 thread, search_k = 17300
02:25:56 Annoy recall = 100%
02:26:08 Commencing smooth kNN distance calibration using 1 thread
02:26:32 Initializing from normalized Laplacian + noise
02:26:32 Commencing optimization for 500 epochs, with 230050 positive edges
02:26:48 Optimization finished
[1] "173 0.02"02:26:48 UMAP embedding parameters a = 1.859 b = 0.8109
02:26:48 Read 1203 rows and found 38 numeric columns
02:26:48 Using Annoy for neighbor search, n_neighbors = 173
02:26:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:26:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742c2809b
02:26:49 Searching Annoy index using 1 thread, search_k = 17300
02:26:50 Annoy recall = 100%
02:27:02 Commencing smooth kNN distance calibration using 1 thread
02:27:25 Initializing from normalized Laplacian + noise
02:27:26 Commencing optimization for 500 epochs, with 230050 positive edges
02:27:42 Optimization finished
[1] "173 0.03"02:27:42 UMAP embedding parameters a = 1.822 b = 0.8212
02:27:42 Read 1203 rows and found 38 numeric columns
02:27:42 Using Annoy for neighbor search, n_neighbors = 173
02:27:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:27:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8787158f
02:27:43 Searching Annoy index using 1 thread, search_k = 17300
02:27:44 Annoy recall = 100%
02:27:56 Commencing smooth kNN distance calibration using 1 thread
02:28:19 Initializing from normalized Laplacian + noise
02:28:19 Commencing optimization for 500 epochs, with 230050 positive edges
02:28:36 Optimization finished
[1] "173 0.04"02:28:36 UMAP embedding parameters a = 1.786 b = 0.8316
02:28:36 Read 1203 rows and found 38 numeric columns
02:28:36 Using Annoy for neighbor search, n_neighbors = 173
02:28:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:28:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777a05032
02:28:37 Searching Annoy index using 1 thread, search_k = 17300
02:28:38 Annoy recall = 100%
02:28:50 Commencing smooth kNN distance calibration using 1 thread
02:29:13 Initializing from normalized Laplacian + noise
02:29:13 Commencing optimization for 500 epochs, with 230050 positive edges
02:29:29 Optimization finished
[1] "173 0.05"02:29:29 UMAP embedding parameters a = 1.75 b = 0.8421
02:29:30 Read 1203 rows and found 38 numeric columns
02:29:30 Using Annoy for neighbor search, n_neighbors = 173
02:29:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:29:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c9b1992
02:29:30 Searching Annoy index using 1 thread, search_k = 17300
02:29:32 Annoy recall = 100%
02:29:43 Commencing smooth kNN distance calibration using 1 thread
02:30:07 Initializing from normalized Laplacian + noise
02:30:07 Commencing optimization for 500 epochs, with 230050 positive edges
02:30:23 Optimization finished
[1] "173 0.06"02:30:23 UMAP embedding parameters a = 1.715 b = 0.8526
02:30:23 Read 1203 rows and found 38 numeric columns
02:30:23 Using Annoy for neighbor search, n_neighbors = 173
02:30:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:30:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873adfda4b
02:30:24 Searching Annoy index using 1 thread, search_k = 17300
02:30:25 Annoy recall = 100%
02:30:37 Commencing smooth kNN distance calibration using 1 thread
02:31:01 Initializing from normalized Laplacian + noise
02:31:01 Commencing optimization for 500 epochs, with 230050 positive edges
02:31:17 Optimization finished
[1] "173 0.07"02:31:17 UMAP embedding parameters a = 1.68 b = 0.8631
02:31:17 Read 1203 rows and found 38 numeric columns
02:31:17 Using Annoy for neighbor search, n_neighbors = 173
02:31:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:31:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87579151ca
02:31:18 Searching Annoy index using 1 thread, search_k = 17300
02:31:19 Annoy recall = 100%
02:31:31 Commencing smooth kNN distance calibration using 1 thread
02:31:55 Initializing from normalized Laplacian + noise
02:31:55 Commencing optimization for 500 epochs, with 230050 positive edges
02:32:11 Optimization finished
[1] "173 0.08"02:32:11 UMAP embedding parameters a = 1.645 b = 0.8737
02:32:11 Read 1203 rows and found 38 numeric columns
02:32:11 Using Annoy for neighbor search, n_neighbors = 173
02:32:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:32:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876613eaae
02:32:12 Searching Annoy index using 1 thread, search_k = 17300
02:32:13 Annoy recall = 100%
02:32:25 Commencing smooth kNN distance calibration using 1 thread
02:32:48 Initializing from normalized Laplacian + noise
02:32:48 Commencing optimization for 500 epochs, with 230050 positive edges
02:33:05 Optimization finished
[1] "173 0.09"02:33:05 UMAP embedding parameters a = 1.611 b = 0.8844
02:33:05 Read 1203 rows and found 38 numeric columns
02:33:05 Using Annoy for neighbor search, n_neighbors = 173
02:33:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:33:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f5cb3d1
02:33:06 Searching Annoy index using 1 thread, search_k = 17300
02:33:07 Annoy recall = 100%
02:33:19 Commencing smooth kNN distance calibration using 1 thread
02:33:42 Initializing from normalized Laplacian + noise
02:33:42 Commencing optimization for 500 epochs, with 230050 positive edges
02:33:58 Optimization finished
[1] "173 0.1"02:33:59 UMAP embedding parameters a = 1.577 b = 0.8951
02:33:59 Read 1203 rows and found 38 numeric columns
02:33:59 Using Annoy for neighbor search, n_neighbors = 173
02:33:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:34:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877b3380e8
02:34:00 Searching Annoy index using 1 thread, search_k = 17300
02:34:01 Annoy recall = 100%
02:34:12 Commencing smooth kNN distance calibration using 1 thread
02:34:36 Initializing from normalized Laplacian + noise
02:34:36 Commencing optimization for 500 epochs, with 230050 positive edges
02:34:52 Optimization finished
[1] "173 0.11"02:34:52 UMAP embedding parameters a = 1.544 b = 0.9058
02:34:53 Read 1203 rows and found 38 numeric columns
02:34:53 Using Annoy for neighbor search, n_neighbors = 173
02:34:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:34:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87774e34b5
02:34:53 Searching Annoy index using 1 thread, search_k = 17300
02:34:55 Annoy recall = 100%
02:35:06 Commencing smooth kNN distance calibration using 1 thread
02:35:30 Initializing from normalized Laplacian + noise
02:35:30 Commencing optimization for 500 epochs, with 230050 positive edges
02:35:46 Optimization finished
[1] "173 0.12"02:35:46 UMAP embedding parameters a = 1.51 b = 0.9165
02:35:46 Read 1203 rows and found 38 numeric columns
02:35:46 Using Annoy for neighbor search, n_neighbors = 173
02:35:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:35:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87218c63cb
02:35:47 Searching Annoy index using 1 thread, search_k = 17300
02:35:48 Annoy recall = 100%
02:36:00 Commencing smooth kNN distance calibration using 1 thread
02:36:24 Initializing from normalized Laplacian + noise
02:36:24 Commencing optimization for 500 epochs, with 230050 positive edges
02:36:40 Optimization finished
[1] "173 0.13"02:36:40 UMAP embedding parameters a = 1.478 b = 0.9272
02:36:40 Read 1203 rows and found 38 numeric columns
02:36:40 Using Annoy for neighbor search, n_neighbors = 173
02:36:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:36:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b7b04be
02:36:41 Searching Annoy index using 1 thread, search_k = 17300
02:36:42 Annoy recall = 100%
02:36:54 Commencing smooth kNN distance calibration using 1 thread
02:37:18 Initializing from normalized Laplacian + noise
02:37:18 Commencing optimization for 500 epochs, with 230050 positive edges
02:37:34 Optimization finished
[1] "173 0.14"02:37:34 UMAP embedding parameters a = 1.446 b = 0.938
02:37:34 Read 1203 rows and found 38 numeric columns
02:37:34 Using Annoy for neighbor search, n_neighbors = 173
02:37:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:37:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b8b1cf0
02:37:35 Searching Annoy index using 1 thread, search_k = 17300
02:37:36 Annoy recall = 100%
02:37:48 Commencing smooth kNN distance calibration using 1 thread
02:38:12 Initializing from normalized Laplacian + noise
02:38:12 Commencing optimization for 500 epochs, with 230050 positive edges
02:38:28 Optimization finished
[1] "173 0.15"02:38:28 UMAP embedding parameters a = 1.414 b = 0.9488
02:38:28 Read 1203 rows and found 38 numeric columns
02:38:28 Using Annoy for neighbor search, n_neighbors = 173
02:38:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:38:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733e4a9b4
02:38:29 Searching Annoy index using 1 thread, search_k = 17300
02:38:30 Annoy recall = 100%
02:38:42 Commencing smooth kNN distance calibration using 1 thread
02:39:06 Initializing from normalized Laplacian + noise
02:39:06 Commencing optimization for 500 epochs, with 230050 positive edges
02:39:22 Optimization finished
[1] "173 0.16"02:39:22 UMAP embedding parameters a = 1.383 b = 0.9596
02:39:22 Read 1203 rows and found 38 numeric columns
02:39:22 Using Annoy for neighbor search, n_neighbors = 173
02:39:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:39:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742400a36
02:39:23 Searching Annoy index using 1 thread, search_k = 17300
02:39:24 Annoy recall = 100%
02:39:36 Commencing smooth kNN distance calibration using 1 thread
02:39:59 Initializing from normalized Laplacian + noise
02:40:00 Commencing optimization for 500 epochs, with 230050 positive edges
02:40:16 Optimization finished
[1] "173 0.17"02:40:16 UMAP embedding parameters a = 1.352 b = 0.9704
02:40:16 Read 1203 rows and found 38 numeric columns
02:40:16 Using Annoy for neighbor search, n_neighbors = 173
02:40:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:40:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717098589
02:40:17 Searching Annoy index using 1 thread, search_k = 17300
02:40:18 Annoy recall = 100%
02:40:30 Commencing smooth kNN distance calibration using 1 thread
02:40:53 Initializing from normalized Laplacian + noise
02:40:53 Commencing optimization for 500 epochs, with 230050 positive edges
02:41:10 Optimization finished
[1] "173 0.18"02:41:10 UMAP embedding parameters a = 1.321 b = 0.9813
02:41:10 Read 1203 rows and found 38 numeric columns
02:41:10 Using Annoy for neighbor search, n_neighbors = 173
02:41:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:41:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87458c4f91
02:41:11 Searching Annoy index using 1 thread, search_k = 17300
02:41:12 Annoy recall = 100%
02:41:24 Commencing smooth kNN distance calibration using 1 thread
02:41:47 Initializing from normalized Laplacian + noise
02:41:47 Commencing optimization for 500 epochs, with 230050 positive edges
02:42:04 Optimization finished
[1] "173 0.19"02:42:04 UMAP embedding parameters a = 1.292 b = 0.9921
02:42:04 Read 1203 rows and found 38 numeric columns
02:42:04 Using Annoy for neighbor search, n_neighbors = 173
02:42:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:42:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876747917e
02:42:05 Searching Annoy index using 1 thread, search_k = 17300
02:42:06 Annoy recall = 100%
02:42:18 Commencing smooth kNN distance calibration using 1 thread
02:42:41 Initializing from normalized Laplacian + noise
02:42:42 Commencing optimization for 500 epochs, with 230050 positive edges
02:42:58 Optimization finished
[1] "173 0.2"02:42:58 UMAP embedding parameters a = 1.262 b = 1.003
02:42:58 Read 1203 rows and found 38 numeric columns
02:42:58 Using Annoy for neighbor search, n_neighbors = 173
02:42:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:42:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876eaa7da9
02:42:59 Searching Annoy index using 1 thread, search_k = 17300
02:43:00 Annoy recall = 100%
02:43:12 Commencing smooth kNN distance calibration using 1 thread
02:43:35 Initializing from normalized Laplacian + noise
02:43:36 Commencing optimization for 500 epochs, with 230050 positive edges
02:43:52 Optimization finished
[1] "174 0"02:43:52 UMAP embedding parameters a = 1.933 b = 0.7905
02:43:52 Read 1203 rows and found 38 numeric columns
02:43:52 Using Annoy for neighbor search, n_neighbors = 174
02:43:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:43:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875926df2
02:43:53 Searching Annoy index using 1 thread, search_k = 17400
02:43:54 Annoy recall = 100%
02:44:06 Commencing smooth kNN distance calibration using 1 thread
02:44:29 Initializing from normalized Laplacian + noise
02:44:30 Commencing optimization for 500 epochs, with 231254 positive edges
02:44:46 Optimization finished
[1] "174 0.01"02:44:46 UMAP embedding parameters a = 1.896 b = 0.8006
02:44:46 Read 1203 rows and found 38 numeric columns
02:44:46 Using Annoy for neighbor search, n_neighbors = 174
02:44:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:44:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b05be40
02:44:47 Searching Annoy index using 1 thread, search_k = 17400
02:44:48 Annoy recall = 100%
02:45:00 Commencing smooth kNN distance calibration using 1 thread
02:45:23 Initializing from normalized Laplacian + noise
02:45:24 Commencing optimization for 500 epochs, with 231254 positive edges
02:45:40 Optimization finished
[1] "174 0.02"02:45:40 UMAP embedding parameters a = 1.859 b = 0.8109
02:45:40 Read 1203 rows and found 38 numeric columns
02:45:40 Using Annoy for neighbor search, n_neighbors = 174
02:45:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:45:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762de9d12
02:45:41 Searching Annoy index using 1 thread, search_k = 17400
02:45:42 Annoy recall = 100%
02:45:54 Commencing smooth kNN distance calibration using 1 thread
02:46:17 Initializing from normalized Laplacian + noise
02:46:18 Commencing optimization for 500 epochs, with 231254 positive edges
02:46:34 Optimization finished
[1] "174 0.03"02:46:34 UMAP embedding parameters a = 1.822 b = 0.8212
02:46:34 Read 1203 rows and found 38 numeric columns
02:46:34 Using Annoy for neighbor search, n_neighbors = 174
02:46:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:46:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87482a8b00
02:46:35 Searching Annoy index using 1 thread, search_k = 17400
02:46:36 Annoy recall = 100%
02:46:48 Commencing smooth kNN distance calibration using 1 thread
02:47:11 Initializing from normalized Laplacian + noise
02:47:12 Commencing optimization for 500 epochs, with 231254 positive edges
02:47:28 Optimization finished
[1] "174 0.04"02:47:28 UMAP embedding parameters a = 1.786 b = 0.8316
02:47:28 Read 1203 rows and found 38 numeric columns
02:47:28 Using Annoy for neighbor search, n_neighbors = 174
02:47:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:47:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874edbc1e1
02:47:29 Searching Annoy index using 1 thread, search_k = 17400
02:47:30 Annoy recall = 100%
02:47:42 Commencing smooth kNN distance calibration using 1 thread
02:48:06 Initializing from normalized Laplacian + noise
02:48:06 Commencing optimization for 500 epochs, with 231254 positive edges
02:48:22 Optimization finished
[1] "174 0.05"02:48:22 UMAP embedding parameters a = 1.75 b = 0.8421
02:48:22 Read 1203 rows and found 38 numeric columns
02:48:22 Using Annoy for neighbor search, n_neighbors = 174
02:48:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:48:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875d229fd4
02:48:23 Searching Annoy index using 1 thread, search_k = 17400
02:48:24 Annoy recall = 100%
02:48:36 Commencing smooth kNN distance calibration using 1 thread
02:49:00 Initializing from normalized Laplacian + noise
02:49:00 Commencing optimization for 500 epochs, with 231254 positive edges
02:49:16 Optimization finished
[1] "174 0.06"02:49:16 UMAP embedding parameters a = 1.715 b = 0.8526
02:49:16 Read 1203 rows and found 38 numeric columns
02:49:16 Using Annoy for neighbor search, n_neighbors = 174
02:49:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:49:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724073e2e
02:49:17 Searching Annoy index using 1 thread, search_k = 17400
02:49:18 Annoy recall = 100%
02:49:30 Commencing smooth kNN distance calibration using 1 thread
02:49:54 Initializing from normalized Laplacian + noise
02:49:54 Commencing optimization for 500 epochs, with 231254 positive edges
02:50:10 Optimization finished
[1] "174 0.07"02:50:10 UMAP embedding parameters a = 1.68 b = 0.8631
02:50:10 Read 1203 rows and found 38 numeric columns
02:50:10 Using Annoy for neighbor search, n_neighbors = 174
02:50:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:50:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87751e74a2
02:50:11 Searching Annoy index using 1 thread, search_k = 17400
02:50:13 Annoy recall = 100%
02:50:24 Commencing smooth kNN distance calibration using 1 thread
02:50:48 Initializing from normalized Laplacian + noise
02:50:48 Commencing optimization for 500 epochs, with 231254 positive edges
02:51:04 Optimization finished
[1] "174 0.08"02:51:05 UMAP embedding parameters a = 1.645 b = 0.8737
02:51:05 Read 1203 rows and found 38 numeric columns
02:51:05 Using Annoy for neighbor search, n_neighbors = 174
02:51:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:51:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876870bc2b
02:51:06 Searching Annoy index using 1 thread, search_k = 17400
02:51:07 Annoy recall = 100%
02:51:18 Commencing smooth kNN distance calibration using 1 thread
02:51:42 Initializing from normalized Laplacian + noise
02:51:42 Commencing optimization for 500 epochs, with 231254 positive edges
02:51:58 Optimization finished
[1] "174 0.09"02:51:59 UMAP embedding parameters a = 1.611 b = 0.8844
02:51:59 Read 1203 rows and found 38 numeric columns
02:51:59 Using Annoy for neighbor search, n_neighbors = 174
02:51:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:52:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87518d458f
02:52:00 Searching Annoy index using 1 thread, search_k = 17400
02:52:01 Annoy recall = 100%
02:52:13 Commencing smooth kNN distance calibration using 1 thread
02:52:36 Initializing from normalized Laplacian + noise
02:52:36 Commencing optimization for 500 epochs, with 231254 positive edges
02:52:53 Optimization finished
[1] "174 0.1"02:52:53 UMAP embedding parameters a = 1.577 b = 0.8951
02:52:53 Read 1203 rows and found 38 numeric columns
02:52:53 Using Annoy for neighbor search, n_neighbors = 174
02:52:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:52:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dba5ab
02:52:54 Searching Annoy index using 1 thread, search_k = 17400
02:52:55 Annoy recall = 100%
02:53:07 Commencing smooth kNN distance calibration using 1 thread
02:53:30 Initializing from normalized Laplacian + noise
02:53:30 Commencing optimization for 500 epochs, with 231254 positive edges
02:53:47 Optimization finished
[1] "174 0.11"02:53:47 UMAP embedding parameters a = 1.544 b = 0.9058
02:53:47 Read 1203 rows and found 38 numeric columns
02:53:47 Using Annoy for neighbor search, n_neighbors = 174
02:53:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:53:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87576c83da
02:53:48 Searching Annoy index using 1 thread, search_k = 17400
02:53:49 Annoy recall = 100%
02:54:01 Commencing smooth kNN distance calibration using 1 thread
02:54:25 Initializing from normalized Laplacian + noise
02:54:25 Commencing optimization for 500 epochs, with 231254 positive edges
02:54:41 Optimization finished
[1] "174 0.12"02:54:41 UMAP embedding parameters a = 1.51 b = 0.9165
02:54:41 Read 1203 rows and found 38 numeric columns
02:54:41 Using Annoy for neighbor search, n_neighbors = 174
02:54:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:54:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87144fc62a
02:54:42 Searching Annoy index using 1 thread, search_k = 17400
02:54:43 Annoy recall = 100%
02:54:55 Commencing smooth kNN distance calibration using 1 thread
02:55:19 Initializing from normalized Laplacian + noise
02:55:19 Commencing optimization for 500 epochs, with 231254 positive edges
02:55:35 Optimization finished
[1] "174 0.13"02:55:35 UMAP embedding parameters a = 1.478 b = 0.9272
02:55:35 Read 1203 rows and found 38 numeric columns
02:55:35 Using Annoy for neighbor search, n_neighbors = 174
02:55:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:55:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87362bb3b
02:55:36 Searching Annoy index using 1 thread, search_k = 17400
02:55:37 Annoy recall = 100%
02:55:49 Commencing smooth kNN distance calibration using 1 thread
02:56:13 Initializing from normalized Laplacian + noise
02:56:13 Commencing optimization for 500 epochs, with 231254 positive edges
02:56:29 Optimization finished
[1] "174 0.14"02:56:30 UMAP embedding parameters a = 1.446 b = 0.938
02:56:30 Read 1203 rows and found 38 numeric columns
02:56:30 Using Annoy for neighbor search, n_neighbors = 174
02:56:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:56:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f0cd40c
02:56:31 Searching Annoy index using 1 thread, search_k = 17400
02:56:32 Annoy recall = 100%
02:56:43 Commencing smooth kNN distance calibration using 1 thread
02:57:07 Initializing from normalized Laplacian + noise
02:57:07 Commencing optimization for 500 epochs, with 231254 positive edges
02:57:24 Optimization finished
[1] "174 0.15"02:57:24 UMAP embedding parameters a = 1.414 b = 0.9488
02:57:24 Read 1203 rows and found 38 numeric columns
02:57:24 Using Annoy for neighbor search, n_neighbors = 174
02:57:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:57:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87eadfbc
02:57:25 Searching Annoy index using 1 thread, search_k = 17400
02:57:26 Annoy recall = 100%
02:57:38 Commencing smooth kNN distance calibration using 1 thread
02:58:01 Initializing from normalized Laplacian + noise
02:58:02 Commencing optimization for 500 epochs, with 231254 positive edges
02:58:18 Optimization finished
[1] "174 0.16"02:58:18 UMAP embedding parameters a = 1.383 b = 0.9596
02:58:18 Read 1203 rows and found 38 numeric columns
02:58:18 Using Annoy for neighbor search, n_neighbors = 174
02:58:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:58:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e429586
02:58:19 Searching Annoy index using 1 thread, search_k = 17400
02:58:20 Annoy recall = 100%
02:58:32 Commencing smooth kNN distance calibration using 1 thread
02:58:56 Initializing from normalized Laplacian + noise
02:58:56 Commencing optimization for 500 epochs, with 231254 positive edges
02:59:12 Optimization finished
[1] "174 0.17"02:59:12 UMAP embedding parameters a = 1.352 b = 0.9704
02:59:12 Read 1203 rows and found 38 numeric columns
02:59:12 Using Annoy for neighbor search, n_neighbors = 174
02:59:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
02:59:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87269e25d6
02:59:13 Searching Annoy index using 1 thread, search_k = 17400
02:59:14 Annoy recall = 100%
02:59:26 Commencing smooth kNN distance calibration using 1 thread
02:59:50 Initializing from normalized Laplacian + noise
02:59:50 Commencing optimization for 500 epochs, with 231254 positive edges
03:00:06 Optimization finished
[1] "174 0.18"03:00:06 UMAP embedding parameters a = 1.321 b = 0.9813
03:00:06 Read 1203 rows and found 38 numeric columns
03:00:06 Using Annoy for neighbor search, n_neighbors = 174
03:00:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:00:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766feca6a
03:00:07 Searching Annoy index using 1 thread, search_k = 17400
03:00:09 Annoy recall = 100%
03:00:20 Commencing smooth kNN distance calibration using 1 thread
03:00:44 Initializing from normalized Laplacian + noise
03:00:44 Commencing optimization for 500 epochs, with 231254 positive edges
03:01:00 Optimization finished
[1] "174 0.19"03:01:01 UMAP embedding parameters a = 1.292 b = 0.9921
03:01:01 Read 1203 rows and found 38 numeric columns
03:01:01 Using Annoy for neighbor search, n_neighbors = 174
03:01:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:01:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d9f4957
03:01:02 Searching Annoy index using 1 thread, search_k = 17400
03:01:03 Annoy recall = 100%
03:01:15 Commencing smooth kNN distance calibration using 1 thread
03:01:39 Initializing from normalized Laplacian + noise
03:01:39 Commencing optimization for 500 epochs, with 231254 positive edges
03:01:55 Optimization finished
[1] "174 0.2"03:01:55 UMAP embedding parameters a = 1.262 b = 1.003
03:01:55 Read 1203 rows and found 38 numeric columns
03:01:55 Using Annoy for neighbor search, n_neighbors = 174
03:01:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:01:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721d1a6be
03:01:56 Searching Annoy index using 1 thread, search_k = 17400
03:01:57 Annoy recall = 100%
03:02:09 Commencing smooth kNN distance calibration using 1 thread
03:02:33 Initializing from normalized Laplacian + noise
03:02:33 Commencing optimization for 500 epochs, with 231254 positive edges
03:02:49 Optimization finished
[1] "175 0"03:02:50 UMAP embedding parameters a = 1.933 b = 0.7905
03:02:50 Read 1203 rows and found 38 numeric columns
03:02:50 Using Annoy for neighbor search, n_neighbors = 175
03:02:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:02:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e4cff20
03:02:51 Searching Annoy index using 1 thread, search_k = 17500
03:02:52 Annoy recall = 100%
03:03:03 Commencing smooth kNN distance calibration using 1 thread
03:03:27 Initializing from normalized Laplacian + noise
03:03:27 Commencing optimization for 500 epochs, with 232420 positive edges
03:03:44 Optimization finished
[1] "175 0.01"03:03:44 UMAP embedding parameters a = 1.896 b = 0.8006
03:03:44 Read 1203 rows and found 38 numeric columns
03:03:44 Using Annoy for neighbor search, n_neighbors = 175
03:03:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:03:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873f2bad22
03:03:45 Searching Annoy index using 1 thread, search_k = 17500
03:03:46 Annoy recall = 100%
03:03:58 Commencing smooth kNN distance calibration using 1 thread
03:04:21 Initializing from normalized Laplacian + noise
03:04:22 Commencing optimization for 500 epochs, with 232420 positive edges
03:04:38 Optimization finished
[1] "175 0.02"03:04:38 UMAP embedding parameters a = 1.859 b = 0.8109
03:04:38 Read 1203 rows and found 38 numeric columns
03:04:38 Using Annoy for neighbor search, n_neighbors = 175
03:04:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:04:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d4cab7d
03:04:39 Searching Annoy index using 1 thread, search_k = 17500
03:04:40 Annoy recall = 100%
03:04:53 Commencing smooth kNN distance calibration using 1 thread
03:05:17 Initializing from normalized Laplacian + noise
03:05:17 Commencing optimization for 500 epochs, with 232420 positive edges
03:05:34 Optimization finished
[1] "175 0.03"03:05:34 UMAP embedding parameters a = 1.822 b = 0.8212
03:05:34 Read 1203 rows and found 38 numeric columns
03:05:34 Using Annoy for neighbor search, n_neighbors = 175
03:05:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:05:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769d81c10
03:05:35 Searching Annoy index using 1 thread, search_k = 17500
03:05:36 Annoy recall = 100%
03:05:48 Commencing smooth kNN distance calibration using 1 thread
03:06:13 Initializing from normalized Laplacian + noise
03:06:13 Commencing optimization for 500 epochs, with 232420 positive edges
03:06:30 Optimization finished
[1] "175 0.04"03:06:30 UMAP embedding parameters a = 1.786 b = 0.8316
03:06:30 Read 1203 rows and found 38 numeric columns
03:06:30 Using Annoy for neighbor search, n_neighbors = 175
03:06:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:06:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87731056d6
03:06:31 Searching Annoy index using 1 thread, search_k = 17500
03:06:32 Annoy recall = 100%
03:06:44 Commencing smooth kNN distance calibration using 1 thread
03:07:09 Initializing from normalized Laplacian + noise
03:07:09 Commencing optimization for 500 epochs, with 232420 positive edges
03:07:25 Optimization finished
[1] "175 0.05"03:07:26 UMAP embedding parameters a = 1.75 b = 0.8421
03:07:26 Read 1203 rows and found 38 numeric columns
03:07:26 Using Annoy for neighbor search, n_neighbors = 175
03:07:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:07:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f8cb5b3
03:07:27 Searching Annoy index using 1 thread, search_k = 17500
03:07:28 Annoy recall = 100%
03:07:40 Commencing smooth kNN distance calibration using 1 thread
03:08:05 Initializing from normalized Laplacian + noise
03:08:05 Commencing optimization for 500 epochs, with 232420 positive edges
03:08:21 Optimization finished
[1] "175 0.06"03:08:22 UMAP embedding parameters a = 1.715 b = 0.8526
03:08:22 Read 1203 rows and found 38 numeric columns
03:08:22 Using Annoy for neighbor search, n_neighbors = 175
03:08:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:08:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e1a199
03:08:23 Searching Annoy index using 1 thread, search_k = 17500
03:08:24 Annoy recall = 100%
03:08:36 Commencing smooth kNN distance calibration using 1 thread
03:09:01 Initializing from normalized Laplacian + noise
03:09:01 Commencing optimization for 500 epochs, with 232420 positive edges
03:09:18 Optimization finished
[1] "175 0.07"03:09:18 UMAP embedding parameters a = 1.68 b = 0.8631
03:09:18 Read 1203 rows and found 38 numeric columns
03:09:18 Using Annoy for neighbor search, n_neighbors = 175
03:09:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:09:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87389ca667
03:09:19 Searching Annoy index using 1 thread, search_k = 17500
03:09:20 Annoy recall = 100%
03:09:32 Commencing smooth kNN distance calibration using 1 thread
03:09:57 Initializing from normalized Laplacian + noise
03:09:57 Commencing optimization for 500 epochs, with 232420 positive edges
03:10:13 Optimization finished
[1] "175 0.08"03:10:14 UMAP embedding parameters a = 1.645 b = 0.8737
03:10:14 Read 1203 rows and found 38 numeric columns
03:10:14 Using Annoy for neighbor search, n_neighbors = 175
03:10:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:10:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776d44731
03:10:15 Searching Annoy index using 1 thread, search_k = 17500
03:10:16 Annoy recall = 100%
03:10:28 Commencing smooth kNN distance calibration using 1 thread
03:10:53 Initializing from normalized Laplacian + noise
03:10:53 Commencing optimization for 500 epochs, with 232420 positive edges
03:11:09 Optimization finished
[1] "175 0.09"03:11:10 UMAP embedding parameters a = 1.611 b = 0.8844
03:11:10 Read 1203 rows and found 38 numeric columns
03:11:10 Using Annoy for neighbor search, n_neighbors = 175
03:11:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:11:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f8c1f43
03:11:11 Searching Annoy index using 1 thread, search_k = 17500
03:11:12 Annoy recall = 100%
03:11:24 Commencing smooth kNN distance calibration using 1 thread
03:11:49 Initializing from normalized Laplacian + noise
03:11:49 Commencing optimization for 500 epochs, with 232420 positive edges
03:12:05 Optimization finished
[1] "175 0.1"03:12:06 UMAP embedding parameters a = 1.577 b = 0.8951
03:12:06 Read 1203 rows and found 38 numeric columns
03:12:06 Using Annoy for neighbor search, n_neighbors = 175
03:12:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:12:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e2f1459
03:12:07 Searching Annoy index using 1 thread, search_k = 17500
03:12:08 Annoy recall = 100%
03:12:20 Commencing smooth kNN distance calibration using 1 thread
03:12:45 Initializing from normalized Laplacian + noise
03:12:45 Commencing optimization for 500 epochs, with 232420 positive edges
03:13:01 Optimization finished
[1] "175 0.11"03:13:02 UMAP embedding parameters a = 1.544 b = 0.9058
03:13:02 Read 1203 rows and found 38 numeric columns
03:13:02 Using Annoy for neighbor search, n_neighbors = 175
03:13:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:13:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731da0571
03:13:03 Searching Annoy index using 1 thread, search_k = 17500
03:13:04 Annoy recall = 100%
03:13:16 Commencing smooth kNN distance calibration using 1 thread
03:13:41 Initializing from normalized Laplacian + noise
03:13:41 Commencing optimization for 500 epochs, with 232420 positive edges
03:13:57 Optimization finished
[1] "175 0.12"03:13:58 UMAP embedding parameters a = 1.51 b = 0.9165
03:13:58 Read 1203 rows and found 38 numeric columns
03:13:58 Using Annoy for neighbor search, n_neighbors = 175
03:13:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:13:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87526abc55
03:13:59 Searching Annoy index using 1 thread, search_k = 17500
03:14:00 Annoy recall = 100%
03:14:12 Commencing smooth kNN distance calibration using 1 thread
03:14:37 Initializing from normalized Laplacian + noise
03:14:37 Commencing optimization for 500 epochs, with 232420 positive edges
03:14:53 Optimization finished
[1] "175 0.13"03:14:54 UMAP embedding parameters a = 1.478 b = 0.9272
03:14:54 Read 1203 rows and found 38 numeric columns
03:14:54 Using Annoy for neighbor search, n_neighbors = 175
03:14:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:14:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876599f59
03:14:55 Searching Annoy index using 1 thread, search_k = 17500
03:14:56 Annoy recall = 100%
03:15:08 Commencing smooth kNN distance calibration using 1 thread
03:15:33 Initializing from normalized Laplacian + noise
03:15:33 Commencing optimization for 500 epochs, with 232420 positive edges
03:15:49 Optimization finished
[1] "175 0.14"03:15:50 UMAP embedding parameters a = 1.446 b = 0.938
03:15:50 Read 1203 rows and found 38 numeric columns
03:15:50 Using Annoy for neighbor search, n_neighbors = 175
03:15:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:15:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b5c752
03:15:51 Searching Annoy index using 1 thread, search_k = 17500
03:15:52 Annoy recall = 100%
03:16:04 Commencing smooth kNN distance calibration using 1 thread
03:16:29 Initializing from normalized Laplacian + noise
03:16:29 Commencing optimization for 500 epochs, with 232420 positive edges
03:16:46 Optimization finished
[1] "175 0.15"03:16:46 UMAP embedding parameters a = 1.414 b = 0.9488
03:16:46 Read 1203 rows and found 38 numeric columns
03:16:46 Using Annoy for neighbor search, n_neighbors = 175
03:16:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:16:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f8d5c29
03:16:47 Searching Annoy index using 1 thread, search_k = 17500
03:16:48 Annoy recall = 100%
03:17:00 Commencing smooth kNN distance calibration using 1 thread
03:17:25 Initializing from normalized Laplacian + noise
03:17:25 Commencing optimization for 500 epochs, with 232420 positive edges
03:17:42 Optimization finished
[1] "175 0.16"03:17:42 UMAP embedding parameters a = 1.383 b = 0.9596
03:17:42 Read 1203 rows and found 38 numeric columns
03:17:42 Using Annoy for neighbor search, n_neighbors = 175
03:17:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:17:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a60dd88
03:17:43 Searching Annoy index using 1 thread, search_k = 17500
03:17:44 Annoy recall = 100%
03:17:56 Commencing smooth kNN distance calibration using 1 thread
03:18:21 Initializing from normalized Laplacian + noise
03:18:21 Commencing optimization for 500 epochs, with 232420 positive edges
03:18:38 Optimization finished
[1] "175 0.17"03:18:38 UMAP embedding parameters a = 1.352 b = 0.9704
03:18:38 Read 1203 rows and found 38 numeric columns
03:18:38 Using Annoy for neighbor search, n_neighbors = 175
03:18:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:18:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8775d43bf5
03:18:39 Searching Annoy index using 1 thread, search_k = 17500
03:18:40 Annoy recall = 100%
03:18:53 Commencing smooth kNN distance calibration using 1 thread
03:19:17 Initializing from normalized Laplacian + noise
03:19:17 Commencing optimization for 500 epochs, with 232420 positive edges
03:19:34 Optimization finished
[1] "175 0.18"03:19:34 UMAP embedding parameters a = 1.321 b = 0.9813
03:19:34 Read 1203 rows and found 38 numeric columns
03:19:34 Using Annoy for neighbor search, n_neighbors = 175
03:19:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:19:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8717fe1855
03:19:35 Searching Annoy index using 1 thread, search_k = 17500
03:19:36 Annoy recall = 100%
03:19:49 Commencing smooth kNN distance calibration using 1 thread
03:20:13 Initializing from normalized Laplacian + noise
03:20:13 Commencing optimization for 500 epochs, with 232420 positive edges
03:20:30 Optimization finished
[1] "175 0.19"03:20:30 UMAP embedding parameters a = 1.292 b = 0.9921
03:20:30 Read 1203 rows and found 38 numeric columns
03:20:30 Using Annoy for neighbor search, n_neighbors = 175
03:20:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:20:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bee2317
03:20:31 Searching Annoy index using 1 thread, search_k = 17500
03:20:32 Annoy recall = 100%
03:20:45 Commencing smooth kNN distance calibration using 1 thread
03:21:09 Initializing from normalized Laplacian + noise
03:21:09 Commencing optimization for 500 epochs, with 232420 positive edges
03:21:26 Optimization finished
[1] "175 0.2"03:21:26 UMAP embedding parameters a = 1.262 b = 1.003
03:21:26 Read 1203 rows and found 38 numeric columns
03:21:26 Using Annoy for neighbor search, n_neighbors = 175
03:21:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:21:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778afe1a0
03:21:27 Searching Annoy index using 1 thread, search_k = 17500
03:21:28 Annoy recall = 100%
03:21:41 Commencing smooth kNN distance calibration using 1 thread
03:22:06 Initializing from normalized Laplacian + noise
03:22:06 Commencing optimization for 500 epochs, with 232420 positive edges
03:22:22 Optimization finished
[1] "176 0"03:22:22 UMAP embedding parameters a = 1.933 b = 0.7905
03:22:22 Read 1203 rows and found 38 numeric columns
03:22:22 Using Annoy for neighbor search, n_neighbors = 176
03:22:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:22:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f6a9c2f
03:22:23 Searching Annoy index using 1 thread, search_k = 17600
03:22:25 Annoy recall = 100%
03:22:37 Commencing smooth kNN distance calibration using 1 thread
03:23:02 Initializing from normalized Laplacian + noise
03:23:02 Commencing optimization for 500 epochs, with 233634 positive edges
03:23:18 Optimization finished
[1] "176 0.01"03:23:19 UMAP embedding parameters a = 1.896 b = 0.8006
03:23:19 Read 1203 rows and found 38 numeric columns
03:23:19 Using Annoy for neighbor search, n_neighbors = 176
03:23:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:23:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87103de941
03:23:20 Searching Annoy index using 1 thread, search_k = 17600
03:23:21 Annoy recall = 100%
03:23:33 Commencing smooth kNN distance calibration using 1 thread
03:23:58 Initializing from normalized Laplacian + noise
03:23:58 Commencing optimization for 500 epochs, with 233634 positive edges
03:24:15 Optimization finished
[1] "176 0.02"03:24:15 UMAP embedding parameters a = 1.859 b = 0.8109
03:24:15 Read 1203 rows and found 38 numeric columns
03:24:15 Using Annoy for neighbor search, n_neighbors = 176
03:24:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:24:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c129cdb
03:24:16 Searching Annoy index using 1 thread, search_k = 17600
03:24:17 Annoy recall = 100%
03:24:29 Commencing smooth kNN distance calibration using 1 thread
03:24:54 Initializing from normalized Laplacian + noise
03:24:54 Commencing optimization for 500 epochs, with 233634 positive edges
03:25:11 Optimization finished
[1] "176 0.03"03:25:11 UMAP embedding parameters a = 1.822 b = 0.8212
03:25:11 Read 1203 rows and found 38 numeric columns
03:25:11 Using Annoy for neighbor search, n_neighbors = 176
03:25:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:25:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e77703b
03:25:12 Searching Annoy index using 1 thread, search_k = 17600
03:25:13 Annoy recall = 100%
03:25:25 Commencing smooth kNN distance calibration using 1 thread
03:25:50 Initializing from normalized Laplacian + noise
03:25:50 Commencing optimization for 500 epochs, with 233634 positive edges
03:26:07 Optimization finished
[1] "176 0.04"03:26:07 UMAP embedding parameters a = 1.786 b = 0.8316
03:26:07 Read 1203 rows and found 38 numeric columns
03:26:07 Using Annoy for neighbor search, n_neighbors = 176
03:26:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:26:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871128c8fd
03:26:08 Searching Annoy index using 1 thread, search_k = 17600
03:26:10 Annoy recall = 100%
03:26:22 Commencing smooth kNN distance calibration using 1 thread
03:26:46 Initializing from normalized Laplacian + noise
03:26:47 Commencing optimization for 500 epochs, with 233634 positive edges
03:27:03 Optimization finished
[1] "176 0.05"03:27:04 UMAP embedding parameters a = 1.75 b = 0.8421
03:27:04 Read 1203 rows and found 38 numeric columns
03:27:04 Using Annoy for neighbor search, n_neighbors = 176
03:27:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:27:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a553261
03:27:05 Searching Annoy index using 1 thread, search_k = 17600
03:27:06 Annoy recall = 100%
03:27:18 Commencing smooth kNN distance calibration using 1 thread
03:27:43 Initializing from normalized Laplacian + noise
03:27:43 Commencing optimization for 500 epochs, with 233634 positive edges
03:28:00 Optimization finished
[1] "176 0.06"03:28:00 UMAP embedding parameters a = 1.715 b = 0.8526
03:28:00 Read 1203 rows and found 38 numeric columns
03:28:00 Using Annoy for neighbor search, n_neighbors = 176
03:28:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:28:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8765159612
03:28:01 Searching Annoy index using 1 thread, search_k = 17600
03:28:02 Annoy recall = 100%
03:28:14 Commencing smooth kNN distance calibration using 1 thread
03:28:39 Initializing from normalized Laplacian + noise
03:28:39 Commencing optimization for 500 epochs, with 233634 positive edges
03:28:56 Optimization finished
[1] "176 0.07"03:28:56 UMAP embedding parameters a = 1.68 b = 0.8631
03:28:56 Read 1203 rows and found 38 numeric columns
03:28:56 Using Annoy for neighbor search, n_neighbors = 176
03:28:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:28:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778279368
03:28:57 Searching Annoy index using 1 thread, search_k = 17600
03:28:58 Annoy recall = 100%
03:29:11 Commencing smooth kNN distance calibration using 1 thread
03:29:35 Initializing from normalized Laplacian + noise
03:29:35 Commencing optimization for 500 epochs, with 233634 positive edges
03:29:52 Optimization finished
[1] "176 0.08"03:29:52 UMAP embedding parameters a = 1.645 b = 0.8737
03:29:52 Read 1203 rows and found 38 numeric columns
03:29:52 Using Annoy for neighbor search, n_neighbors = 176
03:29:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:29:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757f47bb8
03:29:53 Searching Annoy index using 1 thread, search_k = 17600
03:29:54 Annoy recall = 100%
03:30:07 Commencing smooth kNN distance calibration using 1 thread
03:30:32 Initializing from normalized Laplacian + noise
03:30:32 Commencing optimization for 500 epochs, with 233634 positive edges
03:30:48 Optimization finished
[1] "176 0.09"03:30:49 UMAP embedding parameters a = 1.611 b = 0.8844
03:30:49 Read 1203 rows and found 38 numeric columns
03:30:49 Using Annoy for neighbor search, n_neighbors = 176
03:30:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:30:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e73cd0
03:30:50 Searching Annoy index using 1 thread, search_k = 17600
03:30:51 Annoy recall = 100%
03:31:03 Commencing smooth kNN distance calibration using 1 thread
03:31:28 Initializing from normalized Laplacian + noise
03:31:28 Commencing optimization for 500 epochs, with 233634 positive edges
03:31:45 Optimization finished
[1] "176 0.1"03:31:45 UMAP embedding parameters a = 1.577 b = 0.8951
03:31:45 Read 1203 rows and found 38 numeric columns
03:31:45 Using Annoy for neighbor search, n_neighbors = 176
03:31:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:31:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756749288
03:31:46 Searching Annoy index using 1 thread, search_k = 17600
03:31:47 Annoy recall = 100%
03:31:59 Commencing smooth kNN distance calibration using 1 thread
03:32:24 Initializing from normalized Laplacian + noise
03:32:24 Commencing optimization for 500 epochs, with 233634 positive edges
03:32:41 Optimization finished
[1] "176 0.11"03:32:41 UMAP embedding parameters a = 1.544 b = 0.9058
03:32:41 Read 1203 rows and found 38 numeric columns
03:32:41 Using Annoy for neighbor search, n_neighbors = 176
03:32:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:32:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87172028da
03:32:42 Searching Annoy index using 1 thread, search_k = 17600
03:32:44 Annoy recall = 100%
03:32:56 Commencing smooth kNN distance calibration using 1 thread
03:33:20 Initializing from normalized Laplacian + noise
03:33:21 Commencing optimization for 500 epochs, with 233634 positive edges
03:33:37 Optimization finished
[1] "176 0.12"03:33:38 UMAP embedding parameters a = 1.51 b = 0.9165
03:33:38 Read 1203 rows and found 38 numeric columns
03:33:38 Using Annoy for neighbor search, n_neighbors = 176
03:33:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:33:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875433e84d
03:33:39 Searching Annoy index using 1 thread, search_k = 17600
03:33:40 Annoy recall = 100%
03:33:52 Commencing smooth kNN distance calibration using 1 thread
03:34:17 Initializing from normalized Laplacian + noise
03:34:17 Commencing optimization for 500 epochs, with 233634 positive edges
03:34:34 Optimization finished
[1] "176 0.13"03:34:34 UMAP embedding parameters a = 1.478 b = 0.9272
03:34:34 Read 1203 rows and found 38 numeric columns
03:34:34 Using Annoy for neighbor search, n_neighbors = 176
03:34:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:34:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87404cae98
03:34:35 Searching Annoy index using 1 thread, search_k = 17600
03:34:36 Annoy recall = 100%
03:34:49 Commencing smooth kNN distance calibration using 1 thread
03:35:13 Initializing from normalized Laplacian + noise
03:35:13 Commencing optimization for 500 epochs, with 233634 positive edges
03:35:30 Optimization finished
[1] "176 0.14"03:35:30 UMAP embedding parameters a = 1.446 b = 0.938
03:35:30 Read 1203 rows and found 38 numeric columns
03:35:30 Using Annoy for neighbor search, n_neighbors = 176
03:35:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:35:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a307fb0
03:35:31 Searching Annoy index using 1 thread, search_k = 17600
03:35:32 Annoy recall = 100%
03:35:45 Commencing smooth kNN distance calibration using 1 thread
03:36:09 Initializing from normalized Laplacian + noise
03:36:10 Commencing optimization for 500 epochs, with 233634 positive edges
03:36:26 Optimization finished
[1] "176 0.15"03:36:27 UMAP embedding parameters a = 1.414 b = 0.9488
03:36:27 Read 1203 rows and found 38 numeric columns
03:36:27 Using Annoy for neighbor search, n_neighbors = 176
03:36:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:36:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763c09e00
03:36:28 Searching Annoy index using 1 thread, search_k = 17600
03:36:29 Annoy recall = 100%
03:36:41 Commencing smooth kNN distance calibration using 1 thread
03:37:06 Initializing from normalized Laplacian + noise
03:37:06 Commencing optimization for 500 epochs, with 233634 positive edges
03:37:23 Optimization finished
[1] "176 0.16"03:37:23 UMAP embedding parameters a = 1.383 b = 0.9596
03:37:23 Read 1203 rows and found 38 numeric columns
03:37:23 Using Annoy for neighbor search, n_neighbors = 176
03:37:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:37:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87412e5031
03:37:24 Searching Annoy index using 1 thread, search_k = 17600
03:37:25 Annoy recall = 100%
03:37:38 Commencing smooth kNN distance calibration using 1 thread
03:38:02 Initializing from normalized Laplacian + noise
03:38:03 Commencing optimization for 500 epochs, with 233634 positive edges
03:38:19 Optimization finished
[1] "176 0.17"03:38:19 UMAP embedding parameters a = 1.352 b = 0.9704
03:38:19 Read 1203 rows and found 38 numeric columns
03:38:19 Using Annoy for neighbor search, n_neighbors = 176
03:38:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:38:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742cd2617
03:38:20 Searching Annoy index using 1 thread, search_k = 17600
03:38:22 Annoy recall = 100%
03:38:34 Commencing smooth kNN distance calibration using 1 thread
03:38:59 Initializing from normalized Laplacian + noise
03:38:59 Commencing optimization for 500 epochs, with 233634 positive edges
03:39:16 Optimization finished
[1] "176 0.18"03:39:16 UMAP embedding parameters a = 1.321 b = 0.9813
03:39:16 Read 1203 rows and found 38 numeric columns
03:39:16 Using Annoy for neighbor search, n_neighbors = 176
03:39:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:39:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a94e531
03:39:17 Searching Annoy index using 1 thread, search_k = 17600
03:39:18 Annoy recall = 100%
03:39:30 Commencing smooth kNN distance calibration using 1 thread
03:39:55 Initializing from normalized Laplacian + noise
03:39:55 Commencing optimization for 500 epochs, with 233634 positive edges
03:40:12 Optimization finished
[1] "176 0.19"03:40:12 UMAP embedding parameters a = 1.292 b = 0.9921
03:40:12 Read 1203 rows and found 38 numeric columns
03:40:12 Using Annoy for neighbor search, n_neighbors = 176
03:40:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:40:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730ba6f74
03:40:13 Searching Annoy index using 1 thread, search_k = 17600
03:40:15 Annoy recall = 100%
03:40:27 Commencing smooth kNN distance calibration using 1 thread
03:40:52 Initializing from normalized Laplacian + noise
03:40:52 Commencing optimization for 500 epochs, with 233634 positive edges
03:41:09 Optimization finished
[1] "176 0.2"03:41:09 UMAP embedding parameters a = 1.262 b = 1.003
03:41:09 Read 1203 rows and found 38 numeric columns
03:41:09 Using Annoy for neighbor search, n_neighbors = 176
03:41:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:41:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87fc3a71
03:41:10 Searching Annoy index using 1 thread, search_k = 17600
03:41:11 Annoy recall = 100%
03:41:23 Commencing smooth kNN distance calibration using 1 thread
03:41:48 Initializing from normalized Laplacian + noise
03:41:48 Commencing optimization for 500 epochs, with 233634 positive edges
03:42:05 Optimization finished
[1] "177 0"03:42:05 UMAP embedding parameters a = 1.933 b = 0.7905
03:42:05 Read 1203 rows and found 38 numeric columns
03:42:05 Using Annoy for neighbor search, n_neighbors = 177
03:42:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:42:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c6eeaa3
03:42:06 Searching Annoy index using 1 thread, search_k = 17700
03:42:07 Annoy recall = 100%
03:42:20 Commencing smooth kNN distance calibration using 1 thread
03:42:45 Initializing from normalized Laplacian + noise
03:42:45 Commencing optimization for 500 epochs, with 234802 positive edges
03:43:01 Optimization finished
[1] "177 0.01"03:43:02 UMAP embedding parameters a = 1.896 b = 0.8006
03:43:02 Read 1203 rows and found 38 numeric columns
03:43:02 Using Annoy for neighbor search, n_neighbors = 177
03:43:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:43:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873252bca
03:43:03 Searching Annoy index using 1 thread, search_k = 17700
03:43:04 Annoy recall = 100%
03:43:16 Commencing smooth kNN distance calibration using 1 thread
03:43:41 Initializing from normalized Laplacian + noise
03:43:41 Commencing optimization for 500 epochs, with 234802 positive edges
03:43:58 Optimization finished
[1] "177 0.02"03:43:58 UMAP embedding parameters a = 1.859 b = 0.8109
03:43:58 Read 1203 rows and found 38 numeric columns
03:43:58 Using Annoy for neighbor search, n_neighbors = 177
03:43:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:43:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87755d9ca
03:43:59 Searching Annoy index using 1 thread, search_k = 17700
03:44:00 Annoy recall = 100%
03:44:13 Commencing smooth kNN distance calibration using 1 thread
03:44:38 Initializing from normalized Laplacian + noise
03:44:38 Commencing optimization for 500 epochs, with 234802 positive edges
03:44:54 Optimization finished
[1] "177 0.03"03:44:55 UMAP embedding parameters a = 1.822 b = 0.8212
03:44:55 Read 1203 rows and found 38 numeric columns
03:44:55 Using Annoy for neighbor search, n_neighbors = 177
03:44:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:44:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d24b1f5
03:44:56 Searching Annoy index using 1 thread, search_k = 17700
03:44:57 Annoy recall = 100%
03:45:09 Commencing smooth kNN distance calibration using 1 thread
03:45:34 Initializing from normalized Laplacian + noise
03:45:34 Commencing optimization for 500 epochs, with 234802 positive edges
03:45:51 Optimization finished
[1] "177 0.04"03:45:51 UMAP embedding parameters a = 1.786 b = 0.8316
03:45:51 Read 1203 rows and found 38 numeric columns
03:45:51 Using Annoy for neighbor search, n_neighbors = 177
03:45:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:45:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732b287f3
03:45:52 Searching Annoy index using 1 thread, search_k = 17700
03:45:53 Annoy recall = 100%
03:46:06 Commencing smooth kNN distance calibration using 1 thread
03:46:31 Initializing from normalized Laplacian + noise
03:46:31 Commencing optimization for 500 epochs, with 234802 positive edges
03:46:47 Optimization finished
[1] "177 0.05"03:46:48 UMAP embedding parameters a = 1.75 b = 0.8421
03:46:48 Read 1203 rows and found 38 numeric columns
03:46:48 Using Annoy for neighbor search, n_neighbors = 177
03:46:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:46:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731b6b752
03:46:49 Searching Annoy index using 1 thread, search_k = 17700
03:46:50 Annoy recall = 100%
03:47:02 Commencing smooth kNN distance calibration using 1 thread
03:47:27 Initializing from normalized Laplacian + noise
03:47:27 Commencing optimization for 500 epochs, with 234802 positive edges
03:47:44 Optimization finished
[1] "177 0.06"03:47:44 UMAP embedding parameters a = 1.715 b = 0.8526
03:47:44 Read 1203 rows and found 38 numeric columns
03:47:44 Using Annoy for neighbor search, n_neighbors = 177
03:47:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:47:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f8edea
03:47:45 Searching Annoy index using 1 thread, search_k = 17700
03:47:46 Annoy recall = 100%
03:47:59 Commencing smooth kNN distance calibration using 1 thread
03:48:24 Initializing from normalized Laplacian + noise
03:48:24 Commencing optimization for 500 epochs, with 234802 positive edges
03:48:41 Optimization finished
[1] "177 0.07"03:48:41 UMAP embedding parameters a = 1.68 b = 0.8631
03:48:41 Read 1203 rows and found 38 numeric columns
03:48:41 Using Annoy for neighbor search, n_neighbors = 177
03:48:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:48:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ab0a048
03:48:42 Searching Annoy index using 1 thread, search_k = 17700
03:48:43 Annoy recall = 100%
03:48:55 Commencing smooth kNN distance calibration using 1 thread
03:49:20 Initializing from normalized Laplacian + noise
03:49:20 Commencing optimization for 500 epochs, with 234802 positive edges
03:49:37 Optimization finished
[1] "177 0.08"03:49:38 UMAP embedding parameters a = 1.645 b = 0.8737
03:49:38 Read 1203 rows and found 38 numeric columns
03:49:38 Using Annoy for neighbor search, n_neighbors = 177
03:49:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:49:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872da4da69
03:49:39 Searching Annoy index using 1 thread, search_k = 17700
03:49:40 Annoy recall = 100%
03:49:52 Commencing smooth kNN distance calibration using 1 thread
03:50:17 Initializing from normalized Laplacian + noise
03:50:17 Commencing optimization for 500 epochs, with 234802 positive edges
03:50:34 Optimization finished
[1] "177 0.09"03:50:34 UMAP embedding parameters a = 1.611 b = 0.8844
03:50:34 Read 1203 rows and found 38 numeric columns
03:50:34 Using Annoy for neighbor search, n_neighbors = 177
03:50:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:50:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ba8cf8b
03:50:35 Searching Annoy index using 1 thread, search_k = 17700
03:50:36 Annoy recall = 100%
03:50:49 Commencing smooth kNN distance calibration using 1 thread
03:51:13 Initializing from normalized Laplacian + noise
03:51:14 Commencing optimization for 500 epochs, with 234802 positive edges
03:51:30 Optimization finished
[1] "177 0.1"03:51:31 UMAP embedding parameters a = 1.577 b = 0.8951
03:51:31 Read 1203 rows and found 38 numeric columns
03:51:31 Using Annoy for neighbor search, n_neighbors = 177
03:51:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:51:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a1b3c77
03:51:32 Searching Annoy index using 1 thread, search_k = 17700
03:51:33 Annoy recall = 100%
03:51:45 Commencing smooth kNN distance calibration using 1 thread
03:52:10 Initializing from normalized Laplacian + noise
03:52:10 Commencing optimization for 500 epochs, with 234802 positive edges
03:52:27 Optimization finished
[1] "177 0.11"03:52:27 UMAP embedding parameters a = 1.544 b = 0.9058
03:52:27 Read 1203 rows and found 38 numeric columns
03:52:27 Using Annoy for neighbor search, n_neighbors = 177
03:52:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:52:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873de2c3aa
03:52:28 Searching Annoy index using 1 thread, search_k = 17700
03:52:29 Annoy recall = 100%
03:52:42 Commencing smooth kNN distance calibration using 1 thread
03:53:07 Initializing from normalized Laplacian + noise
03:53:07 Commencing optimization for 500 epochs, with 234802 positive edges
03:53:24 Optimization finished
[1] "177 0.12"03:53:24 UMAP embedding parameters a = 1.51 b = 0.9165
03:53:24 Read 1203 rows and found 38 numeric columns
03:53:24 Using Annoy for neighbor search, n_neighbors = 177
03:53:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:53:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777bb6c66
03:53:25 Searching Annoy index using 1 thread, search_k = 17700
03:53:26 Annoy recall = 100%
03:53:38 Commencing smooth kNN distance calibration using 1 thread
03:54:03 Initializing from normalized Laplacian + noise
03:54:04 Commencing optimization for 500 epochs, with 234802 positive edges
03:54:20 Optimization finished
[1] "177 0.13"03:54:20 UMAP embedding parameters a = 1.478 b = 0.9272
03:54:20 Read 1203 rows and found 38 numeric columns
03:54:20 Using Annoy for neighbor search, n_neighbors = 177
03:54:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:54:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877892acb3
03:54:21 Searching Annoy index using 1 thread, search_k = 17700
03:54:23 Annoy recall = 100%
03:54:35 Commencing smooth kNN distance calibration using 1 thread
03:55:00 Initializing from normalized Laplacian + noise
03:55:00 Commencing optimization for 500 epochs, with 234802 positive edges
03:55:17 Optimization finished
[1] "177 0.14"03:55:17 UMAP embedding parameters a = 1.446 b = 0.938
03:55:17 Read 1203 rows and found 38 numeric columns
03:55:17 Using Annoy for neighbor search, n_neighbors = 177
03:55:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:55:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f0b8ca8
03:55:18 Searching Annoy index using 1 thread, search_k = 17700
03:55:19 Annoy recall = 100%
03:55:32 Commencing smooth kNN distance calibration using 1 thread
03:55:57 Initializing from normalized Laplacian + noise
03:55:57 Commencing optimization for 500 epochs, with 234802 positive edges
03:56:14 Optimization finished
[1] "177 0.15"03:56:14 UMAP embedding parameters a = 1.414 b = 0.9488
03:56:14 Read 1203 rows and found 38 numeric columns
03:56:14 Using Annoy for neighbor search, n_neighbors = 177
03:56:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:56:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732109ec8
03:56:15 Searching Annoy index using 1 thread, search_k = 17700
03:56:16 Annoy recall = 100%
03:56:28 Commencing smooth kNN distance calibration using 1 thread
03:56:53 Initializing from normalized Laplacian + noise
03:56:54 Commencing optimization for 500 epochs, with 234802 positive edges
03:57:11 Optimization finished
[1] "177 0.16"03:57:11 UMAP embedding parameters a = 1.383 b = 0.9596
03:57:11 Read 1203 rows and found 38 numeric columns
03:57:11 Using Annoy for neighbor search, n_neighbors = 177
03:57:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:57:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875da842c5
03:57:12 Searching Annoy index using 1 thread, search_k = 17700
03:57:13 Annoy recall = 100%
03:57:25 Commencing smooth kNN distance calibration using 1 thread
03:57:50 Initializing from normalized Laplacian + noise
03:57:50 Commencing optimization for 500 epochs, with 234802 positive edges
03:58:07 Optimization finished
[1] "177 0.17"03:58:07 UMAP embedding parameters a = 1.352 b = 0.9704
03:58:07 Read 1203 rows and found 38 numeric columns
03:58:07 Using Annoy for neighbor search, n_neighbors = 177
03:58:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:58:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747332010
03:58:08 Searching Annoy index using 1 thread, search_k = 17700
03:58:10 Annoy recall = 100%
03:58:22 Commencing smooth kNN distance calibration using 1 thread
03:58:47 Initializing from normalized Laplacian + noise
03:58:47 Commencing optimization for 500 epochs, with 234802 positive edges
03:59:04 Optimization finished
[1] "177 0.18"03:59:04 UMAP embedding parameters a = 1.321 b = 0.9813
03:59:04 Read 1203 rows and found 38 numeric columns
03:59:04 Using Annoy for neighbor search, n_neighbors = 177
03:59:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:59:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a051a80
03:59:05 Searching Annoy index using 1 thread, search_k = 17700
03:59:06 Annoy recall = 100%
03:59:19 Commencing smooth kNN distance calibration using 1 thread
03:59:44 Initializing from normalized Laplacian + noise
03:59:44 Commencing optimization for 500 epochs, with 234802 positive edges
04:00:01 Optimization finished
[1] "177 0.19"04:00:01 UMAP embedding parameters a = 1.292 b = 0.9921
04:00:01 Read 1203 rows and found 38 numeric columns
04:00:01 Using Annoy for neighbor search, n_neighbors = 177
04:00:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:00:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87648f7f95
04:00:02 Searching Annoy index using 1 thread, search_k = 17700
04:00:03 Annoy recall = 100%
04:00:15 Commencing smooth kNN distance calibration using 1 thread
04:00:40 Initializing from normalized Laplacian + noise
04:00:40 Commencing optimization for 500 epochs, with 234802 positive edges
04:00:57 Optimization finished
[1] "177 0.2"04:00:57 UMAP embedding parameters a = 1.262 b = 1.003
04:00:57 Read 1203 rows and found 38 numeric columns
04:00:58 Using Annoy for neighbor search, n_neighbors = 177
04:00:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:00:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871da7b298
04:00:58 Searching Annoy index using 1 thread, search_k = 17700
04:01:00 Annoy recall = 100%
04:01:12 Commencing smooth kNN distance calibration using 1 thread
04:01:37 Initializing from normalized Laplacian + noise
04:01:37 Commencing optimization for 500 epochs, with 234802 positive edges
04:01:54 Optimization finished
[1] "178 0"04:01:54 UMAP embedding parameters a = 1.933 b = 0.7905
04:01:54 Read 1203 rows and found 38 numeric columns
04:01:54 Using Annoy for neighbor search, n_neighbors = 178
04:01:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:01:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872125435b
04:01:55 Searching Annoy index using 1 thread, search_k = 17800
04:01:56 Annoy recall = 100%
04:02:09 Commencing smooth kNN distance calibration using 1 thread
04:02:34 Initializing from normalized Laplacian + noise
04:02:34 Commencing optimization for 500 epochs, with 235968 positive edges
04:02:51 Optimization finished
[1] "178 0.01"04:02:51 UMAP embedding parameters a = 1.896 b = 0.8006
04:02:51 Read 1203 rows and found 38 numeric columns
04:02:51 Using Annoy for neighbor search, n_neighbors = 178
04:02:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:02:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738c367e3
04:02:52 Searching Annoy index using 1 thread, search_k = 17800
04:02:53 Annoy recall = 100%
04:03:06 Commencing smooth kNN distance calibration using 1 thread
04:03:31 Initializing from normalized Laplacian + noise
04:03:31 Commencing optimization for 500 epochs, with 235968 positive edges
04:03:47 Optimization finished
[1] "178 0.02"04:03:48 UMAP embedding parameters a = 1.859 b = 0.8109
04:03:48 Read 1203 rows and found 38 numeric columns
04:03:48 Using Annoy for neighbor search, n_neighbors = 178
04:03:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:03:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875df46130
04:03:49 Searching Annoy index using 1 thread, search_k = 17800
04:03:50 Annoy recall = 100%
04:04:02 Commencing smooth kNN distance calibration using 1 thread
04:04:27 Initializing from normalized Laplacian + noise
04:04:28 Commencing optimization for 500 epochs, with 235968 positive edges
04:04:44 Optimization finished
[1] "178 0.03"04:04:45 UMAP embedding parameters a = 1.822 b = 0.8212
04:04:45 Read 1203 rows and found 38 numeric columns
04:04:45 Using Annoy for neighbor search, n_neighbors = 178
04:04:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:04:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b55c30b
04:04:46 Searching Annoy index using 1 thread, search_k = 17800
04:04:47 Annoy recall = 100%
04:04:59 Commencing smooth kNN distance calibration using 1 thread
04:05:24 Initializing from normalized Laplacian + noise
04:05:24 Commencing optimization for 500 epochs, with 235968 positive edges
04:05:41 Optimization finished
[1] "178 0.04"04:05:42 UMAP embedding parameters a = 1.786 b = 0.8316
04:05:42 Read 1203 rows and found 38 numeric columns
04:05:42 Using Annoy for neighbor search, n_neighbors = 178
04:05:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:05:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c8405e3
04:05:43 Searching Annoy index using 1 thread, search_k = 17800
04:05:44 Annoy recall = 100%
04:05:56 Commencing smooth kNN distance calibration using 1 thread
04:06:21 Initializing from normalized Laplacian + noise
04:06:21 Commencing optimization for 500 epochs, with 235968 positive edges
04:06:38 Optimization finished
[1] "178 0.05"04:06:38 UMAP embedding parameters a = 1.75 b = 0.8421
04:06:38 Read 1203 rows and found 38 numeric columns
04:06:38 Using Annoy for neighbor search, n_neighbors = 178
04:06:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:06:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f22b161
04:06:39 Searching Annoy index using 1 thread, search_k = 17800
04:06:41 Annoy recall = 100%
04:06:53 Commencing smooth kNN distance calibration using 1 thread
04:07:17 Initializing from normalized Laplacian + noise
04:07:18 Commencing optimization for 500 epochs, with 235968 positive edges
04:07:34 Optimization finished
[1] "178 0.06"04:07:35 UMAP embedding parameters a = 1.715 b = 0.8526
04:07:35 Read 1203 rows and found 38 numeric columns
04:07:35 Using Annoy for neighbor search, n_neighbors = 178
04:07:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:07:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e22e923
04:07:36 Searching Annoy index using 1 thread, search_k = 17800
04:07:37 Annoy recall = 100%
04:07:49 Commencing smooth kNN distance calibration using 1 thread
04:08:14 Initializing from normalized Laplacian + noise
04:08:14 Commencing optimization for 500 epochs, with 235968 positive edges
04:08:31 Optimization finished
[1] "178 0.07"04:08:31 UMAP embedding parameters a = 1.68 b = 0.8631
04:08:31 Read 1203 rows and found 38 numeric columns
04:08:31 Using Annoy for neighbor search, n_neighbors = 178
04:08:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:08:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877718eb15
04:08:32 Searching Annoy index using 1 thread, search_k = 17800
04:08:33 Annoy recall = 100%
04:08:45 Commencing smooth kNN distance calibration using 1 thread
04:09:10 Initializing from normalized Laplacian + noise
04:09:10 Commencing optimization for 500 epochs, with 235968 positive edges
04:09:27 Optimization finished
[1] "178 0.08"04:09:27 UMAP embedding parameters a = 1.645 b = 0.8737
04:09:27 Read 1203 rows and found 38 numeric columns
04:09:27 Using Annoy for neighbor search, n_neighbors = 178
04:09:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:09:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fdd20d6
04:09:28 Searching Annoy index using 1 thread, search_k = 17800
04:09:29 Annoy recall = 100%
04:09:42 Commencing smooth kNN distance calibration using 1 thread
04:10:06 Initializing from normalized Laplacian + noise
04:10:07 Commencing optimization for 500 epochs, with 235968 positive edges
04:10:23 Optimization finished
[1] "178 0.09"04:10:23 UMAP embedding parameters a = 1.611 b = 0.8844
04:10:23 Read 1203 rows and found 38 numeric columns
04:10:23 Using Annoy for neighbor search, n_neighbors = 178
04:10:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:10:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f1f2394
04:10:24 Searching Annoy index using 1 thread, search_k = 17800
04:10:26 Annoy recall = 100%
04:10:38 Commencing smooth kNN distance calibration using 1 thread
04:11:03 Initializing from normalized Laplacian + noise
04:11:03 Commencing optimization for 500 epochs, with 235968 positive edges
04:11:19 Optimization finished
[1] "178 0.1"04:11:20 UMAP embedding parameters a = 1.577 b = 0.8951
04:11:20 Read 1203 rows and found 38 numeric columns
04:11:20 Using Annoy for neighbor search, n_neighbors = 178
04:11:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:11:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87387d5b8
04:11:21 Searching Annoy index using 1 thread, search_k = 17800
04:11:22 Annoy recall = 100%
04:11:34 Commencing smooth kNN distance calibration using 1 thread
04:11:59 Initializing from normalized Laplacian + noise
04:11:59 Commencing optimization for 500 epochs, with 235968 positive edges
04:12:16 Optimization finished
[1] "178 0.11"04:12:16 UMAP embedding parameters a = 1.544 b = 0.9058
04:12:16 Read 1203 rows and found 38 numeric columns
04:12:16 Using Annoy for neighbor search, n_neighbors = 178
04:12:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:12:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753024ca0
04:12:17 Searching Annoy index using 1 thread, search_k = 17800
04:12:18 Annoy recall = 100%
04:12:30 Commencing smooth kNN distance calibration using 1 thread
04:12:55 Initializing from normalized Laplacian + noise
04:12:55 Commencing optimization for 500 epochs, with 235968 positive edges
04:13:12 Optimization finished
[1] "178 0.12"04:13:13 UMAP embedding parameters a = 1.51 b = 0.9165
04:13:13 Read 1203 rows and found 38 numeric columns
04:13:13 Using Annoy for neighbor search, n_neighbors = 178
04:13:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:13:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877674fd5e
04:13:14 Searching Annoy index using 1 thread, search_k = 17800
04:13:15 Annoy recall = 100%
04:13:27 Commencing smooth kNN distance calibration using 1 thread
04:13:52 Initializing from normalized Laplacian + noise
04:13:52 Commencing optimization for 500 epochs, with 235968 positive edges
04:14:09 Optimization finished
[1] "178 0.13"04:14:09 UMAP embedding parameters a = 1.478 b = 0.9272
04:14:09 Read 1203 rows and found 38 numeric columns
04:14:09 Using Annoy for neighbor search, n_neighbors = 178
04:14:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:14:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710ac87ad
04:14:10 Searching Annoy index using 1 thread, search_k = 17800
04:14:11 Annoy recall = 100%
04:14:23 Commencing smooth kNN distance calibration using 1 thread
04:14:48 Initializing from normalized Laplacian + noise
04:14:48 Commencing optimization for 500 epochs, with 235968 positive edges
04:15:05 Optimization finished
[1] "178 0.14"04:15:05 UMAP embedding parameters a = 1.446 b = 0.938
04:15:05 Read 1203 rows and found 38 numeric columns
04:15:05 Using Annoy for neighbor search, n_neighbors = 178
04:15:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:15:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b4d493
04:15:06 Searching Annoy index using 1 thread, search_k = 17800
04:15:07 Annoy recall = 100%
04:15:20 Commencing smooth kNN distance calibration using 1 thread
04:15:44 Initializing from normalized Laplacian + noise
04:15:44 Commencing optimization for 500 epochs, with 235968 positive edges
04:16:01 Optimization finished
[1] "178 0.15"04:16:02 UMAP embedding parameters a = 1.414 b = 0.9488
04:16:02 Read 1203 rows and found 38 numeric columns
04:16:02 Using Annoy for neighbor search, n_neighbors = 178
04:16:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:16:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87282bb4b1
04:16:02 Searching Annoy index using 1 thread, search_k = 17800
04:16:04 Annoy recall = 100%
04:16:16 Commencing smooth kNN distance calibration using 1 thread
04:16:41 Initializing from normalized Laplacian + noise
04:16:41 Commencing optimization for 500 epochs, with 235968 positive edges
04:16:58 Optimization finished
[1] "178 0.16"04:16:58 UMAP embedding parameters a = 1.383 b = 0.9596
04:16:58 Read 1203 rows and found 38 numeric columns
04:16:58 Using Annoy for neighbor search, n_neighbors = 178
04:16:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:16:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713a57598
04:16:59 Searching Annoy index using 1 thread, search_k = 17800
04:17:00 Annoy recall = 100%
04:17:12 Commencing smooth kNN distance calibration using 1 thread
04:17:37 Initializing from normalized Laplacian + noise
04:17:37 Commencing optimization for 500 epochs, with 235968 positive edges
04:17:53 Optimization finished
[1] "178 0.17"04:17:54 UMAP embedding parameters a = 1.352 b = 0.9704
04:17:54 Read 1203 rows and found 38 numeric columns
04:17:54 Using Annoy for neighbor search, n_neighbors = 178
04:17:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:17:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87506574dc
04:17:55 Searching Annoy index using 1 thread, search_k = 17800
04:17:56 Annoy recall = 100%
04:18:08 Commencing smooth kNN distance calibration using 1 thread
04:18:33 Initializing from normalized Laplacian + noise
04:18:33 Commencing optimization for 500 epochs, with 235968 positive edges
04:18:49 Optimization finished
[1] "178 0.18"04:18:49 UMAP embedding parameters a = 1.321 b = 0.9813
04:18:49 Read 1203 rows and found 38 numeric columns
04:18:49 Using Annoy for neighbor search, n_neighbors = 178
04:18:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:18:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755d08f1a
04:18:50 Searching Annoy index using 1 thread, search_k = 17800
04:18:52 Annoy recall = 100%
04:19:04 Commencing smooth kNN distance calibration using 1 thread
04:19:28 Initializing from normalized Laplacian + noise
04:19:29 Commencing optimization for 500 epochs, with 235968 positive edges
04:19:45 Optimization finished
[1] "178 0.19"04:19:45 UMAP embedding parameters a = 1.292 b = 0.9921
04:19:45 Read 1203 rows and found 38 numeric columns
04:19:45 Using Annoy for neighbor search, n_neighbors = 178
04:19:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:19:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f4e4523
04:19:46 Searching Annoy index using 1 thread, search_k = 17800
04:19:47 Annoy recall = 100%
04:20:00 Commencing smooth kNN distance calibration using 1 thread
04:20:24 Initializing from normalized Laplacian + noise
04:20:24 Commencing optimization for 500 epochs, with 235968 positive edges
04:20:41 Optimization finished
[1] "178 0.2"04:20:41 UMAP embedding parameters a = 1.262 b = 1.003
04:20:41 Read 1203 rows and found 38 numeric columns
04:20:41 Using Annoy for neighbor search, n_neighbors = 178
04:20:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:20:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a80b153
04:20:42 Searching Annoy index using 1 thread, search_k = 17800
04:20:43 Annoy recall = 100%
04:20:55 Commencing smooth kNN distance calibration using 1 thread
04:21:20 Initializing from normalized Laplacian + noise
04:21:20 Commencing optimization for 500 epochs, with 235968 positive edges
04:21:37 Optimization finished
[1] "179 0"04:21:37 UMAP embedding parameters a = 1.933 b = 0.7905
04:21:37 Read 1203 rows and found 38 numeric columns
04:21:37 Using Annoy for neighbor search, n_neighbors = 179
04:21:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:21:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713b352c5
04:21:38 Searching Annoy index using 1 thread, search_k = 17900
04:21:39 Annoy recall = 100%
04:21:51 Commencing smooth kNN distance calibration using 1 thread
04:22:16 Initializing from normalized Laplacian + noise
04:22:16 Commencing optimization for 500 epochs, with 237140 positive edges
04:22:33 Optimization finished
[1] "179 0.01"04:22:33 UMAP embedding parameters a = 1.896 b = 0.8006
04:22:33 Read 1203 rows and found 38 numeric columns
04:22:33 Using Annoy for neighbor search, n_neighbors = 179
04:22:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:22:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87709b189
04:22:34 Searching Annoy index using 1 thread, search_k = 17900
04:22:35 Annoy recall = 100%
04:22:47 Commencing smooth kNN distance calibration using 1 thread
04:23:12 Initializing from normalized Laplacian + noise
04:23:12 Commencing optimization for 500 epochs, with 237140 positive edges
04:23:28 Optimization finished
[1] "179 0.02"04:23:29 UMAP embedding parameters a = 1.859 b = 0.8109
04:23:29 Read 1203 rows and found 38 numeric columns
04:23:29 Using Annoy for neighbor search, n_neighbors = 179
04:23:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:23:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873135e06
04:23:30 Searching Annoy index using 1 thread, search_k = 17900
04:23:31 Annoy recall = 100%
04:23:43 Commencing smooth kNN distance calibration using 1 thread
04:24:08 Initializing from normalized Laplacian + noise
04:24:08 Commencing optimization for 500 epochs, with 237140 positive edges
04:24:24 Optimization finished
[1] "179 0.03"04:24:25 UMAP embedding parameters a = 1.822 b = 0.8212
04:24:25 Read 1203 rows and found 38 numeric columns
04:24:25 Using Annoy for neighbor search, n_neighbors = 179
04:24:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:24:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762bedf6d
04:24:26 Searching Annoy index using 1 thread, search_k = 17900
04:24:27 Annoy recall = 100%
04:24:39 Commencing smooth kNN distance calibration using 1 thread
04:25:03 Initializing from normalized Laplacian + noise
04:25:04 Commencing optimization for 500 epochs, with 237140 positive edges
04:25:20 Optimization finished
[1] "179 0.04"04:25:20 UMAP embedding parameters a = 1.786 b = 0.8316
04:25:20 Read 1203 rows and found 38 numeric columns
04:25:20 Using Annoy for neighbor search, n_neighbors = 179
04:25:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:25:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87391a5051
04:25:21 Searching Annoy index using 1 thread, search_k = 17900
04:25:23 Annoy recall = 100%
04:25:35 Commencing smooth kNN distance calibration using 1 thread
04:26:00 Initializing from normalized Laplacian + noise
04:26:00 Commencing optimization for 500 epochs, with 237140 positive edges
04:26:16 Optimization finished
[1] "179 0.05"04:26:16 UMAP embedding parameters a = 1.75 b = 0.8421
04:26:16 Read 1203 rows and found 38 numeric columns
04:26:16 Using Annoy for neighbor search, n_neighbors = 179
04:26:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:26:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760bba0cb
04:26:17 Searching Annoy index using 1 thread, search_k = 17900
04:26:19 Annoy recall = 100%
04:26:31 Commencing smooth kNN distance calibration using 1 thread
04:26:55 Initializing from normalized Laplacian + noise
04:26:56 Commencing optimization for 500 epochs, with 237140 positive edges
04:27:12 Optimization finished
[1] "179 0.06"04:27:12 UMAP embedding parameters a = 1.715 b = 0.8526
04:27:12 Read 1203 rows and found 38 numeric columns
04:27:12 Using Annoy for neighbor search, n_neighbors = 179
04:27:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:27:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8729f1ff7d
04:27:13 Searching Annoy index using 1 thread, search_k = 17900
04:27:15 Annoy recall = 100%
04:27:27 Commencing smooth kNN distance calibration using 1 thread
04:27:51 Initializing from normalized Laplacian + noise
04:27:51 Commencing optimization for 500 epochs, with 237140 positive edges
04:28:08 Optimization finished
[1] "179 0.07"04:28:08 UMAP embedding parameters a = 1.68 b = 0.8631
04:28:08 Read 1203 rows and found 38 numeric columns
04:28:08 Using Annoy for neighbor search, n_neighbors = 179
04:28:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:28:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87431f6ad2
04:28:09 Searching Annoy index using 1 thread, search_k = 17900
04:28:11 Annoy recall = 100%
04:28:23 Commencing smooth kNN distance calibration using 1 thread
04:28:47 Initializing from normalized Laplacian + noise
04:28:47 Commencing optimization for 500 epochs, with 237140 positive edges
04:29:04 Optimization finished
[1] "179 0.08"04:29:04 UMAP embedding parameters a = 1.645 b = 0.8737
04:29:04 Read 1203 rows and found 38 numeric columns
04:29:04 Using Annoy for neighbor search, n_neighbors = 179
04:29:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:29:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87454b2061
04:29:05 Searching Annoy index using 1 thread, search_k = 17900
04:29:07 Annoy recall = 100%
04:29:19 Commencing smooth kNN distance calibration using 1 thread
04:29:43 Initializing from normalized Laplacian + noise
04:29:43 Commencing optimization for 500 epochs, with 237140 positive edges
04:30:00 Optimization finished
[1] "179 0.09"04:30:00 UMAP embedding parameters a = 1.611 b = 0.8844
04:30:00 Read 1203 rows and found 38 numeric columns
04:30:00 Using Annoy for neighbor search, n_neighbors = 179
04:30:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:30:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874799b215
04:30:01 Searching Annoy index using 1 thread, search_k = 17900
04:30:03 Annoy recall = 100%
04:30:15 Commencing smooth kNN distance calibration using 1 thread
04:30:39 Initializing from normalized Laplacian + noise
04:30:39 Commencing optimization for 500 epochs, with 237140 positive edges
04:30:56 Optimization finished
[1] "179 0.1"04:30:56 UMAP embedding parameters a = 1.577 b = 0.8951
04:30:56 Read 1203 rows and found 38 numeric columns
04:30:56 Using Annoy for neighbor search, n_neighbors = 179
04:30:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:30:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876444ae2d
04:30:57 Searching Annoy index using 1 thread, search_k = 17900
04:30:58 Annoy recall = 100%
04:31:11 Commencing smooth kNN distance calibration using 1 thread
04:31:35 Initializing from normalized Laplacian + noise
04:31:35 Commencing optimization for 500 epochs, with 237140 positive edges
04:31:52 Optimization finished
[1] "179 0.11"04:31:52 UMAP embedding parameters a = 1.544 b = 0.9058
04:31:52 Read 1203 rows and found 38 numeric columns
04:31:52 Using Annoy for neighbor search, n_neighbors = 179
04:31:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:31:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e0e8844
04:31:53 Searching Annoy index using 1 thread, search_k = 17900
04:31:54 Annoy recall = 100%
04:32:07 Commencing smooth kNN distance calibration using 1 thread
04:32:31 Initializing from normalized Laplacian + noise
04:32:31 Commencing optimization for 500 epochs, with 237140 positive edges
04:32:48 Optimization finished
[1] "179 0.12"04:32:48 UMAP embedding parameters a = 1.51 b = 0.9165
04:32:48 Read 1203 rows and found 38 numeric columns
04:32:48 Using Annoy for neighbor search, n_neighbors = 179
04:32:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:32:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87258e1345
04:32:49 Searching Annoy index using 1 thread, search_k = 17900
04:32:50 Annoy recall = 100%
04:33:03 Commencing smooth kNN distance calibration using 1 thread
04:33:27 Initializing from normalized Laplacian + noise
04:33:27 Commencing optimization for 500 epochs, with 237140 positive edges
04:33:44 Optimization finished
[1] "179 0.13"04:33:44 UMAP embedding parameters a = 1.478 b = 0.9272
04:33:44 Read 1203 rows and found 38 numeric columns
04:33:44 Using Annoy for neighbor search, n_neighbors = 179
04:33:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:33:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f9a7138
04:33:45 Searching Annoy index using 1 thread, search_k = 17900
04:33:46 Annoy recall = 100%
04:33:59 Commencing smooth kNN distance calibration using 1 thread
04:34:23 Initializing from normalized Laplacian + noise
04:34:23 Commencing optimization for 500 epochs, with 237140 positive edges
04:34:40 Optimization finished
[1] "179 0.14"04:34:40 UMAP embedding parameters a = 1.446 b = 0.938
04:34:40 Read 1203 rows and found 38 numeric columns
04:34:40 Using Annoy for neighbor search, n_neighbors = 179
04:34:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:34:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a928e27
04:34:41 Searching Annoy index using 1 thread, search_k = 17900
04:34:42 Annoy recall = 100%
04:34:55 Commencing smooth kNN distance calibration using 1 thread
04:35:19 Initializing from normalized Laplacian + noise
04:35:19 Commencing optimization for 500 epochs, with 237140 positive edges
04:35:36 Optimization finished
[1] "179 0.15"04:35:36 UMAP embedding parameters a = 1.414 b = 0.9488
04:35:36 Read 1203 rows and found 38 numeric columns
04:35:36 Using Annoy for neighbor search, n_neighbors = 179
04:35:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:35:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744b0c4a6
04:35:37 Searching Annoy index using 1 thread, search_k = 17900
04:35:39 Annoy recall = 100%
04:35:51 Commencing smooth kNN distance calibration using 1 thread
04:36:15 Initializing from normalized Laplacian + noise
04:36:15 Commencing optimization for 500 epochs, with 237140 positive edges
04:36:32 Optimization finished
[1] "179 0.16"04:36:32 UMAP embedding parameters a = 1.383 b = 0.9596
04:36:32 Read 1203 rows and found 38 numeric columns
04:36:33 Using Annoy for neighbor search, n_neighbors = 179
04:36:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:36:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877dbd5a5b
04:36:33 Searching Annoy index using 1 thread, search_k = 17900
04:36:35 Annoy recall = 100%
04:36:47 Commencing smooth kNN distance calibration using 1 thread
04:37:11 Initializing from normalized Laplacian + noise
04:37:11 Commencing optimization for 500 epochs, with 237140 positive edges
04:37:28 Optimization finished
[1] "179 0.17"04:37:28 UMAP embedding parameters a = 1.352 b = 0.9704
04:37:29 Read 1203 rows and found 38 numeric columns
04:37:29 Using Annoy for neighbor search, n_neighbors = 179
04:37:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:37:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711ab793c
04:37:29 Searching Annoy index using 1 thread, search_k = 17900
04:37:31 Annoy recall = 100%
04:37:43 Commencing smooth kNN distance calibration using 1 thread
04:38:07 Initializing from normalized Laplacian + noise
04:38:08 Commencing optimization for 500 epochs, with 237140 positive edges
04:38:24 Optimization finished
[1] "179 0.18"04:38:25 UMAP embedding parameters a = 1.321 b = 0.9813
04:38:25 Read 1203 rows and found 38 numeric columns
04:38:25 Using Annoy for neighbor search, n_neighbors = 179
04:38:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:38:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87148de57c
04:38:25 Searching Annoy index using 1 thread, search_k = 17900
04:38:27 Annoy recall = 100%
04:38:39 Commencing smooth kNN distance calibration using 1 thread
04:39:03 Initializing from normalized Laplacian + noise
04:39:04 Commencing optimization for 500 epochs, with 237140 positive edges
04:39:20 Optimization finished
[1] "179 0.19"04:39:21 UMAP embedding parameters a = 1.292 b = 0.9921
04:39:21 Read 1203 rows and found 38 numeric columns
04:39:21 Using Annoy for neighbor search, n_neighbors = 179
04:39:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:39:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cdc7def
04:39:22 Searching Annoy index using 1 thread, search_k = 17900
04:39:23 Annoy recall = 100%
04:39:35 Commencing smooth kNN distance calibration using 1 thread
04:40:00 Initializing from normalized Laplacian + noise
04:40:00 Commencing optimization for 500 epochs, with 237140 positive edges
04:40:16 Optimization finished
[1] "179 0.2"04:40:17 UMAP embedding parameters a = 1.262 b = 1.003
04:40:17 Read 1203 rows and found 38 numeric columns
04:40:17 Using Annoy for neighbor search, n_neighbors = 179
04:40:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:40:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715334ef4
04:40:18 Searching Annoy index using 1 thread, search_k = 17900
04:40:19 Annoy recall = 100%
04:40:31 Commencing smooth kNN distance calibration using 1 thread
04:40:56 Initializing from normalized Laplacian + noise
04:40:56 Commencing optimization for 500 epochs, with 237140 positive edges
04:41:12 Optimization finished
[1] "180 0"04:41:13 UMAP embedding parameters a = 1.933 b = 0.7905
04:41:13 Read 1203 rows and found 38 numeric columns
04:41:13 Using Annoy for neighbor search, n_neighbors = 180
04:41:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:41:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876790321c
04:41:14 Searching Annoy index using 1 thread, search_k = 18000
04:41:15 Annoy recall = 100%
04:41:27 Commencing smooth kNN distance calibration using 1 thread
04:41:52 Initializing from normalized Laplacian + noise
04:41:52 Commencing optimization for 500 epochs, with 238300 positive edges
04:42:09 Optimization finished
[1] "180 0.01"04:42:09 UMAP embedding parameters a = 1.896 b = 0.8006
04:42:09 Read 1203 rows and found 38 numeric columns
04:42:09 Using Annoy for neighbor search, n_neighbors = 180
04:42:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:42:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763517b4e
04:42:10 Searching Annoy index using 1 thread, search_k = 18000
04:42:11 Annoy recall = 100%
04:42:23 Commencing smooth kNN distance calibration using 1 thread
04:42:48 Initializing from normalized Laplacian + noise
04:42:48 Commencing optimization for 500 epochs, with 238300 positive edges
04:43:05 Optimization finished
[1] "180 0.02"04:43:05 UMAP embedding parameters a = 1.859 b = 0.8109
04:43:05 Read 1203 rows and found 38 numeric columns
04:43:05 Using Annoy for neighbor search, n_neighbors = 180
04:43:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:43:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725dfd6a2
04:43:06 Searching Annoy index using 1 thread, search_k = 18000
04:43:07 Annoy recall = 100%
04:43:19 Commencing smooth kNN distance calibration using 1 thread
04:43:44 Initializing from normalized Laplacian + noise
04:43:44 Commencing optimization for 500 epochs, with 238300 positive edges
04:44:01 Optimization finished
[1] "180 0.03"04:44:01 UMAP embedding parameters a = 1.822 b = 0.8212
04:44:01 Read 1203 rows and found 38 numeric columns
04:44:01 Using Annoy for neighbor search, n_neighbors = 180
04:44:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:44:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876d4506b0
04:44:02 Searching Annoy index using 1 thread, search_k = 18000
04:44:04 Annoy recall = 100%
04:44:16 Commencing smooth kNN distance calibration using 1 thread
04:44:40 Initializing from normalized Laplacian + noise
04:44:41 Commencing optimization for 500 epochs, with 238300 positive edges
04:44:57 Optimization finished
[1] "180 0.04"04:44:58 UMAP embedding parameters a = 1.786 b = 0.8316
04:44:58 Read 1203 rows and found 38 numeric columns
04:44:58 Using Annoy for neighbor search, n_neighbors = 180
04:44:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:44:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b7d2fff
04:44:59 Searching Annoy index using 1 thread, search_k = 18000
04:45:00 Annoy recall = 100%
04:45:12 Commencing smooth kNN distance calibration using 1 thread
04:45:37 Initializing from normalized Laplacian + noise
04:45:37 Commencing optimization for 500 epochs, with 238300 positive edges
04:45:53 Optimization finished
[1] "180 0.05"04:45:54 UMAP embedding parameters a = 1.75 b = 0.8421
04:45:54 Read 1203 rows and found 38 numeric columns
04:45:54 Using Annoy for neighbor search, n_neighbors = 180
04:45:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:45:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739854c3a
04:45:55 Searching Annoy index using 1 thread, search_k = 18000
04:45:56 Annoy recall = 100%
04:46:08 Commencing smooth kNN distance calibration using 1 thread
04:46:33 Initializing from normalized Laplacian + noise
04:46:33 Commencing optimization for 500 epochs, with 238300 positive edges
04:46:50 Optimization finished
[1] "180 0.06"04:46:50 UMAP embedding parameters a = 1.715 b = 0.8526
04:46:50 Read 1203 rows and found 38 numeric columns
04:46:50 Using Annoy for neighbor search, n_neighbors = 180
04:46:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:46:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873daa7b8c
04:46:51 Searching Annoy index using 1 thread, search_k = 18000
04:46:52 Annoy recall = 100%
04:47:04 Commencing smooth kNN distance calibration using 1 thread
04:47:29 Initializing from normalized Laplacian + noise
04:47:29 Commencing optimization for 500 epochs, with 238300 positive edges
04:47:46 Optimization finished
[1] "180 0.07"04:47:46 UMAP embedding parameters a = 1.68 b = 0.8631
04:47:46 Read 1203 rows and found 38 numeric columns
04:47:46 Using Annoy for neighbor search, n_neighbors = 180
04:47:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:47:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87614dbf19
04:47:47 Searching Annoy index using 1 thread, search_k = 18000
04:47:48 Annoy recall = 100%
04:48:01 Commencing smooth kNN distance calibration using 1 thread
04:48:25 Initializing from normalized Laplacian + noise
04:48:26 Commencing optimization for 500 epochs, with 238300 positive edges
04:48:42 Optimization finished
[1] "180 0.08"04:48:42 UMAP embedding parameters a = 1.645 b = 0.8737
04:48:42 Read 1203 rows and found 38 numeric columns
04:48:42 Using Annoy for neighbor search, n_neighbors = 180
04:48:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:48:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748d3915d
04:48:43 Searching Annoy index using 1 thread, search_k = 18000
04:48:45 Annoy recall = 100%
04:48:57 Commencing smooth kNN distance calibration using 1 thread
04:49:22 Initializing from normalized Laplacian + noise
04:49:22 Commencing optimization for 500 epochs, with 238300 positive edges
04:49:38 Optimization finished
[1] "180 0.09"04:49:39 UMAP embedding parameters a = 1.611 b = 0.8844
04:49:39 Read 1203 rows and found 38 numeric columns
04:49:39 Using Annoy for neighbor search, n_neighbors = 180
04:49:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:49:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87482b2cdf
04:49:40 Searching Annoy index using 1 thread, search_k = 18000
04:49:41 Annoy recall = 100%
04:49:53 Commencing smooth kNN distance calibration using 1 thread
04:50:18 Initializing from normalized Laplacian + noise
04:50:18 Commencing optimization for 500 epochs, with 238300 positive edges
04:50:35 Optimization finished
[1] "180 0.1"04:50:35 UMAP embedding parameters a = 1.577 b = 0.8951
04:50:35 Read 1203 rows and found 38 numeric columns
04:50:35 Using Annoy for neighbor search, n_neighbors = 180
04:50:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:50:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87750111de
04:50:36 Searching Annoy index using 1 thread, search_k = 18000
04:50:37 Annoy recall = 100%
04:50:49 Commencing smooth kNN distance calibration using 1 thread
04:51:14 Initializing from normalized Laplacian + noise
04:51:14 Commencing optimization for 500 epochs, with 238300 positive edges
04:51:31 Optimization finished
[1] "180 0.11"04:51:31 UMAP embedding parameters a = 1.544 b = 0.9058
04:51:31 Read 1203 rows and found 38 numeric columns
04:51:31 Using Annoy for neighbor search, n_neighbors = 180
04:51:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:51:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874fdd42e6
04:51:32 Searching Annoy index using 1 thread, search_k = 18000
04:51:34 Annoy recall = 100%
04:51:46 Commencing smooth kNN distance calibration using 1 thread
04:52:10 Initializing from normalized Laplacian + noise
04:52:10 Commencing optimization for 500 epochs, with 238300 positive edges
04:52:27 Optimization finished
[1] "180 0.12"04:52:28 UMAP embedding parameters a = 1.51 b = 0.9165
04:52:28 Read 1203 rows and found 38 numeric columns
04:52:28 Using Annoy for neighbor search, n_neighbors = 180
04:52:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:52:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b3e8ae6
04:52:29 Searching Annoy index using 1 thread, search_k = 18000
04:52:30 Annoy recall = 100%
04:52:42 Commencing smooth kNN distance calibration using 1 thread
04:53:07 Initializing from normalized Laplacian + noise
04:53:07 Commencing optimization for 500 epochs, with 238300 positive edges
04:53:24 Optimization finished
[1] "180 0.13"04:53:24 UMAP embedding parameters a = 1.478 b = 0.9272
04:53:24 Read 1203 rows and found 38 numeric columns
04:53:24 Using Annoy for neighbor search, n_neighbors = 180
04:53:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:53:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757bff14b
04:53:25 Searching Annoy index using 1 thread, search_k = 18000
04:53:26 Annoy recall = 100%
04:53:38 Commencing smooth kNN distance calibration using 1 thread
04:54:03 Initializing from normalized Laplacian + noise
04:54:03 Commencing optimization for 500 epochs, with 238300 positive edges
04:54:20 Optimization finished
[1] "180 0.14"04:54:20 UMAP embedding parameters a = 1.446 b = 0.938
04:54:20 Read 1203 rows and found 38 numeric columns
04:54:20 Using Annoy for neighbor search, n_neighbors = 180
04:54:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:54:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878f79338
04:54:21 Searching Annoy index using 1 thread, search_k = 18000
04:54:22 Annoy recall = 100%
04:54:35 Commencing smooth kNN distance calibration using 1 thread
04:54:59 Initializing from normalized Laplacian + noise
04:54:59 Commencing optimization for 500 epochs, with 238300 positive edges
04:55:16 Optimization finished
[1] "180 0.15"04:55:16 UMAP embedding parameters a = 1.414 b = 0.9488
04:55:16 Read 1203 rows and found 38 numeric columns
04:55:16 Using Annoy for neighbor search, n_neighbors = 180
04:55:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:55:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872bfa2bb1
04:55:17 Searching Annoy index using 1 thread, search_k = 18000
04:55:19 Annoy recall = 100%
04:55:31 Commencing smooth kNN distance calibration using 1 thread
04:55:56 Initializing from normalized Laplacian + noise
04:55:56 Commencing optimization for 500 epochs, with 238300 positive edges
04:56:12 Optimization finished
[1] "180 0.16"04:56:13 UMAP embedding parameters a = 1.383 b = 0.9596
04:56:13 Read 1203 rows and found 38 numeric columns
04:56:13 Using Annoy for neighbor search, n_neighbors = 180
04:56:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:56:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b1f0c8
04:56:14 Searching Annoy index using 1 thread, search_k = 18000
04:56:15 Annoy recall = 100%
04:56:27 Commencing smooth kNN distance calibration using 1 thread
04:56:52 Initializing from normalized Laplacian + noise
04:56:52 Commencing optimization for 500 epochs, with 238300 positive edges
04:57:09 Optimization finished
[1] "180 0.17"04:57:09 UMAP embedding parameters a = 1.352 b = 0.9704
04:57:09 Read 1203 rows and found 38 numeric columns
04:57:09 Using Annoy for neighbor search, n_neighbors = 180
04:57:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:57:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874c16fe0a
04:57:10 Searching Annoy index using 1 thread, search_k = 18000
04:57:11 Annoy recall = 100%
04:57:24 Commencing smooth kNN distance calibration using 1 thread
04:57:49 Initializing from normalized Laplacian + noise
04:57:49 Commencing optimization for 500 epochs, with 238300 positive edges
04:58:05 Optimization finished
[1] "180 0.18"04:58:06 UMAP embedding parameters a = 1.321 b = 0.9813
04:58:06 Read 1203 rows and found 38 numeric columns
04:58:06 Using Annoy for neighbor search, n_neighbors = 180
04:58:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:58:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8771454c12
04:58:07 Searching Annoy index using 1 thread, search_k = 18000
04:58:08 Annoy recall = 100%
04:58:20 Commencing smooth kNN distance calibration using 1 thread
04:58:45 Initializing from normalized Laplacian + noise
04:58:45 Commencing optimization for 500 epochs, with 238300 positive edges
04:59:02 Optimization finished
[1] "180 0.19"04:59:02 UMAP embedding parameters a = 1.292 b = 0.9921
04:59:02 Read 1203 rows and found 38 numeric columns
04:59:02 Using Annoy for neighbor search, n_neighbors = 180
04:59:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:59:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87494ba2dd
04:59:03 Searching Annoy index using 1 thread, search_k = 18000
04:59:04 Annoy recall = 100%
04:59:16 Commencing smooth kNN distance calibration using 1 thread
04:59:41 Initializing from normalized Laplacian + noise
04:59:41 Commencing optimization for 500 epochs, with 238300 positive edges
04:59:58 Optimization finished
[1] "180 0.2"04:59:58 UMAP embedding parameters a = 1.262 b = 1.003
04:59:58 Read 1203 rows and found 38 numeric columns
04:59:58 Using Annoy for neighbor search, n_neighbors = 180
04:59:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
04:59:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87305bac37
04:59:59 Searching Annoy index using 1 thread, search_k = 18000
05:00:00 Annoy recall = 100%
05:00:12 Commencing smooth kNN distance calibration using 1 thread
05:00:37 Initializing from normalized Laplacian + noise
05:00:37 Commencing optimization for 500 epochs, with 238300 positive edges
05:00:53 Optimization finished
[1] "181 0"05:00:54 UMAP embedding parameters a = 1.933 b = 0.7905
05:00:54 Read 1203 rows and found 38 numeric columns
05:00:54 Using Annoy for neighbor search, n_neighbors = 181
05:00:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:00:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f53d456
05:00:55 Searching Annoy index using 1 thread, search_k = 18100
05:00:56 Annoy recall = 100%
05:01:08 Commencing smooth kNN distance calibration using 1 thread
05:01:32 Initializing from normalized Laplacian + noise
05:01:32 Commencing optimization for 500 epochs, with 239566 positive edges
05:01:49 Optimization finished
[1] "181 0.01"05:01:49 UMAP embedding parameters a = 1.896 b = 0.8006
05:01:49 Read 1203 rows and found 38 numeric columns
05:01:49 Using Annoy for neighbor search, n_neighbors = 181
05:01:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:01:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ed9b622
05:01:50 Searching Annoy index using 1 thread, search_k = 18100
05:01:52 Annoy recall = 100%
05:02:04 Commencing smooth kNN distance calibration using 1 thread
05:02:28 Initializing from normalized Laplacian + noise
05:02:28 Commencing optimization for 500 epochs, with 239566 positive edges
05:02:45 Optimization finished
[1] "181 0.02"05:02:45 UMAP embedding parameters a = 1.859 b = 0.8109
05:02:45 Read 1203 rows and found 38 numeric columns
05:02:45 Using Annoy for neighbor search, n_neighbors = 181
05:02:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:02:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ff61d6f
05:02:46 Searching Annoy index using 1 thread, search_k = 18100
05:02:47 Annoy recall = 100%
05:03:00 Commencing smooth kNN distance calibration using 1 thread
05:03:24 Initializing from normalized Laplacian + noise
05:03:24 Commencing optimization for 500 epochs, with 239566 positive edges
05:03:41 Optimization finished
[1] "181 0.03"05:03:41 UMAP embedding parameters a = 1.822 b = 0.8212
05:03:41 Read 1203 rows and found 38 numeric columns
05:03:41 Using Annoy for neighbor search, n_neighbors = 181
05:03:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:03:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879e6627e
05:03:42 Searching Annoy index using 1 thread, search_k = 18100
05:03:43 Annoy recall = 100%
05:03:56 Commencing smooth kNN distance calibration using 1 thread
05:04:20 Initializing from normalized Laplacian + noise
05:04:20 Commencing optimization for 500 epochs, with 239566 positive edges
05:04:37 Optimization finished
[1] "181 0.04"05:04:37 UMAP embedding parameters a = 1.786 b = 0.8316
05:04:37 Read 1203 rows and found 38 numeric columns
05:04:37 Using Annoy for neighbor search, n_neighbors = 181
05:04:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:04:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87338a7ac9
05:04:38 Searching Annoy index using 1 thread, search_k = 18100
05:04:39 Annoy recall = 100%
05:04:51 Commencing smooth kNN distance calibration using 1 thread
05:05:16 Initializing from normalized Laplacian + noise
05:05:16 Commencing optimization for 500 epochs, with 239566 positive edges
05:05:33 Optimization finished
[1] "181 0.05"05:05:33 UMAP embedding parameters a = 1.75 b = 0.8421
05:05:33 Read 1203 rows and found 38 numeric columns
05:05:33 Using Annoy for neighbor search, n_neighbors = 181
05:05:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:05:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873db377cb
05:05:34 Searching Annoy index using 1 thread, search_k = 18100
05:05:35 Annoy recall = 100%
05:05:47 Commencing smooth kNN distance calibration using 1 thread
05:06:12 Initializing from normalized Laplacian + noise
05:06:12 Commencing optimization for 500 epochs, with 239566 positive edges
05:06:29 Optimization finished
[1] "181 0.06"05:06:29 UMAP embedding parameters a = 1.715 b = 0.8526
05:06:29 Read 1203 rows and found 38 numeric columns
05:06:29 Using Annoy for neighbor search, n_neighbors = 181
05:06:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:06:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b91dbba
05:06:30 Searching Annoy index using 1 thread, search_k = 18100
05:06:31 Annoy recall = 100%
05:06:43 Commencing smooth kNN distance calibration using 1 thread
05:07:08 Initializing from normalized Laplacian + noise
05:07:08 Commencing optimization for 500 epochs, with 239566 positive edges
05:07:24 Optimization finished
[1] "181 0.07"05:07:25 UMAP embedding parameters a = 1.68 b = 0.8631
05:07:25 Read 1203 rows and found 38 numeric columns
05:07:25 Using Annoy for neighbor search, n_neighbors = 181
05:07:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:07:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8748186045
05:07:26 Searching Annoy index using 1 thread, search_k = 18100
05:07:27 Annoy recall = 100%
05:07:39 Commencing smooth kNN distance calibration using 1 thread
05:08:04 Initializing from normalized Laplacian + noise
05:08:04 Commencing optimization for 500 epochs, with 239566 positive edges
05:08:20 Optimization finished
[1] "181 0.08"05:08:21 UMAP embedding parameters a = 1.645 b = 0.8737
05:08:21 Read 1203 rows and found 38 numeric columns
05:08:21 Using Annoy for neighbor search, n_neighbors = 181
05:08:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:08:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a8ff5ba
05:08:22 Searching Annoy index using 1 thread, search_k = 18100
05:08:23 Annoy recall = 100%
05:08:35 Commencing smooth kNN distance calibration using 1 thread
05:08:59 Initializing from normalized Laplacian + noise
05:09:00 Commencing optimization for 500 epochs, with 239566 positive edges
05:09:16 Optimization finished
[1] "181 0.09"05:09:17 UMAP embedding parameters a = 1.611 b = 0.8844
05:09:17 Read 1203 rows and found 38 numeric columns
05:09:17 Using Annoy for neighbor search, n_neighbors = 181
05:09:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:09:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730c52aaf
05:09:18 Searching Annoy index using 1 thread, search_k = 18100
05:09:19 Annoy recall = 100%
05:09:31 Commencing smooth kNN distance calibration using 1 thread
05:09:55 Initializing from normalized Laplacian + noise
05:09:56 Commencing optimization for 500 epochs, with 239566 positive edges
05:10:12 Optimization finished
[1] "181 0.1"05:10:12 UMAP embedding parameters a = 1.577 b = 0.8951
05:10:12 Read 1203 rows and found 38 numeric columns
05:10:12 Using Annoy for neighbor search, n_neighbors = 181
05:10:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:10:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fa89262
05:10:13 Searching Annoy index using 1 thread, search_k = 18100
05:10:15 Annoy recall = 100%
05:10:27 Commencing smooth kNN distance calibration using 1 thread
05:10:52 Initializing from normalized Laplacian + noise
05:10:52 Commencing optimization for 500 epochs, with 239566 positive edges
05:11:08 Optimization finished
[1] "181 0.11"05:11:08 UMAP embedding parameters a = 1.544 b = 0.9058
05:11:08 Read 1203 rows and found 38 numeric columns
05:11:08 Using Annoy for neighbor search, n_neighbors = 181
05:11:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:11:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87de17108
05:11:09 Searching Annoy index using 1 thread, search_k = 18100
05:11:11 Annoy recall = 100%
05:11:23 Commencing smooth kNN distance calibration using 1 thread
05:11:48 Initializing from normalized Laplacian + noise
05:11:48 Commencing optimization for 500 epochs, with 239566 positive edges
05:12:04 Optimization finished
[1] "181 0.12"05:12:04 UMAP embedding parameters a = 1.51 b = 0.9165
05:12:04 Read 1203 rows and found 38 numeric columns
05:12:04 Using Annoy for neighbor search, n_neighbors = 181
05:12:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:12:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8756a50151
05:12:05 Searching Annoy index using 1 thread, search_k = 18100
05:12:07 Annoy recall = 100%
05:12:19 Commencing smooth kNN distance calibration using 1 thread
05:12:44 Initializing from normalized Laplacian + noise
05:12:44 Commencing optimization for 500 epochs, with 239566 positive edges
05:13:00 Optimization finished
[1] "181 0.13"05:13:01 UMAP embedding parameters a = 1.478 b = 0.9272
05:13:01 Read 1203 rows and found 38 numeric columns
05:13:01 Using Annoy for neighbor search, n_neighbors = 181
05:13:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:13:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ced9912
05:13:02 Searching Annoy index using 1 thread, search_k = 18100
05:13:03 Annoy recall = 100%
05:13:15 Commencing smooth kNN distance calibration using 1 thread
05:13:39 Initializing from normalized Laplacian + noise
05:13:40 Commencing optimization for 500 epochs, with 239566 positive edges
05:13:56 Optimization finished
[1] "181 0.14"05:13:57 UMAP embedding parameters a = 1.446 b = 0.938
05:13:57 Read 1203 rows and found 38 numeric columns
05:13:57 Using Annoy for neighbor search, n_neighbors = 181
05:13:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:13:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87195ea107
05:13:58 Searching Annoy index using 1 thread, search_k = 18100
05:13:59 Annoy recall = 100%
05:14:11 Commencing smooth kNN distance calibration using 1 thread
05:14:35 Initializing from normalized Laplacian + noise
05:14:36 Commencing optimization for 500 epochs, with 239566 positive edges
05:14:52 Optimization finished
[1] "181 0.15"05:14:53 UMAP embedding parameters a = 1.414 b = 0.9488
05:14:53 Read 1203 rows and found 38 numeric columns
05:14:53 Using Annoy for neighbor search, n_neighbors = 181
05:14:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:14:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87102a4d8b
05:14:54 Searching Annoy index using 1 thread, search_k = 18100
05:14:55 Annoy recall = 100%
05:15:07 Commencing smooth kNN distance calibration using 1 thread
05:15:31 Initializing from normalized Laplacian + noise
05:15:32 Commencing optimization for 500 epochs, with 239566 positive edges
05:15:48 Optimization finished
[1] "181 0.16"05:15:49 UMAP embedding parameters a = 1.383 b = 0.9596
05:15:49 Read 1203 rows and found 38 numeric columns
05:15:49 Using Annoy for neighbor search, n_neighbors = 181
05:15:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:15:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a98149e
05:15:50 Searching Annoy index using 1 thread, search_k = 18100
05:15:51 Annoy recall = 100%
05:16:03 Commencing smooth kNN distance calibration using 1 thread
05:16:27 Initializing from normalized Laplacian + noise
05:16:28 Commencing optimization for 500 epochs, with 239566 positive edges
05:16:44 Optimization finished
[1] "181 0.17"05:16:45 UMAP embedding parameters a = 1.352 b = 0.9704
05:16:45 Read 1203 rows and found 38 numeric columns
05:16:45 Using Annoy for neighbor search, n_neighbors = 181
05:16:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:16:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877aac6021
05:16:46 Searching Annoy index using 1 thread, search_k = 18100
05:16:47 Annoy recall = 100%
05:16:59 Commencing smooth kNN distance calibration using 1 thread
05:17:24 Initializing from normalized Laplacian + noise
05:17:24 Commencing optimization for 500 epochs, with 239566 positive edges
05:17:40 Optimization finished
[1] "181 0.18"05:17:41 UMAP embedding parameters a = 1.321 b = 0.9813
05:17:41 Read 1203 rows and found 38 numeric columns
05:17:41 Using Annoy for neighbor search, n_neighbors = 181
05:17:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:17:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758fddee8
05:17:42 Searching Annoy index using 1 thread, search_k = 18100
05:17:43 Annoy recall = 100%
05:17:55 Commencing smooth kNN distance calibration using 1 thread
05:18:20 Initializing from normalized Laplacian + noise
05:18:20 Commencing optimization for 500 epochs, with 239566 positive edges
05:18:36 Optimization finished
[1] "181 0.19"05:18:37 UMAP embedding parameters a = 1.292 b = 0.9921
05:18:37 Read 1203 rows and found 38 numeric columns
05:18:37 Using Annoy for neighbor search, n_neighbors = 181
05:18:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:18:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722c3417d
05:18:38 Searching Annoy index using 1 thread, search_k = 18100
05:18:39 Annoy recall = 100%
05:18:51 Commencing smooth kNN distance calibration using 1 thread
05:19:16 Initializing from normalized Laplacian + noise
05:19:16 Commencing optimization for 500 epochs, with 239566 positive edges
05:19:32 Optimization finished
[1] "181 0.2"05:19:33 UMAP embedding parameters a = 1.262 b = 1.003
05:19:33 Read 1203 rows and found 38 numeric columns
05:19:33 Using Annoy for neighbor search, n_neighbors = 181
05:19:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:19:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876fad71ff
05:19:34 Searching Annoy index using 1 thread, search_k = 18100
05:19:35 Annoy recall = 100%
05:19:47 Commencing smooth kNN distance calibration using 1 thread
05:20:12 Initializing from normalized Laplacian + noise
05:20:12 Commencing optimization for 500 epochs, with 239566 positive edges
05:20:29 Optimization finished
[1] "182 0"05:20:29 UMAP embedding parameters a = 1.933 b = 0.7905
05:20:29 Read 1203 rows and found 38 numeric columns
05:20:29 Using Annoy for neighbor search, n_neighbors = 182
05:20:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:20:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728db21ce
05:20:30 Searching Annoy index using 1 thread, search_k = 18200
05:20:31 Annoy recall = 100%
05:20:43 Commencing smooth kNN distance calibration using 1 thread
05:21:08 Initializing from normalized Laplacian + noise
05:21:08 Commencing optimization for 500 epochs, with 240732 positive edges
05:21:25 Optimization finished
[1] "182 0.01"05:21:25 UMAP embedding parameters a = 1.896 b = 0.8006
05:21:25 Read 1203 rows and found 38 numeric columns
05:21:25 Using Annoy for neighbor search, n_neighbors = 182
05:21:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:21:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e01cc63
05:21:26 Searching Annoy index using 1 thread, search_k = 18200
05:21:27 Annoy recall = 100%
05:21:39 Commencing smooth kNN distance calibration using 1 thread
05:22:04 Initializing from normalized Laplacian + noise
05:22:04 Commencing optimization for 500 epochs, with 240732 positive edges
05:22:21 Optimization finished
[1] "182 0.02"05:22:21 UMAP embedding parameters a = 1.859 b = 0.8109
05:22:21 Read 1203 rows and found 38 numeric columns
05:22:21 Using Annoy for neighbor search, n_neighbors = 182
05:22:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:22:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87476d634b
05:22:22 Searching Annoy index using 1 thread, search_k = 18200
05:22:23 Annoy recall = 100%
05:22:36 Commencing smooth kNN distance calibration using 1 thread
05:23:00 Initializing from normalized Laplacian + noise
05:23:00 Commencing optimization for 500 epochs, with 240732 positive edges
05:23:17 Optimization finished
[1] "182 0.03"05:23:17 UMAP embedding parameters a = 1.822 b = 0.8212
05:23:17 Read 1203 rows and found 38 numeric columns
05:23:17 Using Annoy for neighbor search, n_neighbors = 182
05:23:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:23:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731d2b506
05:23:18 Searching Annoy index using 1 thread, search_k = 18200
05:23:20 Annoy recall = 100%
05:23:32 Commencing smooth kNN distance calibration using 1 thread
05:23:56 Initializing from normalized Laplacian + noise
05:23:56 Commencing optimization for 500 epochs, with 240732 positive edges
05:24:13 Optimization finished
[1] "182 0.04"05:24:13 UMAP embedding parameters a = 1.786 b = 0.8316
05:24:13 Read 1203 rows and found 38 numeric columns
05:24:13 Using Annoy for neighbor search, n_neighbors = 182
05:24:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:24:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8719fbf815
05:24:14 Searching Annoy index using 1 thread, search_k = 18200
05:24:16 Annoy recall = 100%
05:24:28 Commencing smooth kNN distance calibration using 1 thread
05:24:53 Initializing from normalized Laplacian + noise
05:24:53 Commencing optimization for 500 epochs, with 240732 positive edges
05:25:09 Optimization finished
[1] "182 0.05"05:25:10 UMAP embedding parameters a = 1.75 b = 0.8421
05:25:10 Read 1203 rows and found 38 numeric columns
05:25:10 Using Annoy for neighbor search, n_neighbors = 182
05:25:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:25:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87491f5413
05:25:11 Searching Annoy index using 1 thread, search_k = 18200
05:25:12 Annoy recall = 100%
05:25:24 Commencing smooth kNN distance calibration using 1 thread
05:25:49 Initializing from normalized Laplacian + noise
05:25:49 Commencing optimization for 500 epochs, with 240732 positive edges
05:26:05 Optimization finished
[1] "182 0.06"05:26:06 UMAP embedding parameters a = 1.715 b = 0.8526
05:26:06 Read 1203 rows and found 38 numeric columns
05:26:06 Using Annoy for neighbor search, n_neighbors = 182
05:26:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:26:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877de9b310
05:26:07 Searching Annoy index using 1 thread, search_k = 18200
05:26:08 Annoy recall = 100%
05:26:20 Commencing smooth kNN distance calibration using 1 thread
05:26:45 Initializing from normalized Laplacian + noise
05:26:45 Commencing optimization for 500 epochs, with 240732 positive edges
05:27:02 Optimization finished
[1] "182 0.07"05:27:02 UMAP embedding parameters a = 1.68 b = 0.8631
05:27:02 Read 1203 rows and found 38 numeric columns
05:27:02 Using Annoy for neighbor search, n_neighbors = 182
05:27:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:27:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b414427
05:27:03 Searching Annoy index using 1 thread, search_k = 18200
05:27:04 Annoy recall = 100%
05:27:16 Commencing smooth kNN distance calibration using 1 thread
05:27:41 Initializing from normalized Laplacian + noise
05:27:41 Commencing optimization for 500 epochs, with 240732 positive edges
05:27:58 Optimization finished
[1] "182 0.08"05:27:58 UMAP embedding parameters a = 1.645 b = 0.8737
05:27:58 Read 1203 rows and found 38 numeric columns
05:27:58 Using Annoy for neighbor search, n_neighbors = 182
05:27:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:27:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87126af6f1
05:27:59 Searching Annoy index using 1 thread, search_k = 18200
05:28:01 Annoy recall = 100%
05:28:13 Commencing smooth kNN distance calibration using 1 thread
05:28:37 Initializing from normalized Laplacian + noise
05:28:38 Commencing optimization for 500 epochs, with 240732 positive edges
05:28:54 Optimization finished
[1] "182 0.09"05:28:55 UMAP embedding parameters a = 1.611 b = 0.8844
05:28:55 Read 1203 rows and found 38 numeric columns
05:28:55 Using Annoy for neighbor search, n_neighbors = 182
05:28:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:28:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e455f47
05:28:56 Searching Annoy index using 1 thread, search_k = 18200
05:28:57 Annoy recall = 100%
05:29:09 Commencing smooth kNN distance calibration using 1 thread
05:29:34 Initializing from normalized Laplacian + noise
05:29:34 Commencing optimization for 500 epochs, with 240732 positive edges
05:29:51 Optimization finished
[1] "182 0.1"05:29:51 UMAP embedding parameters a = 1.577 b = 0.8951
05:29:51 Read 1203 rows and found 38 numeric columns
05:29:51 Using Annoy for neighbor search, n_neighbors = 182
05:29:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:29:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a95187e
05:29:52 Searching Annoy index using 1 thread, search_k = 18200
05:29:53 Annoy recall = 100%
05:30:05 Commencing smooth kNN distance calibration using 1 thread
05:30:30 Initializing from normalized Laplacian + noise
05:30:30 Commencing optimization for 500 epochs, with 240732 positive edges
05:30:47 Optimization finished
[1] "182 0.11"05:30:47 UMAP embedding parameters a = 1.544 b = 0.9058
05:30:47 Read 1203 rows and found 38 numeric columns
05:30:47 Using Annoy for neighbor search, n_neighbors = 182
05:30:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:30:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87144ad13
05:30:48 Searching Annoy index using 1 thread, search_k = 18200
05:30:49 Annoy recall = 100%
05:31:02 Commencing smooth kNN distance calibration using 1 thread
05:31:26 Initializing from normalized Laplacian + noise
05:31:26 Commencing optimization for 500 epochs, with 240732 positive edges
05:31:43 Optimization finished
[1] "182 0.12"05:31:43 UMAP embedding parameters a = 1.51 b = 0.9165
05:31:43 Read 1203 rows and found 38 numeric columns
05:31:43 Using Annoy for neighbor search, n_neighbors = 182
05:31:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:31:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e3b7cb7
05:31:44 Searching Annoy index using 1 thread, search_k = 18200
05:31:46 Annoy recall = 100%
05:31:58 Commencing smooth kNN distance calibration using 1 thread
05:32:22 Initializing from normalized Laplacian + noise
05:32:23 Commencing optimization for 500 epochs, with 240732 positive edges
05:32:39 Optimization finished
[1] "182 0.13"05:32:40 UMAP embedding parameters a = 1.478 b = 0.9272
05:32:40 Read 1203 rows and found 38 numeric columns
05:32:40 Using Annoy for neighbor search, n_neighbors = 182
05:32:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:32:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747b7afc
05:32:41 Searching Annoy index using 1 thread, search_k = 18200
05:32:42 Annoy recall = 100%
05:32:54 Commencing smooth kNN distance calibration using 1 thread
05:33:19 Initializing from normalized Laplacian + noise
05:33:19 Commencing optimization for 500 epochs, with 240732 positive edges
05:33:36 Optimization finished
[1] "182 0.14"05:33:36 UMAP embedding parameters a = 1.446 b = 0.938
05:33:36 Read 1203 rows and found 38 numeric columns
05:33:36 Using Annoy for neighbor search, n_neighbors = 182
05:33:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:33:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734cf27dc
05:33:37 Searching Annoy index using 1 thread, search_k = 18200
05:33:38 Annoy recall = 100%
05:33:50 Commencing smooth kNN distance calibration using 1 thread
05:34:15 Initializing from normalized Laplacian + noise
05:34:15 Commencing optimization for 500 epochs, with 240732 positive edges
05:34:32 Optimization finished
[1] "182 0.15"05:34:32 UMAP embedding parameters a = 1.414 b = 0.9488
05:34:32 Read 1203 rows and found 38 numeric columns
05:34:32 Using Annoy for neighbor search, n_neighbors = 182
05:34:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:34:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872beef482
05:34:33 Searching Annoy index using 1 thread, search_k = 18200
05:34:34 Annoy recall = 100%
05:34:47 Commencing smooth kNN distance calibration using 1 thread
05:35:12 Initializing from normalized Laplacian + noise
05:35:12 Commencing optimization for 500 epochs, with 240732 positive edges
05:35:28 Optimization finished
[1] "182 0.16"05:35:29 UMAP embedding parameters a = 1.383 b = 0.9596
05:35:29 Read 1203 rows and found 38 numeric columns
05:35:29 Using Annoy for neighbor search, n_neighbors = 182
05:35:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:35:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87200d56b6
05:35:30 Searching Annoy index using 1 thread, search_k = 18200
05:35:31 Annoy recall = 100%
05:35:43 Commencing smooth kNN distance calibration using 1 thread
05:36:08 Initializing from normalized Laplacian + noise
05:36:08 Commencing optimization for 500 epochs, with 240732 positive edges
05:36:25 Optimization finished
[1] "182 0.17"05:36:25 UMAP embedding parameters a = 1.352 b = 0.9704
05:36:25 Read 1203 rows and found 38 numeric columns
05:36:25 Using Annoy for neighbor search, n_neighbors = 182
05:36:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:36:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ce78822
05:36:26 Searching Annoy index using 1 thread, search_k = 18200
05:36:27 Annoy recall = 100%
05:36:39 Commencing smooth kNN distance calibration using 1 thread
05:37:04 Initializing from normalized Laplacian + noise
05:37:04 Commencing optimization for 500 epochs, with 240732 positive edges
05:37:21 Optimization finished
[1] "182 0.18"05:37:21 UMAP embedding parameters a = 1.321 b = 0.9813
05:37:21 Read 1203 rows and found 38 numeric columns
05:37:21 Using Annoy for neighbor search, n_neighbors = 182
05:37:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:37:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87567eea3c
05:37:22 Searching Annoy index using 1 thread, search_k = 18200
05:37:24 Annoy recall = 100%
05:37:36 Commencing smooth kNN distance calibration using 1 thread
05:38:00 Initializing from normalized Laplacian + noise
05:38:01 Commencing optimization for 500 epochs, with 240732 positive edges
05:38:17 Optimization finished
[1] "182 0.19"05:38:18 UMAP embedding parameters a = 1.292 b = 0.9921
05:38:18 Read 1203 rows and found 38 numeric columns
05:38:18 Using Annoy for neighbor search, n_neighbors = 182
05:38:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:38:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8750d28165
05:38:19 Searching Annoy index using 1 thread, search_k = 18200
05:38:20 Annoy recall = 100%
05:38:32 Commencing smooth kNN distance calibration using 1 thread
05:38:57 Initializing from normalized Laplacian + noise
05:38:57 Commencing optimization for 500 epochs, with 240732 positive edges
05:39:14 Optimization finished
[1] "182 0.2"05:39:14 UMAP embedding parameters a = 1.262 b = 1.003
05:39:14 Read 1203 rows and found 38 numeric columns
05:39:14 Using Annoy for neighbor search, n_neighbors = 182
05:39:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:39:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c901a84
05:39:15 Searching Annoy index using 1 thread, search_k = 18200
05:39:16 Annoy recall = 100%
05:39:29 Commencing smooth kNN distance calibration using 1 thread
05:39:53 Initializing from normalized Laplacian + noise
05:39:53 Commencing optimization for 500 epochs, with 240732 positive edges
05:40:10 Optimization finished
[1] "183 0"05:40:10 UMAP embedding parameters a = 1.933 b = 0.7905
05:40:10 Read 1203 rows and found 38 numeric columns
05:40:10 Using Annoy for neighbor search, n_neighbors = 183
05:40:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:40:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764605b45
05:40:11 Searching Annoy index using 1 thread, search_k = 18300
05:40:13 Annoy recall = 100%
05:40:25 Commencing smooth kNN distance calibration using 1 thread
05:40:50 Initializing from normalized Laplacian + noise
05:40:50 Commencing optimization for 500 epochs, with 241908 positive edges
05:41:06 Optimization finished
[1] "183 0.01"05:41:07 UMAP embedding parameters a = 1.896 b = 0.8006
05:41:07 Read 1203 rows and found 38 numeric columns
05:41:07 Using Annoy for neighbor search, n_neighbors = 183
05:41:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:41:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87277782b6
05:41:08 Searching Annoy index using 1 thread, search_k = 18300
05:41:09 Annoy recall = 100%
05:41:22 Commencing smooth kNN distance calibration using 1 thread
05:41:47 Initializing from normalized Laplacian + noise
05:41:47 Commencing optimization for 500 epochs, with 241908 positive edges
05:42:03 Optimization finished
[1] "183 0.02"05:42:04 UMAP embedding parameters a = 1.859 b = 0.8109
05:42:04 Read 1203 rows and found 38 numeric columns
05:42:04 Using Annoy for neighbor search, n_neighbors = 183
05:42:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:42:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87497db396
05:42:05 Searching Annoy index using 1 thread, search_k = 18300
05:42:06 Annoy recall = 100%
05:42:18 Commencing smooth kNN distance calibration using 1 thread
05:42:43 Initializing from normalized Laplacian + noise
05:42:44 Commencing optimization for 500 epochs, with 241908 positive edges
05:43:00 Optimization finished
[1] "183 0.03"05:43:00 UMAP embedding parameters a = 1.822 b = 0.8212
05:43:00 Read 1203 rows and found 38 numeric columns
05:43:01 Using Annoy for neighbor search, n_neighbors = 183
05:43:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:43:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877dbefc4c
05:43:01 Searching Annoy index using 1 thread, search_k = 18300
05:43:03 Annoy recall = 100%
05:43:15 Commencing smooth kNN distance calibration using 1 thread
05:43:40 Initializing from normalized Laplacian + noise
05:43:40 Commencing optimization for 500 epochs, with 241908 positive edges
05:43:57 Optimization finished
[1] "183 0.04"05:43:57 UMAP embedding parameters a = 1.786 b = 0.8316
05:43:57 Read 1203 rows and found 38 numeric columns
05:43:57 Using Annoy for neighbor search, n_neighbors = 183
05:43:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:43:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737a1d041
05:43:58 Searching Annoy index using 1 thread, search_k = 18300
05:44:00 Annoy recall = 100%
05:44:12 Commencing smooth kNN distance calibration using 1 thread
05:44:37 Initializing from normalized Laplacian + noise
05:44:37 Commencing optimization for 500 epochs, with 241908 positive edges
05:44:54 Optimization finished
[1] "183 0.05"05:44:54 UMAP embedding parameters a = 1.75 b = 0.8421
05:44:54 Read 1203 rows and found 38 numeric columns
05:44:54 Using Annoy for neighbor search, n_neighbors = 183
05:44:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:44:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872415c834
05:44:55 Searching Annoy index using 1 thread, search_k = 18300
05:44:57 Annoy recall = 100%
05:45:09 Commencing smooth kNN distance calibration using 1 thread
05:45:34 Initializing from normalized Laplacian + noise
05:45:34 Commencing optimization for 500 epochs, with 241908 positive edges
05:45:51 Optimization finished
[1] "183 0.06"05:45:51 UMAP embedding parameters a = 1.715 b = 0.8526
05:45:51 Read 1203 rows and found 38 numeric columns
05:45:51 Using Annoy for neighbor search, n_neighbors = 183
05:45:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:45:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87786b5c6d
05:45:52 Searching Annoy index using 1 thread, search_k = 18300
05:45:54 Annoy recall = 100%
05:46:06 Commencing smooth kNN distance calibration using 1 thread
05:46:31 Initializing from normalized Laplacian + noise
05:46:31 Commencing optimization for 500 epochs, with 241908 positive edges
05:46:48 Optimization finished
[1] "183 0.07"05:46:48 UMAP embedding parameters a = 1.68 b = 0.8631
05:46:48 Read 1203 rows and found 38 numeric columns
05:46:48 Using Annoy for neighbor search, n_neighbors = 183
05:46:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:46:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87109faf29
05:46:49 Searching Annoy index using 1 thread, search_k = 18300
05:46:50 Annoy recall = 100%
05:47:03 Commencing smooth kNN distance calibration using 1 thread
05:47:28 Initializing from normalized Laplacian + noise
05:47:28 Commencing optimization for 500 epochs, with 241908 positive edges
05:47:45 Optimization finished
[1] "183 0.08"05:47:45 UMAP embedding parameters a = 1.645 b = 0.8737
05:47:45 Read 1203 rows and found 38 numeric columns
05:47:45 Using Annoy for neighbor search, n_neighbors = 183
05:47:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:47:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746d909b1
05:47:46 Searching Annoy index using 1 thread, search_k = 18300
05:47:47 Annoy recall = 100%
05:48:00 Commencing smooth kNN distance calibration using 1 thread
05:48:25 Initializing from normalized Laplacian + noise
05:48:25 Commencing optimization for 500 epochs, with 241908 positive edges
05:48:42 Optimization finished
[1] "183 0.09"05:48:42 UMAP embedding parameters a = 1.611 b = 0.8844
05:48:42 Read 1203 rows and found 38 numeric columns
05:48:42 Using Annoy for neighbor search, n_neighbors = 183
05:48:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:48:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876818ce6d
05:48:43 Searching Annoy index using 1 thread, search_k = 18300
05:48:44 Annoy recall = 100%
05:48:57 Commencing smooth kNN distance calibration using 1 thread
05:49:21 Initializing from normalized Laplacian + noise
05:49:22 Commencing optimization for 500 epochs, with 241908 positive edges
05:49:38 Optimization finished
[1] "183 0.1"05:49:39 UMAP embedding parameters a = 1.577 b = 0.8951
05:49:39 Read 1203 rows and found 38 numeric columns
05:49:39 Using Annoy for neighbor search, n_neighbors = 183
05:49:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:49:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87397ad0f8
05:49:40 Searching Annoy index using 1 thread, search_k = 18300
05:49:41 Annoy recall = 100%
05:49:53 Commencing smooth kNN distance calibration using 1 thread
05:50:19 Initializing from normalized Laplacian + noise
05:50:19 Commencing optimization for 500 epochs, with 241908 positive edges
05:50:35 Optimization finished
[1] "183 0.11"05:50:36 UMAP embedding parameters a = 1.544 b = 0.9058
05:50:36 Read 1203 rows and found 38 numeric columns
05:50:36 Using Annoy for neighbor search, n_neighbors = 183
05:50:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:50:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734dad615
05:50:37 Searching Annoy index using 1 thread, search_k = 18300
05:50:38 Annoy recall = 100%
05:50:50 Commencing smooth kNN distance calibration using 1 thread
05:51:15 Initializing from normalized Laplacian + noise
05:51:16 Commencing optimization for 500 epochs, with 241908 positive edges
05:51:32 Optimization finished
[1] "183 0.12"05:51:33 UMAP embedding parameters a = 1.51 b = 0.9165
05:51:33 Read 1203 rows and found 38 numeric columns
05:51:33 Using Annoy for neighbor search, n_neighbors = 183
05:51:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:51:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f8631b8
05:51:34 Searching Annoy index using 1 thread, search_k = 18300
05:51:35 Annoy recall = 100%
05:51:47 Commencing smooth kNN distance calibration using 1 thread
05:52:12 Initializing from normalized Laplacian + noise
05:52:13 Commencing optimization for 500 epochs, with 241908 positive edges
05:52:29 Optimization finished
[1] "183 0.13"05:52:30 UMAP embedding parameters a = 1.478 b = 0.9272
05:52:30 Read 1203 rows and found 38 numeric columns
05:52:30 Using Annoy for neighbor search, n_neighbors = 183
05:52:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:52:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b4d85fe
05:52:31 Searching Annoy index using 1 thread, search_k = 18300
05:52:32 Annoy recall = 100%
05:52:44 Commencing smooth kNN distance calibration using 1 thread
05:53:09 Initializing from normalized Laplacian + noise
05:53:09 Commencing optimization for 500 epochs, with 241908 positive edges
05:53:26 Optimization finished
[1] "183 0.14"05:53:27 UMAP embedding parameters a = 1.446 b = 0.938
05:53:27 Read 1203 rows and found 38 numeric columns
05:53:27 Using Annoy for neighbor search, n_neighbors = 183
05:53:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:53:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ed6ce2a
05:53:28 Searching Annoy index using 1 thread, search_k = 18300
05:53:29 Annoy recall = 100%
05:53:41 Commencing smooth kNN distance calibration using 1 thread
05:54:06 Initializing from normalized Laplacian + noise
05:54:06 Commencing optimization for 500 epochs, with 241908 positive edges
05:54:23 Optimization finished
[1] "183 0.15"05:54:24 UMAP embedding parameters a = 1.414 b = 0.9488
05:54:24 Read 1203 rows and found 38 numeric columns
05:54:24 Using Annoy for neighbor search, n_neighbors = 183
05:54:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:54:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778a585cb
05:54:25 Searching Annoy index using 1 thread, search_k = 18300
05:54:26 Annoy recall = 100%
05:54:38 Commencing smooth kNN distance calibration using 1 thread
05:55:03 Initializing from normalized Laplacian + noise
05:55:03 Commencing optimization for 500 epochs, with 241908 positive edges
05:55:20 Optimization finished
[1] "183 0.16"05:55:21 UMAP embedding parameters a = 1.383 b = 0.9596
05:55:21 Read 1203 rows and found 38 numeric columns
05:55:21 Using Annoy for neighbor search, n_neighbors = 183
05:55:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:55:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876937390f
05:55:22 Searching Annoy index using 1 thread, search_k = 18300
05:55:23 Annoy recall = 100%
05:55:35 Commencing smooth kNN distance calibration using 1 thread
05:56:00 Initializing from normalized Laplacian + noise
05:56:00 Commencing optimization for 500 epochs, with 241908 positive edges
05:56:17 Optimization finished
[1] "183 0.17"05:56:18 UMAP embedding parameters a = 1.352 b = 0.9704
05:56:18 Read 1203 rows and found 38 numeric columns
05:56:18 Using Annoy for neighbor search, n_neighbors = 183
05:56:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:56:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a181251
05:56:19 Searching Annoy index using 1 thread, search_k = 18300
05:56:20 Annoy recall = 100%
05:56:32 Commencing smooth kNN distance calibration using 1 thread
05:56:57 Initializing from normalized Laplacian + noise
05:56:57 Commencing optimization for 500 epochs, with 241908 positive edges
05:57:14 Optimization finished
[1] "183 0.18"05:57:15 UMAP embedding parameters a = 1.321 b = 0.9813
05:57:15 Read 1203 rows and found 38 numeric columns
05:57:15 Using Annoy for neighbor search, n_neighbors = 183
05:57:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:57:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b107cbc
05:57:16 Searching Annoy index using 1 thread, search_k = 18300
05:57:17 Annoy recall = 100%
05:57:29 Commencing smooth kNN distance calibration using 1 thread
05:57:54 Initializing from normalized Laplacian + noise
05:57:54 Commencing optimization for 500 epochs, with 241908 positive edges
05:58:11 Optimization finished
[1] "183 0.19"05:58:12 UMAP embedding parameters a = 1.292 b = 0.9921
05:58:12 Read 1203 rows and found 38 numeric columns
05:58:12 Using Annoy for neighbor search, n_neighbors = 183
05:58:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:58:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87177c9856
05:58:13 Searching Annoy index using 1 thread, search_k = 18300
05:58:14 Annoy recall = 100%
05:58:26 Commencing smooth kNN distance calibration using 1 thread
05:58:51 Initializing from normalized Laplacian + noise
05:58:52 Commencing optimization for 500 epochs, with 241908 positive edges
05:59:08 Optimization finished
[1] "183 0.2"05:59:09 UMAP embedding parameters a = 1.262 b = 1.003
05:59:09 Read 1203 rows and found 38 numeric columns
05:59:09 Using Annoy for neighbor search, n_neighbors = 183
05:59:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:59:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754ad2acf
05:59:10 Searching Annoy index using 1 thread, search_k = 18300
05:59:11 Annoy recall = 100%
05:59:23 Commencing smooth kNN distance calibration using 1 thread
05:59:49 Initializing from normalized Laplacian + noise
05:59:49 Commencing optimization for 500 epochs, with 241908 positive edges
06:00:05 Optimization finished
[1] "184 0"06:00:06 UMAP embedding parameters a = 1.933 b = 0.7905
06:00:06 Read 1203 rows and found 38 numeric columns
06:00:06 Using Annoy for neighbor search, n_neighbors = 184
06:00:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:00:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c5529d0
06:00:07 Searching Annoy index using 1 thread, search_k = 18400
06:00:08 Annoy recall = 100%
06:00:20 Commencing smooth kNN distance calibration using 1 thread
06:00:46 Initializing from normalized Laplacian + noise
06:00:46 Commencing optimization for 500 epochs, with 243088 positive edges
06:01:03 Optimization finished
[1] "184 0.01"06:01:03 UMAP embedding parameters a = 1.896 b = 0.8006
06:01:03 Read 1203 rows and found 38 numeric columns
06:01:03 Using Annoy for neighbor search, n_neighbors = 184
06:01:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:01:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b8150d
06:01:04 Searching Annoy index using 1 thread, search_k = 18400
06:01:05 Annoy recall = 100%
06:01:18 Commencing smooth kNN distance calibration using 1 thread
06:01:43 Initializing from normalized Laplacian + noise
06:01:43 Commencing optimization for 500 epochs, with 243088 positive edges
06:02:00 Optimization finished
[1] "184 0.02"06:02:00 UMAP embedding parameters a = 1.859 b = 0.8109
06:02:00 Read 1203 rows and found 38 numeric columns
06:02:00 Using Annoy for neighbor search, n_neighbors = 184
06:02:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:02:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875928a5cb
06:02:01 Searching Annoy index using 1 thread, search_k = 18400
06:02:02 Annoy recall = 100%
06:02:15 Commencing smooth kNN distance calibration using 1 thread
06:02:40 Initializing from normalized Laplacian + noise
06:02:40 Commencing optimization for 500 epochs, with 243088 positive edges
06:02:57 Optimization finished
[1] "184 0.03"06:02:57 UMAP embedding parameters a = 1.822 b = 0.8212
06:02:57 Read 1203 rows and found 38 numeric columns
06:02:57 Using Annoy for neighbor search, n_neighbors = 184
06:02:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:02:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87412451ac
06:02:58 Searching Annoy index using 1 thread, search_k = 18400
06:03:00 Annoy recall = 100%
06:03:12 Commencing smooth kNN distance calibration using 1 thread
06:03:37 Initializing from normalized Laplacian + noise
06:03:37 Commencing optimization for 500 epochs, with 243088 positive edges
06:03:54 Optimization finished
[1] "184 0.04"06:03:54 UMAP embedding parameters a = 1.786 b = 0.8316
06:03:54 Read 1203 rows and found 38 numeric columns
06:03:54 Using Annoy for neighbor search, n_neighbors = 184
06:03:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:03:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731a7098f
06:03:55 Searching Annoy index using 1 thread, search_k = 18400
06:03:57 Annoy recall = 100%
06:04:09 Commencing smooth kNN distance calibration using 1 thread
06:04:34 Initializing from normalized Laplacian + noise
06:04:34 Commencing optimization for 500 epochs, with 243088 positive edges
06:04:51 Optimization finished
[1] "184 0.05"06:04:52 UMAP embedding parameters a = 1.75 b = 0.8421
06:04:52 Read 1203 rows and found 38 numeric columns
06:04:52 Using Annoy for neighbor search, n_neighbors = 184
06:04:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:04:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877935fc82
06:04:53 Searching Annoy index using 1 thread, search_k = 18400
06:04:54 Annoy recall = 100%
06:05:06 Commencing smooth kNN distance calibration using 1 thread
06:05:31 Initializing from normalized Laplacian + noise
06:05:31 Commencing optimization for 500 epochs, with 243088 positive edges
06:05:48 Optimization finished
[1] "184 0.06"06:05:49 UMAP embedding parameters a = 1.715 b = 0.8526
06:05:49 Read 1203 rows and found 38 numeric columns
06:05:49 Using Annoy for neighbor search, n_neighbors = 184
06:05:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:05:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e0bd9ce
06:05:50 Searching Annoy index using 1 thread, search_k = 18400
06:05:51 Annoy recall = 100%
06:06:04 Commencing smooth kNN distance calibration using 1 thread
06:06:28 Initializing from normalized Laplacian + noise
06:06:29 Commencing optimization for 500 epochs, with 243088 positive edges
06:06:46 Optimization finished
[1] "184 0.07"06:06:46 UMAP embedding parameters a = 1.68 b = 0.8631
06:06:46 Read 1203 rows and found 38 numeric columns
06:06:46 Using Annoy for neighbor search, n_neighbors = 184
06:06:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:06:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87825f3cc
06:06:47 Searching Annoy index using 1 thread, search_k = 18400
06:06:48 Annoy recall = 100%
06:07:01 Commencing smooth kNN distance calibration using 1 thread
06:07:26 Initializing from normalized Laplacian + noise
06:07:26 Commencing optimization for 500 epochs, with 243088 positive edges
06:07:43 Optimization finished
[1] "184 0.08"06:07:43 UMAP embedding parameters a = 1.645 b = 0.8737
06:07:43 Read 1203 rows and found 38 numeric columns
06:07:43 Using Annoy for neighbor search, n_neighbors = 184
06:07:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:07:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a087de7
06:07:44 Searching Annoy index using 1 thread, search_k = 18400
06:07:45 Annoy recall = 100%
06:07:58 Commencing smooth kNN distance calibration using 1 thread
06:08:23 Initializing from normalized Laplacian + noise
06:08:23 Commencing optimization for 500 epochs, with 243088 positive edges
06:08:40 Optimization finished
[1] "184 0.09"06:08:40 UMAP embedding parameters a = 1.611 b = 0.8844
06:08:40 Read 1203 rows and found 38 numeric columns
06:08:40 Using Annoy for neighbor search, n_neighbors = 184
06:08:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:08:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a9bf452
06:08:41 Searching Annoy index using 1 thread, search_k = 18400
06:08:42 Annoy recall = 100%
06:08:55 Commencing smooth kNN distance calibration using 1 thread
06:09:20 Initializing from normalized Laplacian + noise
06:09:20 Commencing optimization for 500 epochs, with 243088 positive edges
06:09:37 Optimization finished
[1] "184 0.1"06:09:37 UMAP embedding parameters a = 1.577 b = 0.8951
06:09:37 Read 1203 rows and found 38 numeric columns
06:09:37 Using Annoy for neighbor search, n_neighbors = 184
06:09:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:09:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c864f11
06:09:38 Searching Annoy index using 1 thread, search_k = 18400
06:09:40 Annoy recall = 100%
06:09:52 Commencing smooth kNN distance calibration using 1 thread
06:10:17 Initializing from normalized Laplacian + noise
06:10:18 Commencing optimization for 500 epochs, with 243088 positive edges
06:10:34 Optimization finished
[1] "184 0.11"06:10:35 UMAP embedding parameters a = 1.544 b = 0.9058
06:10:35 Read 1203 rows and found 38 numeric columns
06:10:35 Using Annoy for neighbor search, n_neighbors = 184
06:10:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:10:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877180009e
06:10:36 Searching Annoy index using 1 thread, search_k = 18400
06:10:37 Annoy recall = 100%
06:10:49 Commencing smooth kNN distance calibration using 1 thread
06:11:15 Initializing from normalized Laplacian + noise
06:11:15 Commencing optimization for 500 epochs, with 243088 positive edges
06:11:32 Optimization finished
[1] "184 0.12"06:11:32 UMAP embedding parameters a = 1.51 b = 0.9165
06:11:32 Read 1203 rows and found 38 numeric columns
06:11:32 Using Annoy for neighbor search, n_neighbors = 184
06:11:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:11:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873419a7e8
06:11:33 Searching Annoy index using 1 thread, search_k = 18400
06:11:34 Annoy recall = 100%
06:11:47 Commencing smooth kNN distance calibration using 1 thread
06:12:12 Initializing from normalized Laplacian + noise
06:12:12 Commencing optimization for 500 epochs, with 243088 positive edges
06:12:29 Optimization finished
[1] "184 0.13"06:12:29 UMAP embedding parameters a = 1.478 b = 0.9272
06:12:29 Read 1203 rows and found 38 numeric columns
06:12:29 Using Annoy for neighbor search, n_neighbors = 184
06:12:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:12:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a454b5d
06:12:30 Searching Annoy index using 1 thread, search_k = 18400
06:12:32 Annoy recall = 100%
06:12:44 Commencing smooth kNN distance calibration using 1 thread
06:13:09 Initializing from normalized Laplacian + noise
06:13:09 Commencing optimization for 500 epochs, with 243088 positive edges
06:13:26 Optimization finished
[1] "184 0.14"06:13:27 UMAP embedding parameters a = 1.446 b = 0.938
06:13:27 Read 1203 rows and found 38 numeric columns
06:13:27 Using Annoy for neighbor search, n_neighbors = 184
06:13:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:13:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872921d0df
06:13:28 Searching Annoy index using 1 thread, search_k = 18400
06:13:29 Annoy recall = 100%
06:13:41 Commencing smooth kNN distance calibration using 1 thread
06:14:06 Initializing from normalized Laplacian + noise
06:14:06 Commencing optimization for 500 epochs, with 243088 positive edges
06:14:24 Optimization finished
[1] "184 0.15"06:14:24 UMAP embedding parameters a = 1.414 b = 0.9488
06:14:24 Read 1203 rows and found 38 numeric columns
06:14:24 Using Annoy for neighbor search, n_neighbors = 184
06:14:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:14:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87582f701c
06:14:25 Searching Annoy index using 1 thread, search_k = 18400
06:14:26 Annoy recall = 100%
06:14:39 Commencing smooth kNN distance calibration using 1 thread
06:15:04 Initializing from normalized Laplacian + noise
06:15:04 Commencing optimization for 500 epochs, with 243088 positive edges
06:15:21 Optimization finished
[1] "184 0.16"06:15:21 UMAP embedding parameters a = 1.383 b = 0.9596
06:15:21 Read 1203 rows and found 38 numeric columns
06:15:21 Using Annoy for neighbor search, n_neighbors = 184
06:15:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:15:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762b0a7cb
06:15:22 Searching Annoy index using 1 thread, search_k = 18400
06:15:23 Annoy recall = 100%
06:15:36 Commencing smooth kNN distance calibration using 1 thread
06:16:01 Initializing from normalized Laplacian + noise
06:16:01 Commencing optimization for 500 epochs, with 243088 positive edges
06:16:18 Optimization finished
[1] "184 0.17"06:16:18 UMAP embedding parameters a = 1.352 b = 0.9704
06:16:18 Read 1203 rows and found 38 numeric columns
06:16:18 Using Annoy for neighbor search, n_neighbors = 184
06:16:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:16:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739c18009
06:16:19 Searching Annoy index using 1 thread, search_k = 18400
06:16:21 Annoy recall = 100%
06:16:33 Commencing smooth kNN distance calibration using 1 thread
06:16:58 Initializing from normalized Laplacian + noise
06:16:58 Commencing optimization for 500 epochs, with 243088 positive edges
06:17:15 Optimization finished
[1] "184 0.18"06:17:16 UMAP embedding parameters a = 1.321 b = 0.9813
06:17:16 Read 1203 rows and found 38 numeric columns
06:17:16 Using Annoy for neighbor search, n_neighbors = 184
06:17:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:17:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f0879ce
06:17:17 Searching Annoy index using 1 thread, search_k = 18400
06:17:18 Annoy recall = 100%
06:17:31 Commencing smooth kNN distance calibration using 1 thread
06:17:56 Initializing from normalized Laplacian + noise
06:17:56 Commencing optimization for 500 epochs, with 243088 positive edges
06:18:13 Optimization finished
[1] "184 0.19"06:18:13 UMAP embedding parameters a = 1.292 b = 0.9921
06:18:13 Read 1203 rows and found 38 numeric columns
06:18:13 Using Annoy for neighbor search, n_neighbors = 184
06:18:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:18:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874ac97638
06:18:14 Searching Annoy index using 1 thread, search_k = 18400
06:18:15 Annoy recall = 100%
06:18:28 Commencing smooth kNN distance calibration using 1 thread
06:18:53 Initializing from normalized Laplacian + noise
06:18:53 Commencing optimization for 500 epochs, with 243088 positive edges
06:19:10 Optimization finished
[1] "184 0.2"06:19:10 UMAP embedding parameters a = 1.262 b = 1.003
06:19:10 Read 1203 rows and found 38 numeric columns
06:19:10 Using Annoy for neighbor search, n_neighbors = 184
06:19:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:19:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87733c5101
06:19:11 Searching Annoy index using 1 thread, search_k = 18400
06:19:13 Annoy recall = 100%
06:19:25 Commencing smooth kNN distance calibration using 1 thread
06:19:50 Initializing from normalized Laplacian + noise
06:19:51 Commencing optimization for 500 epochs, with 243088 positive edges
06:20:07 Optimization finished
[1] "185 0"06:20:08 UMAP embedding parameters a = 1.933 b = 0.7905
06:20:08 Read 1203 rows and found 38 numeric columns
06:20:08 Using Annoy for neighbor search, n_neighbors = 185
06:20:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:20:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753e34fe3
06:20:09 Searching Annoy index using 1 thread, search_k = 18500
06:20:10 Annoy recall = 100%
06:20:22 Commencing smooth kNN distance calibration using 1 thread
06:20:48 Initializing from normalized Laplacian + noise
06:20:48 Commencing optimization for 500 epochs, with 244224 positive edges
06:21:05 Optimization finished
[1] "185 0.01"06:21:05 UMAP embedding parameters a = 1.896 b = 0.8006
06:21:05 Read 1203 rows and found 38 numeric columns
06:21:05 Using Annoy for neighbor search, n_neighbors = 185
06:21:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:21:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a4fa7f0
06:21:06 Searching Annoy index using 1 thread, search_k = 18500
06:21:07 Annoy recall = 100%
06:21:20 Commencing smooth kNN distance calibration using 1 thread
06:21:45 Initializing from normalized Laplacian + noise
06:21:45 Commencing optimization for 500 epochs, with 244224 positive edges
06:22:02 Optimization finished
[1] "185 0.02"06:22:03 UMAP embedding parameters a = 1.859 b = 0.8109
06:22:03 Read 1203 rows and found 38 numeric columns
06:22:03 Using Annoy for neighbor search, n_neighbors = 185
06:22:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:22:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e89d6ff
06:22:04 Searching Annoy index using 1 thread, search_k = 18500
06:22:05 Annoy recall = 100%
06:22:17 Commencing smooth kNN distance calibration using 1 thread
06:22:42 Initializing from normalized Laplacian + noise
06:22:43 Commencing optimization for 500 epochs, with 244224 positive edges
06:23:00 Optimization finished
[1] "185 0.03"06:23:00 UMAP embedding parameters a = 1.822 b = 0.8212
06:23:00 Read 1203 rows and found 38 numeric columns
06:23:00 Using Annoy for neighbor search, n_neighbors = 185
06:23:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:23:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722ba1e0d
06:23:01 Searching Annoy index using 1 thread, search_k = 18500
06:23:02 Annoy recall = 100%
06:23:15 Commencing smooth kNN distance calibration using 1 thread
06:23:40 Initializing from normalized Laplacian + noise
06:23:40 Commencing optimization for 500 epochs, with 244224 positive edges
06:23:57 Optimization finished
[1] "185 0.04"06:23:57 UMAP embedding parameters a = 1.786 b = 0.8316
06:23:57 Read 1203 rows and found 38 numeric columns
06:23:57 Using Annoy for neighbor search, n_neighbors = 185
06:23:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:23:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8772f52dbb
06:23:58 Searching Annoy index using 1 thread, search_k = 18500
06:23:59 Annoy recall = 100%
06:24:12 Commencing smooth kNN distance calibration using 1 thread
06:24:37 Initializing from normalized Laplacian + noise
06:24:37 Commencing optimization for 500 epochs, with 244224 positive edges
06:24:54 Optimization finished
[1] "185 0.05"06:24:54 UMAP embedding parameters a = 1.75 b = 0.8421
06:24:54 Read 1203 rows and found 38 numeric columns
06:24:54 Using Annoy for neighbor search, n_neighbors = 185
06:24:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:24:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747c1100e
06:24:55 Searching Annoy index using 1 thread, search_k = 18500
06:24:57 Annoy recall = 100%
06:25:09 Commencing smooth kNN distance calibration using 1 thread
06:25:34 Initializing from normalized Laplacian + noise
06:25:34 Commencing optimization for 500 epochs, with 244224 positive edges
06:25:51 Optimization finished
[1] "185 0.06"06:25:52 UMAP embedding parameters a = 1.715 b = 0.8526
06:25:52 Read 1203 rows and found 38 numeric columns
06:25:52 Using Annoy for neighbor search, n_neighbors = 185
06:25:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:25:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877cd2305e
06:25:53 Searching Annoy index using 1 thread, search_k = 18500
06:25:54 Annoy recall = 100%
06:26:06 Commencing smooth kNN distance calibration using 1 thread
06:26:32 Initializing from normalized Laplacian + noise
06:26:32 Commencing optimization for 500 epochs, with 244224 positive edges
06:26:49 Optimization finished
[1] "185 0.07"06:26:49 UMAP embedding parameters a = 1.68 b = 0.8631
06:26:49 Read 1203 rows and found 38 numeric columns
06:26:49 Using Annoy for neighbor search, n_neighbors = 185
06:26:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:26:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e05aa78
06:26:50 Searching Annoy index using 1 thread, search_k = 18500
06:26:51 Annoy recall = 100%
06:27:04 Commencing smooth kNN distance calibration using 1 thread
06:27:29 Initializing from normalized Laplacian + noise
06:27:29 Commencing optimization for 500 epochs, with 244224 positive edges
06:27:46 Optimization finished
[1] "185 0.08"06:27:46 UMAP embedding parameters a = 1.645 b = 0.8737
06:27:46 Read 1203 rows and found 38 numeric columns
06:27:46 Using Annoy for neighbor search, n_neighbors = 185
06:27:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:27:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f3da865
06:27:47 Searching Annoy index using 1 thread, search_k = 18500
06:27:48 Annoy recall = 100%
06:28:01 Commencing smooth kNN distance calibration using 1 thread
06:28:26 Initializing from normalized Laplacian + noise
06:28:26 Commencing optimization for 500 epochs, with 244224 positive edges
06:28:43 Optimization finished
[1] "185 0.09"06:28:43 UMAP embedding parameters a = 1.611 b = 0.8844
06:28:44 Read 1203 rows and found 38 numeric columns
06:28:44 Using Annoy for neighbor search, n_neighbors = 185
06:28:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:28:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87517f5b2e
06:28:45 Searching Annoy index using 1 thread, search_k = 18500
06:28:46 Annoy recall = 100%
06:28:58 Commencing smooth kNN distance calibration using 1 thread
06:29:23 Initializing from normalized Laplacian + noise
06:29:24 Commencing optimization for 500 epochs, with 244224 positive edges
06:29:41 Optimization finished
[1] "185 0.1"06:29:41 UMAP embedding parameters a = 1.577 b = 0.8951
06:29:41 Read 1203 rows and found 38 numeric columns
06:29:41 Using Annoy for neighbor search, n_neighbors = 185
06:29:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:29:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a5ad448
06:29:42 Searching Annoy index using 1 thread, search_k = 18500
06:29:43 Annoy recall = 100%
06:29:55 Commencing smooth kNN distance calibration using 1 thread
06:30:21 Initializing from normalized Laplacian + noise
06:30:21 Commencing optimization for 500 epochs, with 244224 positive edges
06:30:38 Optimization finished
[1] "185 0.11"06:30:38 UMAP embedding parameters a = 1.544 b = 0.9058
06:30:38 Read 1203 rows and found 38 numeric columns
06:30:38 Using Annoy for neighbor search, n_neighbors = 185
06:30:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:30:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764f5bd72
06:30:39 Searching Annoy index using 1 thread, search_k = 18500
06:30:40 Annoy recall = 100%
06:30:53 Commencing smooth kNN distance calibration using 1 thread
06:31:18 Initializing from normalized Laplacian + noise
06:31:18 Commencing optimization for 500 epochs, with 244224 positive edges
06:31:35 Optimization finished
[1] "185 0.12"06:31:35 UMAP embedding parameters a = 1.51 b = 0.9165
06:31:35 Read 1203 rows and found 38 numeric columns
06:31:35 Using Annoy for neighbor search, n_neighbors = 185
06:31:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:31:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872aa800f9
06:31:36 Searching Annoy index using 1 thread, search_k = 18500
06:31:38 Annoy recall = 100%
06:31:50 Commencing smooth kNN distance calibration using 1 thread
06:32:15 Initializing from normalized Laplacian + noise
06:32:15 Commencing optimization for 500 epochs, with 244224 positive edges
06:32:33 Optimization finished
[1] "185 0.13"06:32:33 UMAP embedding parameters a = 1.478 b = 0.9272
06:32:33 Read 1203 rows and found 38 numeric columns
06:32:33 Using Annoy for neighbor search, n_neighbors = 185
06:32:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:32:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b7f25f4
06:32:34 Searching Annoy index using 1 thread, search_k = 18500
06:32:35 Annoy recall = 100%
06:32:48 Commencing smooth kNN distance calibration using 1 thread
06:33:13 Initializing from normalized Laplacian + noise
06:33:13 Commencing optimization for 500 epochs, with 244224 positive edges
06:33:30 Optimization finished
[1] "185 0.14"06:33:30 UMAP embedding parameters a = 1.446 b = 0.938
06:33:30 Read 1203 rows and found 38 numeric columns
06:33:30 Using Annoy for neighbor search, n_neighbors = 185
06:33:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:33:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87169cc702
06:33:31 Searching Annoy index using 1 thread, search_k = 18500
06:33:32 Annoy recall = 100%
06:33:45 Commencing smooth kNN distance calibration using 1 thread
06:34:10 Initializing from normalized Laplacian + noise
06:34:10 Commencing optimization for 500 epochs, with 244224 positive edges
06:34:27 Optimization finished
[1] "185 0.15"06:34:27 UMAP embedding parameters a = 1.414 b = 0.9488
06:34:27 Read 1203 rows and found 38 numeric columns
06:34:27 Using Annoy for neighbor search, n_neighbors = 185
06:34:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:34:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8723ddfd7b
06:34:28 Searching Annoy index using 1 thread, search_k = 18500
06:34:30 Annoy recall = 100%
06:34:42 Commencing smooth kNN distance calibration using 1 thread
06:35:08 Initializing from normalized Laplacian + noise
06:35:08 Commencing optimization for 500 epochs, with 244224 positive edges
06:35:25 Optimization finished
[1] "185 0.16"06:35:25 UMAP embedding parameters a = 1.383 b = 0.9596
06:35:25 Read 1203 rows and found 38 numeric columns
06:35:25 Using Annoy for neighbor search, n_neighbors = 185
06:35:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:35:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8798affc3
06:35:26 Searching Annoy index using 1 thread, search_k = 18500
06:35:27 Annoy recall = 100%
06:35:40 Commencing smooth kNN distance calibration using 1 thread
06:36:05 Initializing from normalized Laplacian + noise
06:36:05 Commencing optimization for 500 epochs, with 244224 positive edges
06:36:22 Optimization finished
[1] "185 0.17"06:36:22 UMAP embedding parameters a = 1.352 b = 0.9704
06:36:22 Read 1203 rows and found 38 numeric columns
06:36:22 Using Annoy for neighbor search, n_neighbors = 185
06:36:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:36:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ec2bace
06:36:23 Searching Annoy index using 1 thread, search_k = 18500
06:36:25 Annoy recall = 100%
06:36:37 Commencing smooth kNN distance calibration using 1 thread
06:37:02 Initializing from normalized Laplacian + noise
06:37:03 Commencing optimization for 500 epochs, with 244224 positive edges
06:37:19 Optimization finished
[1] "185 0.18"06:37:20 UMAP embedding parameters a = 1.321 b = 0.9813
06:37:20 Read 1203 rows and found 38 numeric columns
06:37:20 Using Annoy for neighbor search, n_neighbors = 185
06:37:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:37:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876de67b63
06:37:21 Searching Annoy index using 1 thread, search_k = 18500
06:37:22 Annoy recall = 100%
06:37:34 Commencing smooth kNN distance calibration using 1 thread
06:38:00 Initializing from normalized Laplacian + noise
06:38:00 Commencing optimization for 500 epochs, with 244224 positive edges
06:38:17 Optimization finished
[1] "185 0.19"06:38:17 UMAP embedding parameters a = 1.292 b = 0.9921
06:38:17 Read 1203 rows and found 38 numeric columns
06:38:17 Using Annoy for neighbor search, n_neighbors = 185
06:38:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:38:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877426f415
06:38:18 Searching Annoy index using 1 thread, search_k = 18500
06:38:20 Annoy recall = 100%
06:38:32 Commencing smooth kNN distance calibration using 1 thread
06:38:57 Initializing from normalized Laplacian + noise
06:38:57 Commencing optimization for 500 epochs, with 244224 positive edges
06:39:14 Optimization finished
[1] "185 0.2"06:39:15 UMAP embedding parameters a = 1.262 b = 1.003
06:39:15 Read 1203 rows and found 38 numeric columns
06:39:15 Using Annoy for neighbor search, n_neighbors = 185
06:39:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:39:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b4909df
06:39:16 Searching Annoy index using 1 thread, search_k = 18500
06:39:17 Annoy recall = 100%
06:39:29 Commencing smooth kNN distance calibration using 1 thread
06:39:55 Initializing from normalized Laplacian + noise
06:39:55 Commencing optimization for 500 epochs, with 244224 positive edges
06:40:12 Optimization finished
[1] "186 0"06:40:12 UMAP embedding parameters a = 1.933 b = 0.7905
06:40:12 Read 1203 rows and found 38 numeric columns
06:40:12 Using Annoy for neighbor search, n_neighbors = 186
06:40:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:40:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f667c01
06:40:13 Searching Annoy index using 1 thread, search_k = 18600
06:40:14 Annoy recall = 100%
06:40:27 Commencing smooth kNN distance calibration using 1 thread
06:40:52 Initializing from normalized Laplacian + noise
06:40:52 Commencing optimization for 500 epochs, with 245396 positive edges
06:41:09 Optimization finished
[1] "186 0.01"06:41:10 UMAP embedding parameters a = 1.896 b = 0.8006
06:41:10 Read 1203 rows and found 38 numeric columns
06:41:10 Using Annoy for neighbor search, n_neighbors = 186
06:41:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:41:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8728409bfe
06:41:11 Searching Annoy index using 1 thread, search_k = 18600
06:41:12 Annoy recall = 100%
06:41:24 Commencing smooth kNN distance calibration using 1 thread
06:41:49 Initializing from normalized Laplacian + noise
06:41:50 Commencing optimization for 500 epochs, with 245396 positive edges
06:42:07 Optimization finished
[1] "186 0.02"06:42:07 UMAP embedding parameters a = 1.859 b = 0.8109
06:42:07 Read 1203 rows and found 38 numeric columns
06:42:07 Using Annoy for neighbor search, n_neighbors = 186
06:42:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:42:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87758e553c
06:42:08 Searching Annoy index using 1 thread, search_k = 18600
06:42:09 Annoy recall = 100%
06:42:22 Commencing smooth kNN distance calibration using 1 thread
06:42:47 Initializing from normalized Laplacian + noise
06:42:47 Commencing optimization for 500 epochs, with 245396 positive edges
06:43:04 Optimization finished
[1] "186 0.03"06:43:04 UMAP embedding parameters a = 1.822 b = 0.8212
06:43:04 Read 1203 rows and found 38 numeric columns
06:43:04 Using Annoy for neighbor search, n_neighbors = 186
06:43:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:43:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878884ce0
06:43:05 Searching Annoy index using 1 thread, search_k = 18600
06:43:07 Annoy recall = 100%
06:43:19 Commencing smooth kNN distance calibration using 1 thread
06:43:45 Initializing from normalized Laplacian + noise
06:43:45 Commencing optimization for 500 epochs, with 245396 positive edges
06:44:02 Optimization finished
[1] "186 0.04"06:44:02 UMAP embedding parameters a = 1.786 b = 0.8316
06:44:02 Read 1203 rows and found 38 numeric columns
06:44:02 Using Annoy for neighbor search, n_neighbors = 186
06:44:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:44:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87700c1a
06:44:03 Searching Annoy index using 1 thread, search_k = 18600
06:44:04 Annoy recall = 100%
06:44:17 Commencing smooth kNN distance calibration using 1 thread
06:44:42 Initializing from normalized Laplacian + noise
06:44:42 Commencing optimization for 500 epochs, with 245396 positive edges
06:44:59 Optimization finished
[1] "186 0.05"06:44:59 UMAP embedding parameters a = 1.75 b = 0.8421
06:45:00 Read 1203 rows and found 38 numeric columns
06:45:00 Using Annoy for neighbor search, n_neighbors = 186
06:45:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:45:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87583efd07
06:45:01 Searching Annoy index using 1 thread, search_k = 18600
06:45:02 Annoy recall = 100%
06:45:14 Commencing smooth kNN distance calibration using 1 thread
06:45:40 Initializing from normalized Laplacian + noise
06:45:40 Commencing optimization for 500 epochs, with 245396 positive edges
06:45:57 Optimization finished
[1] "186 0.06"06:45:57 UMAP embedding parameters a = 1.715 b = 0.8526
06:45:57 Read 1203 rows and found 38 numeric columns
06:45:57 Using Annoy for neighbor search, n_neighbors = 186
06:45:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:45:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874249cce9
06:45:58 Searching Annoy index using 1 thread, search_k = 18600
06:45:59 Annoy recall = 100%
06:46:12 Commencing smooth kNN distance calibration using 1 thread
06:46:37 Initializing from normalized Laplacian + noise
06:46:37 Commencing optimization for 500 epochs, with 245396 positive edges
06:46:55 Optimization finished
[1] "186 0.07"06:46:55 UMAP embedding parameters a = 1.68 b = 0.8631
06:46:55 Read 1203 rows and found 38 numeric columns
06:46:55 Using Annoy for neighbor search, n_neighbors = 186
06:46:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:46:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f7885e8
06:46:56 Searching Annoy index using 1 thread, search_k = 18600
06:46:57 Annoy recall = 100%
06:47:09 Commencing smooth kNN distance calibration using 1 thread
06:47:35 Initializing from normalized Laplacian + noise
06:47:35 Commencing optimization for 500 epochs, with 245396 positive edges
06:47:52 Optimization finished
[1] "186 0.08"06:47:52 UMAP embedding parameters a = 1.645 b = 0.8737
06:47:52 Read 1203 rows and found 38 numeric columns
06:47:52 Using Annoy for neighbor search, n_neighbors = 186
06:47:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:47:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872308733f
06:47:53 Searching Annoy index using 1 thread, search_k = 18600
06:47:55 Annoy recall = 100%
06:48:07 Commencing smooth kNN distance calibration using 1 thread
06:48:32 Initializing from normalized Laplacian + noise
06:48:33 Commencing optimization for 500 epochs, with 245396 positive edges
06:48:50 Optimization finished
[1] "186 0.09"06:48:50 UMAP embedding parameters a = 1.611 b = 0.8844
06:48:50 Read 1203 rows and found 38 numeric columns
06:48:50 Using Annoy for neighbor search, n_neighbors = 186
06:48:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:48:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735861dea
06:48:51 Searching Annoy index using 1 thread, search_k = 18600
06:48:52 Annoy recall = 100%
06:49:05 Commencing smooth kNN distance calibration using 1 thread
06:49:30 Initializing from normalized Laplacian + noise
06:49:30 Commencing optimization for 500 epochs, with 245396 positive edges
06:49:47 Optimization finished
[1] "186 0.1"06:49:48 UMAP embedding parameters a = 1.577 b = 0.8951
06:49:48 Read 1203 rows and found 38 numeric columns
06:49:48 Using Annoy for neighbor search, n_neighbors = 186
06:49:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:49:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87735bd5cb
06:49:49 Searching Annoy index using 1 thread, search_k = 18600
06:49:50 Annoy recall = 100%
06:50:02 Commencing smooth kNN distance calibration using 1 thread
06:50:28 Initializing from normalized Laplacian + noise
06:50:28 Commencing optimization for 500 epochs, with 245396 positive edges
06:50:45 Optimization finished
[1] "186 0.11"06:50:45 UMAP embedding parameters a = 1.544 b = 0.9058
06:50:45 Read 1203 rows and found 38 numeric columns
06:50:45 Using Annoy for neighbor search, n_neighbors = 186
06:50:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:50:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d581b2f
06:50:46 Searching Annoy index using 1 thread, search_k = 18600
06:50:47 Annoy recall = 100%
06:51:00 Commencing smooth kNN distance calibration using 1 thread
06:51:25 Initializing from normalized Laplacian + noise
06:51:25 Commencing optimization for 500 epochs, with 245396 positive edges
06:51:42 Optimization finished
[1] "186 0.12"06:51:43 UMAP embedding parameters a = 1.51 b = 0.9165
06:51:43 Read 1203 rows and found 38 numeric columns
06:51:43 Using Annoy for neighbor search, n_neighbors = 186
06:51:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:51:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87140ff4ea
06:51:44 Searching Annoy index using 1 thread, search_k = 18600
06:51:45 Annoy recall = 100%
06:51:58 Commencing smooth kNN distance calibration using 1 thread
06:52:23 Initializing from normalized Laplacian + noise
06:52:23 Commencing optimization for 500 epochs, with 245396 positive edges
06:52:40 Optimization finished
[1] "186 0.13"06:52:40 UMAP embedding parameters a = 1.478 b = 0.9272
06:52:40 Read 1203 rows and found 38 numeric columns
06:52:40 Using Annoy for neighbor search, n_neighbors = 186
06:52:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:52:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871615f3d8
06:52:41 Searching Annoy index using 1 thread, search_k = 18600
06:52:43 Annoy recall = 100%
06:52:55 Commencing smooth kNN distance calibration using 1 thread
06:53:21 Initializing from normalized Laplacian + noise
06:53:21 Commencing optimization for 500 epochs, with 245396 positive edges
06:53:38 Optimization finished
[1] "186 0.14"06:53:38 UMAP embedding parameters a = 1.446 b = 0.938
06:53:38 Read 1203 rows and found 38 numeric columns
06:53:38 Using Annoy for neighbor search, n_neighbors = 186
06:53:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:53:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87104d48eb
06:53:39 Searching Annoy index using 1 thread, search_k = 18600
06:53:40 Annoy recall = 100%
06:53:53 Commencing smooth kNN distance calibration using 1 thread
06:54:19 Initializing from normalized Laplacian + noise
06:54:19 Commencing optimization for 500 epochs, with 245396 positive edges
06:54:36 Optimization finished
[1] "186 0.15"06:54:36 UMAP embedding parameters a = 1.414 b = 0.9488
06:54:36 Read 1203 rows and found 38 numeric columns
06:54:36 Using Annoy for neighbor search, n_neighbors = 186
06:54:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:54:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875bd104f8
06:54:37 Searching Annoy index using 1 thread, search_k = 18600
06:54:38 Annoy recall = 100%
06:54:51 Commencing smooth kNN distance calibration using 1 thread
06:55:16 Initializing from normalized Laplacian + noise
06:55:16 Commencing optimization for 500 epochs, with 245396 positive edges
06:55:33 Optimization finished
[1] "186 0.16"06:55:34 UMAP embedding parameters a = 1.383 b = 0.9596
06:55:34 Read 1203 rows and found 38 numeric columns
06:55:34 Using Annoy for neighbor search, n_neighbors = 186
06:55:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:55:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712e82437
06:55:35 Searching Annoy index using 1 thread, search_k = 18600
06:55:36 Annoy recall = 100%
06:55:48 Commencing smooth kNN distance calibration using 1 thread
06:56:14 Initializing from normalized Laplacian + noise
06:56:14 Commencing optimization for 500 epochs, with 245396 positive edges
06:56:31 Optimization finished
[1] "186 0.17"06:56:31 UMAP embedding parameters a = 1.352 b = 0.9704
06:56:31 Read 1203 rows and found 38 numeric columns
06:56:31 Using Annoy for neighbor search, n_neighbors = 186
06:56:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:56:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e52f363
06:56:32 Searching Annoy index using 1 thread, search_k = 18600
06:56:34 Annoy recall = 100%
06:56:46 Commencing smooth kNN distance calibration using 1 thread
06:57:12 Initializing from normalized Laplacian + noise
06:57:12 Commencing optimization for 500 epochs, with 245396 positive edges
06:57:29 Optimization finished
[1] "186 0.18"06:57:29 UMAP embedding parameters a = 1.321 b = 0.9813
06:57:29 Read 1203 rows and found 38 numeric columns
06:57:29 Using Annoy for neighbor search, n_neighbors = 186
06:57:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:57:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b0ead5d
06:57:30 Searching Annoy index using 1 thread, search_k = 18600
06:57:31 Annoy recall = 100%
06:57:44 Commencing smooth kNN distance calibration using 1 thread
06:58:09 Initializing from normalized Laplacian + noise
06:58:09 Commencing optimization for 500 epochs, with 245396 positive edges
06:58:27 Optimization finished
[1] "186 0.19"06:58:27 UMAP embedding parameters a = 1.292 b = 0.9921
06:58:27 Read 1203 rows and found 38 numeric columns
06:58:27 Using Annoy for neighbor search, n_neighbors = 186
06:58:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:58:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764677f65
06:58:28 Searching Annoy index using 1 thread, search_k = 18600
06:58:29 Annoy recall = 100%
06:58:42 Commencing smooth kNN distance calibration using 1 thread
06:59:07 Initializing from normalized Laplacian + noise
06:59:07 Commencing optimization for 500 epochs, with 245396 positive edges
06:59:24 Optimization finished
[1] "186 0.2"06:59:25 UMAP embedding parameters a = 1.262 b = 1.003
06:59:25 Read 1203 rows and found 38 numeric columns
06:59:25 Using Annoy for neighbor search, n_neighbors = 186
06:59:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
06:59:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718adc7ab
06:59:26 Searching Annoy index using 1 thread, search_k = 18600
06:59:27 Annoy recall = 100%
06:59:40 Commencing smooth kNN distance calibration using 1 thread
07:00:05 Initializing from normalized Laplacian + noise
07:00:05 Commencing optimization for 500 epochs, with 245396 positive edges
07:00:22 Optimization finished
[1] "187 0"07:00:22 UMAP embedding parameters a = 1.933 b = 0.7905
07:00:22 Read 1203 rows and found 38 numeric columns
07:00:22 Using Annoy for neighbor search, n_neighbors = 187
07:00:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:00:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720046ad0
07:00:23 Searching Annoy index using 1 thread, search_k = 18700
07:00:25 Annoy recall = 100%
07:00:37 Commencing smooth kNN distance calibration using 1 thread
07:01:02 Initializing from normalized Laplacian + noise
07:01:03 Commencing optimization for 500 epochs, with 246508 positive edges
07:01:20 Optimization finished
[1] "187 0.01"07:01:20 UMAP embedding parameters a = 1.896 b = 0.8006
07:01:20 Read 1203 rows and found 38 numeric columns
07:01:20 Using Annoy for neighbor search, n_neighbors = 187
07:01:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:01:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f0f805e
07:01:21 Searching Annoy index using 1 thread, search_k = 18700
07:01:22 Annoy recall = 100%
07:01:35 Commencing smooth kNN distance calibration using 1 thread
07:02:00 Initializing from normalized Laplacian + noise
07:02:00 Commencing optimization for 500 epochs, with 246508 positive edges
07:02:17 Optimization finished
[1] "187 0.02"07:02:18 UMAP embedding parameters a = 1.859 b = 0.8109
07:02:18 Read 1203 rows and found 38 numeric columns
07:02:18 Using Annoy for neighbor search, n_neighbors = 187
07:02:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:02:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87642ced9f
07:02:19 Searching Annoy index using 1 thread, search_k = 18700
07:02:20 Annoy recall = 100%
07:02:33 Commencing smooth kNN distance calibration using 1 thread
07:02:58 Initializing from normalized Laplacian + noise
07:02:58 Commencing optimization for 500 epochs, with 246508 positive edges
07:03:15 Optimization finished
[1] "187 0.03"07:03:15 UMAP embedding parameters a = 1.822 b = 0.8212
07:03:16 Read 1203 rows and found 38 numeric columns
07:03:16 Using Annoy for neighbor search, n_neighbors = 187
07:03:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:03:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736a131d2
07:03:17 Searching Annoy index using 1 thread, search_k = 18700
07:03:18 Annoy recall = 100%
07:03:31 Commencing smooth kNN distance calibration using 1 thread
07:03:56 Initializing from normalized Laplacian + noise
07:03:56 Commencing optimization for 500 epochs, with 246508 positive edges
07:04:13 Optimization finished
[1] "187 0.04"07:04:13 UMAP embedding parameters a = 1.786 b = 0.8316
07:04:13 Read 1203 rows and found 38 numeric columns
07:04:13 Using Annoy for neighbor search, n_neighbors = 187
07:04:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:04:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8732ed7dda
07:04:14 Searching Annoy index using 1 thread, search_k = 18700
07:04:16 Annoy recall = 100%
07:04:28 Commencing smooth kNN distance calibration using 1 thread
07:04:54 Initializing from normalized Laplacian + noise
07:04:54 Commencing optimization for 500 epochs, with 246508 positive edges
07:05:11 Optimization finished
[1] "187 0.05"07:05:11 UMAP embedding parameters a = 1.75 b = 0.8421
07:05:11 Read 1203 rows and found 38 numeric columns
07:05:11 Using Annoy for neighbor search, n_neighbors = 187
07:05:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:05:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876db7ed62
07:05:12 Searching Annoy index using 1 thread, search_k = 18700
07:05:14 Annoy recall = 100%
07:05:26 Commencing smooth kNN distance calibration using 1 thread
07:05:52 Initializing from normalized Laplacian + noise
07:05:52 Commencing optimization for 500 epochs, with 246508 positive edges
07:06:09 Optimization finished
[1] "187 0.06"07:06:09 UMAP embedding parameters a = 1.715 b = 0.8526
07:06:09 Read 1203 rows and found 38 numeric columns
07:06:09 Using Annoy for neighbor search, n_neighbors = 187
07:06:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:06:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875563eca0
07:06:10 Searching Annoy index using 1 thread, search_k = 18700
07:06:12 Annoy recall = 100%
07:06:24 Commencing smooth kNN distance calibration using 1 thread
07:06:49 Initializing from normalized Laplacian + noise
07:06:50 Commencing optimization for 500 epochs, with 246508 positive edges
07:07:07 Optimization finished
[1] "187 0.07"07:07:07 UMAP embedding parameters a = 1.68 b = 0.8631
07:07:07 Read 1203 rows and found 38 numeric columns
07:07:07 Using Annoy for neighbor search, n_neighbors = 187
07:07:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:07:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720d3f93d
07:07:08 Searching Annoy index using 1 thread, search_k = 18700
07:07:09 Annoy recall = 100%
07:07:22 Commencing smooth kNN distance calibration using 1 thread
07:07:47 Initializing from normalized Laplacian + noise
07:07:47 Commencing optimization for 500 epochs, with 246508 positive edges
07:08:05 Optimization finished
[1] "187 0.08"07:08:05 UMAP embedding parameters a = 1.645 b = 0.8737
07:08:05 Read 1203 rows and found 38 numeric columns
07:08:05 Using Annoy for neighbor search, n_neighbors = 187
07:08:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:08:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761dee178
07:08:06 Searching Annoy index using 1 thread, search_k = 18700
07:08:07 Annoy recall = 100%
07:08:20 Commencing smooth kNN distance calibration using 1 thread
07:08:45 Initializing from normalized Laplacian + noise
07:08:45 Commencing optimization for 500 epochs, with 246508 positive edges
07:09:02 Optimization finished
[1] "187 0.09"07:09:03 UMAP embedding parameters a = 1.611 b = 0.8844
07:09:03 Read 1203 rows and found 38 numeric columns
07:09:03 Using Annoy for neighbor search, n_neighbors = 187
07:09:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:09:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8760acf67f
07:09:04 Searching Annoy index using 1 thread, search_k = 18700
07:09:05 Annoy recall = 100%
07:09:18 Commencing smooth kNN distance calibration using 1 thread
07:09:43 Initializing from normalized Laplacian + noise
07:09:43 Commencing optimization for 500 epochs, with 246508 positive edges
07:10:00 Optimization finished
[1] "187 0.1"07:10:01 UMAP embedding parameters a = 1.577 b = 0.8951
07:10:01 Read 1203 rows and found 38 numeric columns
07:10:01 Using Annoy for neighbor search, n_neighbors = 187
07:10:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:10:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a753e
07:10:02 Searching Annoy index using 1 thread, search_k = 18700
07:10:03 Annoy recall = 100%
07:10:16 Commencing smooth kNN distance calibration using 1 thread
07:10:41 Initializing from normalized Laplacian + noise
07:10:41 Commencing optimization for 500 epochs, with 246508 positive edges
07:10:58 Optimization finished
[1] "187 0.11"07:10:58 UMAP embedding parameters a = 1.544 b = 0.9058
07:10:58 Read 1203 rows and found 38 numeric columns
07:10:58 Using Annoy for neighbor search, n_neighbors = 187
07:10:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:10:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a1f7d76
07:10:59 Searching Annoy index using 1 thread, search_k = 18700
07:11:01 Annoy recall = 100%
07:11:13 Commencing smooth kNN distance calibration using 1 thread
07:11:39 Initializing from normalized Laplacian + noise
07:11:39 Commencing optimization for 500 epochs, with 246508 positive edges
07:11:56 Optimization finished
[1] "187 0.12"07:11:56 UMAP embedding parameters a = 1.51 b = 0.9165
07:11:56 Read 1203 rows and found 38 numeric columns
07:11:56 Using Annoy for neighbor search, n_neighbors = 187
07:11:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:11:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87563b4bbb
07:11:57 Searching Annoy index using 1 thread, search_k = 18700
07:11:59 Annoy recall = 100%
07:12:11 Commencing smooth kNN distance calibration using 1 thread
07:12:37 Initializing from normalized Laplacian + noise
07:12:37 Commencing optimization for 500 epochs, with 246508 positive edges
07:12:54 Optimization finished
[1] "187 0.13"07:12:54 UMAP embedding parameters a = 1.478 b = 0.9272
07:12:54 Read 1203 rows and found 38 numeric columns
07:12:54 Using Annoy for neighbor search, n_neighbors = 187
07:12:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:12:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f878c2c21e
07:12:55 Searching Annoy index using 1 thread, search_k = 18700
07:12:57 Annoy recall = 100%
07:13:09 Commencing smooth kNN distance calibration using 1 thread
07:13:35 Initializing from normalized Laplacian + noise
07:13:35 Commencing optimization for 500 epochs, with 246508 positive edges
07:13:52 Optimization finished
[1] "187 0.14"07:13:52 UMAP embedding parameters a = 1.446 b = 0.938
07:13:52 Read 1203 rows and found 38 numeric columns
07:13:52 Using Annoy for neighbor search, n_neighbors = 187
07:13:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:13:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a8f8990
07:13:53 Searching Annoy index using 1 thread, search_k = 18700
07:13:55 Annoy recall = 100%
07:14:07 Commencing smooth kNN distance calibration using 1 thread
07:14:33 Initializing from normalized Laplacian + noise
07:14:33 Commencing optimization for 500 epochs, with 246508 positive edges
07:14:50 Optimization finished
[1] "187 0.15"07:14:50 UMAP embedding parameters a = 1.414 b = 0.9488
07:14:50 Read 1203 rows and found 38 numeric columns
07:14:50 Using Annoy for neighbor search, n_neighbors = 187
07:14:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:14:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e7a48c3
07:14:51 Searching Annoy index using 1 thread, search_k = 18700
07:14:53 Annoy recall = 100%
07:15:05 Commencing smooth kNN distance calibration using 1 thread
07:15:31 Initializing from normalized Laplacian + noise
07:15:31 Commencing optimization for 500 epochs, with 246508 positive edges
07:15:48 Optimization finished
[1] "187 0.16"07:15:48 UMAP embedding parameters a = 1.383 b = 0.9596
07:15:48 Read 1203 rows and found 38 numeric columns
07:15:48 Using Annoy for neighbor search, n_neighbors = 187
07:15:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:15:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b0c8f08
07:15:49 Searching Annoy index using 1 thread, search_k = 18700
07:15:51 Annoy recall = 100%
07:16:03 Commencing smooth kNN distance calibration using 1 thread
07:16:29 Initializing from normalized Laplacian + noise
07:16:29 Commencing optimization for 500 epochs, with 246508 positive edges
07:16:46 Optimization finished
[1] "187 0.17"07:16:46 UMAP embedding parameters a = 1.352 b = 0.9704
07:16:46 Read 1203 rows and found 38 numeric columns
07:16:46 Using Annoy for neighbor search, n_neighbors = 187
07:16:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:16:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a080f79
07:16:47 Searching Annoy index using 1 thread, search_k = 18700
07:16:49 Annoy recall = 100%
07:17:01 Commencing smooth kNN distance calibration using 1 thread
07:17:27 Initializing from normalized Laplacian + noise
07:17:27 Commencing optimization for 500 epochs, with 246508 positive edges
07:17:44 Optimization finished
[1] "187 0.18"07:17:44 UMAP embedding parameters a = 1.321 b = 0.9813
07:17:44 Read 1203 rows and found 38 numeric columns
07:17:44 Using Annoy for neighbor search, n_neighbors = 187
07:17:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:17:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875182bc02
07:17:45 Searching Annoy index using 1 thread, search_k = 18700
07:17:47 Annoy recall = 100%
07:17:59 Commencing smooth kNN distance calibration using 1 thread
07:18:25 Initializing from normalized Laplacian + noise
07:18:25 Commencing optimization for 500 epochs, with 246508 positive edges
07:18:42 Optimization finished
[1] "187 0.19"07:18:42 UMAP embedding parameters a = 1.292 b = 0.9921
07:18:42 Read 1203 rows and found 38 numeric columns
07:18:42 Using Annoy for neighbor search, n_neighbors = 187
07:18:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:18:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8792acf2
07:18:43 Searching Annoy index using 1 thread, search_k = 18700
07:18:45 Annoy recall = 100%
07:18:57 Commencing smooth kNN distance calibration using 1 thread
07:19:23 Initializing from normalized Laplacian + noise
07:19:23 Commencing optimization for 500 epochs, with 246508 positive edges
07:19:40 Optimization finished
[1] "187 0.2"07:19:40 UMAP embedding parameters a = 1.262 b = 1.003
07:19:40 Read 1203 rows and found 38 numeric columns
07:19:40 Using Annoy for neighbor search, n_neighbors = 187
07:19:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:19:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871d63e544
07:19:41 Searching Annoy index using 1 thread, search_k = 18700
07:19:43 Annoy recall = 100%
07:19:55 Commencing smooth kNN distance calibration using 1 thread
07:20:21 Initializing from normalized Laplacian + noise
07:20:21 Commencing optimization for 500 epochs, with 246508 positive edges
07:20:38 Optimization finished
[1] "188 0"07:20:38 UMAP embedding parameters a = 1.933 b = 0.7905
07:20:38 Read 1203 rows and found 38 numeric columns
07:20:38 Using Annoy for neighbor search, n_neighbors = 188
07:20:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:20:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876edad732
07:20:39 Searching Annoy index using 1 thread, search_k = 18800
07:20:41 Annoy recall = 100%
07:20:53 Commencing smooth kNN distance calibration using 1 thread
07:21:19 Initializing from normalized Laplacian + noise
07:21:19 Commencing optimization for 500 epochs, with 247672 positive edges
07:21:36 Optimization finished
[1] "188 0.01"07:21:36 UMAP embedding parameters a = 1.896 b = 0.8006
07:21:36 Read 1203 rows and found 38 numeric columns
07:21:36 Using Annoy for neighbor search, n_neighbors = 188
07:21:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:21:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714a2a1dc
07:21:38 Searching Annoy index using 1 thread, search_k = 18800
07:21:39 Annoy recall = 100%
07:21:52 Commencing smooth kNN distance calibration using 1 thread
07:22:17 Initializing from normalized Laplacian + noise
07:22:17 Commencing optimization for 500 epochs, with 247672 positive edges
07:22:34 Optimization finished
[1] "188 0.02"07:22:35 UMAP embedding parameters a = 1.859 b = 0.8109
07:22:35 Read 1203 rows and found 38 numeric columns
07:22:35 Using Annoy for neighbor search, n_neighbors = 188
07:22:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:22:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873379d91d
07:22:36 Searching Annoy index using 1 thread, search_k = 18800
07:22:37 Annoy recall = 100%
07:22:50 Commencing smooth kNN distance calibration using 1 thread
07:23:15 Initializing from normalized Laplacian + noise
07:23:15 Commencing optimization for 500 epochs, with 247672 positive edges
07:23:32 Optimization finished
[1] "188 0.03"07:23:33 UMAP embedding parameters a = 1.822 b = 0.8212
07:23:33 Read 1203 rows and found 38 numeric columns
07:23:33 Using Annoy for neighbor search, n_neighbors = 188
07:23:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:23:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f28201d
07:23:34 Searching Annoy index using 1 thread, search_k = 18800
07:23:35 Annoy recall = 100%
07:23:48 Commencing smooth kNN distance calibration using 1 thread
07:24:13 Initializing from normalized Laplacian + noise
07:24:13 Commencing optimization for 500 epochs, with 247672 positive edges
07:24:30 Optimization finished
[1] "188 0.04"07:24:31 UMAP embedding parameters a = 1.786 b = 0.8316
07:24:31 Read 1203 rows and found 38 numeric columns
07:24:31 Using Annoy for neighbor search, n_neighbors = 188
07:24:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:24:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877073a6d5
07:24:32 Searching Annoy index using 1 thread, search_k = 18800
07:24:33 Annoy recall = 100%
07:24:46 Commencing smooth kNN distance calibration using 1 thread
07:25:11 Initializing from normalized Laplacian + noise
07:25:12 Commencing optimization for 500 epochs, with 247672 positive edges
07:25:29 Optimization finished
[1] "188 0.05"07:25:29 UMAP embedding parameters a = 1.75 b = 0.8421
07:25:29 Read 1203 rows and found 38 numeric columns
07:25:29 Using Annoy for neighbor search, n_neighbors = 188
07:25:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:25:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874661fd54
07:25:30 Searching Annoy index using 1 thread, search_k = 18800
07:25:31 Annoy recall = 100%
07:25:44 Commencing smooth kNN distance calibration using 1 thread
07:26:10 Initializing from normalized Laplacian + noise
07:26:10 Commencing optimization for 500 epochs, with 247672 positive edges
07:26:27 Optimization finished
[1] "188 0.06"07:26:27 UMAP embedding parameters a = 1.715 b = 0.8526
07:26:27 Read 1203 rows and found 38 numeric columns
07:26:27 Using Annoy for neighbor search, n_neighbors = 188
07:26:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:26:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d7b1380
07:26:28 Searching Annoy index using 1 thread, search_k = 18800
07:26:30 Annoy recall = 100%
07:26:42 Commencing smooth kNN distance calibration using 1 thread
07:27:08 Initializing from normalized Laplacian + noise
07:27:08 Commencing optimization for 500 epochs, with 247672 positive edges
07:27:25 Optimization finished
[1] "188 0.07"07:27:25 UMAP embedding parameters a = 1.68 b = 0.8631
07:27:25 Read 1203 rows and found 38 numeric columns
07:27:25 Using Annoy for neighbor search, n_neighbors = 188
07:27:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:27:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b825432
07:27:26 Searching Annoy index using 1 thread, search_k = 18800
07:27:28 Annoy recall = 100%
07:27:40 Commencing smooth kNN distance calibration using 1 thread
07:28:06 Initializing from normalized Laplacian + noise
07:28:06 Commencing optimization for 500 epochs, with 247672 positive edges
07:28:23 Optimization finished
[1] "188 0.08"07:28:23 UMAP embedding parameters a = 1.645 b = 0.8737
07:28:24 Read 1203 rows and found 38 numeric columns
07:28:24 Using Annoy for neighbor search, n_neighbors = 188
07:28:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:28:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872ac97cb9
07:28:25 Searching Annoy index using 1 thread, search_k = 18800
07:28:26 Annoy recall = 100%
07:28:38 Commencing smooth kNN distance calibration using 1 thread
07:29:04 Initializing from normalized Laplacian + noise
07:29:04 Commencing optimization for 500 epochs, with 247672 positive edges
07:29:21 Optimization finished
[1] "188 0.09"07:29:22 UMAP embedding parameters a = 1.611 b = 0.8844
07:29:22 Read 1203 rows and found 38 numeric columns
07:29:22 Using Annoy for neighbor search, n_neighbors = 188
07:29:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:29:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872628db2b
07:29:23 Searching Annoy index using 1 thread, search_k = 18800
07:29:24 Annoy recall = 100%
07:29:37 Commencing smooth kNN distance calibration using 1 thread
07:30:02 Initializing from normalized Laplacian + noise
07:30:02 Commencing optimization for 500 epochs, with 247672 positive edges
07:30:20 Optimization finished
[1] "188 0.1"07:30:20 UMAP embedding parameters a = 1.577 b = 0.8951
07:30:20 Read 1203 rows and found 38 numeric columns
07:30:20 Using Annoy for neighbor search, n_neighbors = 188
07:30:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:30:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b86bf02
07:30:21 Searching Annoy index using 1 thread, search_k = 18800
07:30:22 Annoy recall = 100%
07:30:35 Commencing smooth kNN distance calibration using 1 thread
07:31:00 Initializing from normalized Laplacian + noise
07:31:00 Commencing optimization for 500 epochs, with 247672 positive edges
07:31:18 Optimization finished
[1] "188 0.11"07:31:18 UMAP embedding parameters a = 1.544 b = 0.9058
07:31:18 Read 1203 rows and found 38 numeric columns
07:31:18 Using Annoy for neighbor search, n_neighbors = 188
07:31:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:31:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8739d8fd17
07:31:19 Searching Annoy index using 1 thread, search_k = 18800
07:31:20 Annoy recall = 100%
07:31:33 Commencing smooth kNN distance calibration using 1 thread
07:31:59 Initializing from normalized Laplacian + noise
07:31:59 Commencing optimization for 500 epochs, with 247672 positive edges
07:32:16 Optimization finished
[1] "188 0.12"07:32:16 UMAP embedding parameters a = 1.51 b = 0.9165
07:32:16 Read 1203 rows and found 38 numeric columns
07:32:16 Using Annoy for neighbor search, n_neighbors = 188
07:32:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:32:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87a55c8ca
07:32:17 Searching Annoy index using 1 thread, search_k = 18800
07:32:19 Annoy recall = 100%
07:32:31 Commencing smooth kNN distance calibration using 1 thread
07:32:57 Initializing from normalized Laplacian + noise
07:32:57 Commencing optimization for 500 epochs, with 247672 positive edges
07:33:14 Optimization finished
[1] "188 0.13"07:33:14 UMAP embedding parameters a = 1.478 b = 0.9272
07:33:14 Read 1203 rows and found 38 numeric columns
07:33:14 Using Annoy for neighbor search, n_neighbors = 188
07:33:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:33:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87227f0d4
07:33:15 Searching Annoy index using 1 thread, search_k = 18800
07:33:17 Annoy recall = 100%
07:33:30 Commencing smooth kNN distance calibration using 1 thread
07:33:55 Initializing from normalized Laplacian + noise
07:33:55 Commencing optimization for 500 epochs, with 247672 positive edges
07:34:12 Optimization finished
[1] "188 0.14"07:34:13 UMAP embedding parameters a = 1.446 b = 0.938
07:34:13 Read 1203 rows and found 38 numeric columns
07:34:13 Using Annoy for neighbor search, n_neighbors = 188
07:34:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:34:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876cc67af1
07:34:14 Searching Annoy index using 1 thread, search_k = 18800
07:34:15 Annoy recall = 100%
07:34:28 Commencing smooth kNN distance calibration using 1 thread
07:34:54 Initializing from normalized Laplacian + noise
07:34:54 Commencing optimization for 500 epochs, with 247672 positive edges
07:35:11 Optimization finished
[1] "188 0.15"07:35:11 UMAP embedding parameters a = 1.414 b = 0.9488
07:35:11 Read 1203 rows and found 38 numeric columns
07:35:11 Using Annoy for neighbor search, n_neighbors = 188
07:35:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:35:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87780db62d
07:35:12 Searching Annoy index using 1 thread, search_k = 18800
07:35:13 Annoy recall = 100%
07:35:26 Commencing smooth kNN distance calibration using 1 thread
07:35:52 Initializing from normalized Laplacian + noise
07:35:52 Commencing optimization for 500 epochs, with 247672 positive edges
07:36:09 Optimization finished
[1] "188 0.16"07:36:09 UMAP embedding parameters a = 1.383 b = 0.9596
07:36:09 Read 1203 rows and found 38 numeric columns
07:36:09 Using Annoy for neighbor search, n_neighbors = 188
07:36:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:36:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87578bdd74
07:36:10 Searching Annoy index using 1 thread, search_k = 18800
07:36:12 Annoy recall = 100%
07:36:24 Commencing smooth kNN distance calibration using 1 thread
07:36:50 Initializing from normalized Laplacian + noise
07:36:50 Commencing optimization for 500 epochs, with 247672 positive edges
07:37:07 Optimization finished
[1] "188 0.17"07:37:08 UMAP embedding parameters a = 1.352 b = 0.9704
07:37:08 Read 1203 rows and found 38 numeric columns
07:37:08 Using Annoy for neighbor search, n_neighbors = 188
07:37:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:37:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d9a742e
07:37:09 Searching Annoy index using 1 thread, search_k = 18800
07:37:10 Annoy recall = 100%
07:37:23 Commencing smooth kNN distance calibration using 1 thread
07:37:48 Initializing from normalized Laplacian + noise
07:37:49 Commencing optimization for 500 epochs, with 247672 positive edges
07:38:06 Optimization finished
[1] "188 0.18"07:38:06 UMAP embedding parameters a = 1.321 b = 0.9813
07:38:06 Read 1203 rows and found 38 numeric columns
07:38:06 Using Annoy for neighbor search, n_neighbors = 188
07:38:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:38:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759ec97a5
07:38:07 Searching Annoy index using 1 thread, search_k = 18800
07:38:08 Annoy recall = 100%
07:38:21 Commencing smooth kNN distance calibration using 1 thread
07:38:47 Initializing from normalized Laplacian + noise
07:38:47 Commencing optimization for 500 epochs, with 247672 positive edges
07:39:04 Optimization finished
[1] "188 0.19"07:39:04 UMAP embedding parameters a = 1.292 b = 0.9921
07:39:04 Read 1203 rows and found 38 numeric columns
07:39:04 Using Annoy for neighbor search, n_neighbors = 188
07:39:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:39:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873838d3f3
07:39:05 Searching Annoy index using 1 thread, search_k = 18800
07:39:07 Annoy recall = 100%
07:39:19 Commencing smooth kNN distance calibration using 1 thread
07:39:45 Initializing from normalized Laplacian + noise
07:39:45 Commencing optimization for 500 epochs, with 247672 positive edges
07:40:02 Optimization finished
[1] "188 0.2"07:40:03 UMAP embedding parameters a = 1.262 b = 1.003
07:40:03 Read 1203 rows and found 38 numeric columns
07:40:03 Using Annoy for neighbor search, n_neighbors = 188
07:40:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:40:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dd4e96c
07:40:04 Searching Annoy index using 1 thread, search_k = 18800
07:40:05 Annoy recall = 100%
07:40:18 Commencing smooth kNN distance calibration using 1 thread
07:40:43 Initializing from normalized Laplacian + noise
07:40:43 Commencing optimization for 500 epochs, with 247672 positive edges
07:41:01 Optimization finished
[1] "189 0"07:41:01 UMAP embedding parameters a = 1.933 b = 0.7905
07:41:01 Read 1203 rows and found 38 numeric columns
07:41:01 Using Annoy for neighbor search, n_neighbors = 189
07:41:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:41:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87640c151b
07:41:02 Searching Annoy index using 1 thread, search_k = 18900
07:41:03 Annoy recall = 100%
07:41:16 Commencing smooth kNN distance calibration using 1 thread
07:41:42 Initializing from normalized Laplacian + noise
07:41:42 Commencing optimization for 500 epochs, with 248848 positive edges
07:41:59 Optimization finished
[1] "189 0.01"07:41:59 UMAP embedding parameters a = 1.896 b = 0.8006
07:41:59 Read 1203 rows and found 38 numeric columns
07:41:59 Using Annoy for neighbor search, n_neighbors = 189
07:41:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:42:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e741faf
07:42:00 Searching Annoy index using 1 thread, search_k = 18900
07:42:02 Annoy recall = 100%
07:42:14 Commencing smooth kNN distance calibration using 1 thread
07:42:40 Initializing from normalized Laplacian + noise
07:42:40 Commencing optimization for 500 epochs, with 248848 positive edges
07:42:57 Optimization finished
[1] "189 0.02"07:42:58 UMAP embedding parameters a = 1.859 b = 0.8109
07:42:58 Read 1203 rows and found 38 numeric columns
07:42:58 Using Annoy for neighbor search, n_neighbors = 189
07:42:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:42:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871697ab8b
07:42:59 Searching Annoy index using 1 thread, search_k = 18900
07:43:00 Annoy recall = 100%
07:43:13 Commencing smooth kNN distance calibration using 1 thread
07:43:38 Initializing from normalized Laplacian + noise
07:43:38 Commencing optimization for 500 epochs, with 248848 positive edges
07:43:56 Optimization finished
[1] "189 0.03"07:43:56 UMAP embedding parameters a = 1.822 b = 0.8212
07:43:56 Read 1203 rows and found 38 numeric columns
07:43:56 Using Annoy for neighbor search, n_neighbors = 189
07:43:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:43:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e9b9eab
07:43:57 Searching Annoy index using 1 thread, search_k = 18900
07:43:58 Annoy recall = 100%
07:44:11 Commencing smooth kNN distance calibration using 1 thread
07:44:37 Initializing from normalized Laplacian + noise
07:44:37 Commencing optimization for 500 epochs, with 248848 positive edges
07:44:54 Optimization finished
[1] "189 0.04"07:44:54 UMAP embedding parameters a = 1.786 b = 0.8316
07:44:54 Read 1203 rows and found 38 numeric columns
07:44:54 Using Annoy for neighbor search, n_neighbors = 189
07:44:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:44:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cee6872
07:44:55 Searching Annoy index using 1 thread, search_k = 18900
07:44:57 Annoy recall = 100%
07:45:09 Commencing smooth kNN distance calibration using 1 thread
07:45:35 Initializing from normalized Laplacian + noise
07:45:35 Commencing optimization for 500 epochs, with 248848 positive edges
07:45:52 Optimization finished
[1] "189 0.05"07:45:53 UMAP embedding parameters a = 1.75 b = 0.8421
07:45:53 Read 1203 rows and found 38 numeric columns
07:45:53 Using Annoy for neighbor search, n_neighbors = 189
07:45:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:45:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8761a43a93
07:45:54 Searching Annoy index using 1 thread, search_k = 18900
07:45:55 Annoy recall = 100%
07:46:08 Commencing smooth kNN distance calibration using 1 thread
07:46:34 Initializing from normalized Laplacian + noise
07:46:34 Commencing optimization for 500 epochs, with 248848 positive edges
07:46:51 Optimization finished
[1] "189 0.06"07:46:51 UMAP embedding parameters a = 1.715 b = 0.8526
07:46:51 Read 1203 rows and found 38 numeric columns
07:46:51 Using Annoy for neighbor search, n_neighbors = 189
07:46:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:46:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718a3ae24
07:46:52 Searching Annoy index using 1 thread, search_k = 18900
07:46:53 Annoy recall = 100%
07:47:06 Commencing smooth kNN distance calibration using 1 thread
07:47:32 Initializing from normalized Laplacian + noise
07:47:32 Commencing optimization for 500 epochs, with 248848 positive edges
07:47:49 Optimization finished
[1] "189 0.07"07:47:49 UMAP embedding parameters a = 1.68 b = 0.8631
07:47:49 Read 1203 rows and found 38 numeric columns
07:47:49 Using Annoy for neighbor search, n_neighbors = 189
07:47:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:47:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e712474
07:47:50 Searching Annoy index using 1 thread, search_k = 18900
07:47:52 Annoy recall = 100%
07:48:04 Commencing smooth kNN distance calibration using 1 thread
07:48:30 Initializing from normalized Laplacian + noise
07:48:30 Commencing optimization for 500 epochs, with 248848 positive edges
07:48:48 Optimization finished
[1] "189 0.08"07:48:48 UMAP embedding parameters a = 1.645 b = 0.8737
07:48:48 Read 1203 rows and found 38 numeric columns
07:48:48 Using Annoy for neighbor search, n_neighbors = 189
07:48:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:48:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876236e785
07:48:49 Searching Annoy index using 1 thread, search_k = 18900
07:48:50 Annoy recall = 100%
07:49:03 Commencing smooth kNN distance calibration using 1 thread
07:49:29 Initializing from normalized Laplacian + noise
07:49:29 Commencing optimization for 500 epochs, with 248848 positive edges
07:49:46 Optimization finished
[1] "189 0.09"07:49:46 UMAP embedding parameters a = 1.611 b = 0.8844
07:49:46 Read 1203 rows and found 38 numeric columns
07:49:46 Using Annoy for neighbor search, n_neighbors = 189
07:49:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:49:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8736079369
07:49:47 Searching Annoy index using 1 thread, search_k = 18900
07:49:49 Annoy recall = 100%
07:50:01 Commencing smooth kNN distance calibration using 1 thread
07:50:27 Initializing from normalized Laplacian + noise
07:50:27 Commencing optimization for 500 epochs, with 248848 positive edges
07:50:45 Optimization finished
[1] "189 0.1"07:50:45 UMAP embedding parameters a = 1.577 b = 0.8951
07:50:45 Read 1203 rows and found 38 numeric columns
07:50:45 Using Annoy for neighbor search, n_neighbors = 189
07:50:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:50:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877d4bfba6
07:50:46 Searching Annoy index using 1 thread, search_k = 18900
07:50:47 Annoy recall = 100%
07:51:00 Commencing smooth kNN distance calibration using 1 thread
07:51:26 Initializing from normalized Laplacian + noise
07:51:26 Commencing optimization for 500 epochs, with 248848 positive edges
07:51:43 Optimization finished
[1] "189 0.11"07:51:43 UMAP embedding parameters a = 1.544 b = 0.9058
07:51:43 Read 1203 rows and found 38 numeric columns
07:51:43 Using Annoy for neighbor search, n_neighbors = 189
07:51:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:51:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776d98962
07:51:44 Searching Annoy index using 1 thread, search_k = 18900
07:51:46 Annoy recall = 100%
07:51:58 Commencing smooth kNN distance calibration using 1 thread
07:52:24 Initializing from normalized Laplacian + noise
07:52:24 Commencing optimization for 500 epochs, with 248848 positive edges
07:52:41 Optimization finished
[1] "189 0.12"07:52:42 UMAP embedding parameters a = 1.51 b = 0.9165
07:52:42 Read 1203 rows and found 38 numeric columns
07:52:42 Using Annoy for neighbor search, n_neighbors = 189
07:52:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:52:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769816c86
07:52:43 Searching Annoy index using 1 thread, search_k = 18900
07:52:44 Annoy recall = 100%
07:52:57 Commencing smooth kNN distance calibration using 1 thread
07:53:22 Initializing from normalized Laplacian + noise
07:53:23 Commencing optimization for 500 epochs, with 248848 positive edges
07:53:40 Optimization finished
[1] "189 0.13"07:53:40 UMAP embedding parameters a = 1.478 b = 0.9272
07:53:40 Read 1203 rows and found 38 numeric columns
07:53:40 Using Annoy for neighbor search, n_neighbors = 189
07:53:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:53:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877c741bc3
07:53:41 Searching Annoy index using 1 thread, search_k = 18900
07:53:43 Annoy recall = 100%
07:53:55 Commencing smooth kNN distance calibration using 1 thread
07:54:21 Initializing from normalized Laplacian + noise
07:54:21 Commencing optimization for 500 epochs, with 248848 positive edges
07:54:38 Optimization finished
[1] "189 0.14"07:54:39 UMAP embedding parameters a = 1.446 b = 0.938
07:54:39 Read 1203 rows and found 38 numeric columns
07:54:39 Using Annoy for neighbor search, n_neighbors = 189
07:54:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:54:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87674d3037
07:54:40 Searching Annoy index using 1 thread, search_k = 18900
07:54:41 Annoy recall = 100%
07:54:54 Commencing smooth kNN distance calibration using 1 thread
07:55:19 Initializing from normalized Laplacian + noise
07:55:20 Commencing optimization for 500 epochs, with 248848 positive edges
07:55:37 Optimization finished
[1] "189 0.15"07:55:37 UMAP embedding parameters a = 1.414 b = 0.9488
07:55:37 Read 1203 rows and found 38 numeric columns
07:55:37 Using Annoy for neighbor search, n_neighbors = 189
07:55:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:55:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872fe369da
07:55:38 Searching Annoy index using 1 thread, search_k = 18900
07:55:40 Annoy recall = 100%
07:55:52 Commencing smooth kNN distance calibration using 1 thread
07:56:18 Initializing from normalized Laplacian + noise
07:56:18 Commencing optimization for 500 epochs, with 248848 positive edges
07:56:35 Optimization finished
[1] "189 0.16"07:56:36 UMAP embedding parameters a = 1.383 b = 0.9596
07:56:36 Read 1203 rows and found 38 numeric columns
07:56:36 Using Annoy for neighbor search, n_neighbors = 189
07:56:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:56:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879ef2f43
07:56:37 Searching Annoy index using 1 thread, search_k = 18900
07:56:38 Annoy recall = 100%
07:56:51 Commencing smooth kNN distance calibration using 1 thread
07:57:17 Initializing from normalized Laplacian + noise
07:57:17 Commencing optimization for 500 epochs, with 248848 positive edges
07:57:34 Optimization finished
[1] "189 0.17"07:57:34 UMAP embedding parameters a = 1.352 b = 0.9704
07:57:34 Read 1203 rows and found 38 numeric columns
07:57:34 Using Annoy for neighbor search, n_neighbors = 189
07:57:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:57:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712cf8469
07:57:35 Searching Annoy index using 1 thread, search_k = 18900
07:57:37 Annoy recall = 100%
07:57:49 Commencing smooth kNN distance calibration using 1 thread
07:58:15 Initializing from normalized Laplacian + noise
07:58:15 Commencing optimization for 500 epochs, with 248848 positive edges
07:58:32 Optimization finished
[1] "189 0.18"07:58:33 UMAP embedding parameters a = 1.321 b = 0.9813
07:58:33 Read 1203 rows and found 38 numeric columns
07:58:33 Using Annoy for neighbor search, n_neighbors = 189
07:58:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:58:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875aace693
07:58:34 Searching Annoy index using 1 thread, search_k = 18900
07:58:35 Annoy recall = 100%
07:58:48 Commencing smooth kNN distance calibration using 1 thread
07:59:14 Initializing from normalized Laplacian + noise
07:59:14 Commencing optimization for 500 epochs, with 248848 positive edges
07:59:31 Optimization finished
[1] "189 0.19"07:59:31 UMAP embedding parameters a = 1.292 b = 0.9921
07:59:31 Read 1203 rows and found 38 numeric columns
07:59:31 Using Annoy for neighbor search, n_neighbors = 189
07:59:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:59:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8730180a6e
07:59:32 Searching Annoy index using 1 thread, search_k = 18900
07:59:34 Annoy recall = 100%
07:59:46 Commencing smooth kNN distance calibration using 1 thread
08:00:12 Initializing from normalized Laplacian + noise
08:00:12 Commencing optimization for 500 epochs, with 248848 positive edges
08:00:30 Optimization finished
[1] "189 0.2"08:00:30 UMAP embedding parameters a = 1.262 b = 1.003
08:00:30 Read 1203 rows and found 38 numeric columns
08:00:30 Using Annoy for neighbor search, n_neighbors = 189
08:00:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:00:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875e56436c
08:00:31 Searching Annoy index using 1 thread, search_k = 18900
08:00:32 Annoy recall = 100%
08:00:45 Commencing smooth kNN distance calibration using 1 thread
08:01:11 Initializing from normalized Laplacian + noise
08:01:11 Commencing optimization for 500 epochs, with 248848 positive edges
08:01:28 Optimization finished
[1] "190 0"08:01:29 UMAP embedding parameters a = 1.933 b = 0.7905
08:01:29 Read 1203 rows and found 38 numeric columns
08:01:29 Using Annoy for neighbor search, n_neighbors = 190
08:01:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:01:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871485e3aa
08:01:30 Searching Annoy index using 1 thread, search_k = 19000
08:01:31 Annoy recall = 100%
08:01:44 Commencing smooth kNN distance calibration using 1 thread
08:02:09 Initializing from normalized Laplacian + noise
08:02:10 Commencing optimization for 500 epochs, with 250038 positive edges
08:02:27 Optimization finished
[1] "190 0.01"08:02:27 UMAP embedding parameters a = 1.896 b = 0.8006
08:02:27 Read 1203 rows and found 38 numeric columns
08:02:27 Using Annoy for neighbor search, n_neighbors = 190
08:02:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:02:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a6dd339
08:02:28 Searching Annoy index using 1 thread, search_k = 19000
08:02:30 Annoy recall = 100%
08:02:42 Commencing smooth kNN distance calibration using 1 thread
08:03:08 Initializing from normalized Laplacian + noise
08:03:08 Commencing optimization for 500 epochs, with 250038 positive edges
08:03:26 Optimization finished
[1] "190 0.02"08:03:26 UMAP embedding parameters a = 1.859 b = 0.8109
08:03:26 Read 1203 rows and found 38 numeric columns
08:03:26 Using Annoy for neighbor search, n_neighbors = 190
08:03:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:03:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87607e3440
08:03:27 Searching Annoy index using 1 thread, search_k = 19000
08:03:28 Annoy recall = 100%
08:03:41 Commencing smooth kNN distance calibration using 1 thread
08:04:07 Initializing from normalized Laplacian + noise
08:04:07 Commencing optimization for 500 epochs, with 250038 positive edges
08:04:24 Optimization finished
[1] "190 0.03"08:04:24 UMAP embedding parameters a = 1.822 b = 0.8212
08:04:25 Read 1203 rows and found 38 numeric columns
08:04:25 Using Annoy for neighbor search, n_neighbors = 190
08:04:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:04:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714c5e9c
08:04:26 Searching Annoy index using 1 thread, search_k = 19000
08:04:27 Annoy recall = 100%
08:04:40 Commencing smooth kNN distance calibration using 1 thread
08:05:05 Initializing from normalized Laplacian + noise
08:05:05 Commencing optimization for 500 epochs, with 250038 positive edges
08:05:23 Optimization finished
[1] "190 0.04"08:05:23 UMAP embedding parameters a = 1.786 b = 0.8316
08:05:23 Read 1203 rows and found 38 numeric columns
08:05:23 Using Annoy for neighbor search, n_neighbors = 190
08:05:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:05:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87327b8966
08:05:24 Searching Annoy index using 1 thread, search_k = 19000
08:05:25 Annoy recall = 100%
08:05:38 Commencing smooth kNN distance calibration using 1 thread
08:06:04 Initializing from normalized Laplacian + noise
08:06:04 Commencing optimization for 500 epochs, with 250038 positive edges
08:06:21 Optimization finished
[1] "190 0.05"08:06:22 UMAP embedding parameters a = 1.75 b = 0.8421
08:06:22 Read 1203 rows and found 38 numeric columns
08:06:22 Using Annoy for neighbor search, n_neighbors = 190
08:06:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:06:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87380a11b5
08:06:23 Searching Annoy index using 1 thread, search_k = 19000
08:06:24 Annoy recall = 100%
08:06:37 Commencing smooth kNN distance calibration using 1 thread
08:07:03 Initializing from normalized Laplacian + noise
08:07:03 Commencing optimization for 500 epochs, with 250038 positive edges
08:07:20 Optimization finished
[1] "190 0.06"08:07:20 UMAP embedding parameters a = 1.715 b = 0.8526
08:07:20 Read 1203 rows and found 38 numeric columns
08:07:20 Using Annoy for neighbor search, n_neighbors = 190
08:07:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:07:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87ee6d2ca
08:07:21 Searching Annoy index using 1 thread, search_k = 19000
08:07:23 Annoy recall = 100%
08:07:36 Commencing smooth kNN distance calibration using 1 thread
08:08:02 Initializing from normalized Laplacian + noise
08:08:02 Commencing optimization for 500 epochs, with 250038 positive edges
08:08:19 Optimization finished
[1] "190 0.07"08:08:19 UMAP embedding parameters a = 1.68 b = 0.8631
08:08:19 Read 1203 rows and found 38 numeric columns
08:08:19 Using Annoy for neighbor search, n_neighbors = 190
08:08:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:08:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87c68210b
08:08:20 Searching Annoy index using 1 thread, search_k = 19000
08:08:21 Annoy recall = 100%
08:08:34 Commencing smooth kNN distance calibration using 1 thread
08:09:00 Initializing from normalized Laplacian + noise
08:09:00 Commencing optimization for 500 epochs, with 250038 positive edges
08:09:18 Optimization finished
[1] "190 0.08"08:09:18 UMAP embedding parameters a = 1.645 b = 0.8737
08:09:18 Read 1203 rows and found 38 numeric columns
08:09:18 Using Annoy for neighbor search, n_neighbors = 190
08:09:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:09:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877042e5a8
08:09:19 Searching Annoy index using 1 thread, search_k = 19000
08:09:20 Annoy recall = 100%
08:09:33 Commencing smooth kNN distance calibration using 1 thread
08:09:59 Initializing from normalized Laplacian + noise
08:09:59 Commencing optimization for 500 epochs, with 250038 positive edges
08:10:16 Optimization finished
[1] "190 0.09"08:10:17 UMAP embedding parameters a = 1.611 b = 0.8844
08:10:17 Read 1203 rows and found 38 numeric columns
08:10:17 Using Annoy for neighbor search, n_neighbors = 190
08:10:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:10:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871cbbbc37
08:10:18 Searching Annoy index using 1 thread, search_k = 19000
08:10:19 Annoy recall = 100%
08:10:32 Commencing smooth kNN distance calibration using 1 thread
08:10:58 Initializing from normalized Laplacian + noise
08:10:58 Commencing optimization for 500 epochs, with 250038 positive edges
08:11:15 Optimization finished
[1] "190 0.1"08:11:15 UMAP embedding parameters a = 1.577 b = 0.8951
08:11:15 Read 1203 rows and found 38 numeric columns
08:11:15 Using Annoy for neighbor search, n_neighbors = 190
08:11:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:11:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8770743626
08:11:16 Searching Annoy index using 1 thread, search_k = 19000
08:11:18 Annoy recall = 100%
08:11:31 Commencing smooth kNN distance calibration using 1 thread
08:11:56 Initializing from normalized Laplacian + noise
08:11:57 Commencing optimization for 500 epochs, with 250038 positive edges
08:12:14 Optimization finished
[1] "190 0.11"08:12:14 UMAP embedding parameters a = 1.544 b = 0.9058
08:12:14 Read 1203 rows and found 38 numeric columns
08:12:14 Using Annoy for neighbor search, n_neighbors = 190
08:12:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:12:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877eb70557
08:12:15 Searching Annoy index using 1 thread, search_k = 19000
08:12:17 Annoy recall = 100%
08:12:29 Commencing smooth kNN distance calibration using 1 thread
08:12:55 Initializing from normalized Laplacian + noise
08:12:55 Commencing optimization for 500 epochs, with 250038 positive edges
08:13:13 Optimization finished
[1] "190 0.12"08:13:13 UMAP embedding parameters a = 1.51 b = 0.9165
08:13:13 Read 1203 rows and found 38 numeric columns
08:13:13 Using Annoy for neighbor search, n_neighbors = 190
08:13:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:13:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87335367c2
08:13:14 Searching Annoy index using 1 thread, search_k = 19000
08:13:15 Annoy recall = 100%
08:13:28 Commencing smooth kNN distance calibration using 1 thread
08:13:54 Initializing from normalized Laplacian + noise
08:13:54 Commencing optimization for 500 epochs, with 250038 positive edges
08:14:12 Optimization finished
[1] "190 0.13"08:14:12 UMAP embedding parameters a = 1.478 b = 0.9272
08:14:12 Read 1203 rows and found 38 numeric columns
08:14:12 Using Annoy for neighbor search, n_neighbors = 190
08:14:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:14:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f0fd4d1
08:14:13 Searching Annoy index using 1 thread, search_k = 19000
08:14:14 Annoy recall = 100%
08:14:27 Commencing smooth kNN distance calibration using 1 thread
08:14:53 Initializing from normalized Laplacian + noise
08:14:53 Commencing optimization for 500 epochs, with 250038 positive edges
08:15:10 Optimization finished
[1] "190 0.14"08:15:10 UMAP embedding parameters a = 1.446 b = 0.938
08:15:11 Read 1203 rows and found 38 numeric columns
08:15:11 Using Annoy for neighbor search, n_neighbors = 190
08:15:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:15:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873ba56dc9
08:15:12 Searching Annoy index using 1 thread, search_k = 19000
08:15:13 Annoy recall = 100%
08:15:26 Commencing smooth kNN distance calibration using 1 thread
08:15:51 Initializing from normalized Laplacian + noise
08:15:52 Commencing optimization for 500 epochs, with 250038 positive edges
08:16:09 Optimization finished
[1] "190 0.15"08:16:09 UMAP embedding parameters a = 1.414 b = 0.9488
08:16:09 Read 1203 rows and found 38 numeric columns
08:16:09 Using Annoy for neighbor search, n_neighbors = 190
08:16:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:16:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714f7a255
08:16:10 Searching Annoy index using 1 thread, search_k = 19000
08:16:12 Annoy recall = 100%
08:16:25 Commencing smooth kNN distance calibration using 1 thread
08:16:50 Initializing from normalized Laplacian + noise
08:16:50 Commencing optimization for 500 epochs, with 250038 positive edges
08:17:08 Optimization finished
[1] "190 0.16"08:17:08 UMAP embedding parameters a = 1.383 b = 0.9596
08:17:08 Read 1203 rows and found 38 numeric columns
08:17:08 Using Annoy for neighbor search, n_neighbors = 190
08:17:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:17:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8777b382f6
08:17:09 Searching Annoy index using 1 thread, search_k = 19000
08:17:10 Annoy recall = 100%
08:17:23 Commencing smooth kNN distance calibration using 1 thread
08:17:49 Initializing from normalized Laplacian + noise
08:17:49 Commencing optimization for 500 epochs, with 250038 positive edges
08:18:07 Optimization finished
[1] "190 0.17"08:18:07 UMAP embedding parameters a = 1.352 b = 0.9704
08:18:07 Read 1203 rows and found 38 numeric columns
08:18:07 Using Annoy for neighbor search, n_neighbors = 190
08:18:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:18:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a16923e
08:18:08 Searching Annoy index using 1 thread, search_k = 19000
08:18:10 Annoy recall = 100%
08:18:23 Commencing smooth kNN distance calibration using 1 thread
08:18:48 Initializing from normalized Laplacian + noise
08:18:48 Commencing optimization for 500 epochs, with 250038 positive edges
08:19:06 Optimization finished
[1] "190 0.18"08:19:06 UMAP embedding parameters a = 1.321 b = 0.9813
08:19:06 Read 1203 rows and found 38 numeric columns
08:19:06 Using Annoy for neighbor search, n_neighbors = 190
08:19:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:19:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87772e89da
08:19:07 Searching Annoy index using 1 thread, search_k = 19000
08:19:08 Annoy recall = 100%
08:19:21 Commencing smooth kNN distance calibration using 1 thread
08:19:47 Initializing from normalized Laplacian + noise
08:19:47 Commencing optimization for 500 epochs, with 250038 positive edges
08:20:05 Optimization finished
[1] "190 0.19"08:20:05 UMAP embedding parameters a = 1.292 b = 0.9921
08:20:05 Read 1203 rows and found 38 numeric columns
08:20:05 Using Annoy for neighbor search, n_neighbors = 190
08:20:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:20:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dbb165f
08:20:06 Searching Annoy index using 1 thread, search_k = 19000
08:20:07 Annoy recall = 100%
08:20:20 Commencing smooth kNN distance calibration using 1 thread
08:20:46 Initializing from normalized Laplacian + noise
08:20:46 Commencing optimization for 500 epochs, with 250038 positive edges
08:21:03 Optimization finished
[1] "190 0.2"08:21:04 UMAP embedding parameters a = 1.262 b = 1.003
08:21:04 Read 1203 rows and found 38 numeric columns
08:21:04 Using Annoy for neighbor search, n_neighbors = 190
08:21:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:21:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8747628de4
08:21:05 Searching Annoy index using 1 thread, search_k = 19000
08:21:06 Annoy recall = 100%
08:21:19 Commencing smooth kNN distance calibration using 1 thread
08:21:45 Initializing from normalized Laplacian + noise
08:21:45 Commencing optimization for 500 epochs, with 250038 positive edges
08:22:02 Optimization finished
[1] "191 0"08:22:03 UMAP embedding parameters a = 1.933 b = 0.7905
08:22:03 Read 1203 rows and found 38 numeric columns
08:22:03 Using Annoy for neighbor search, n_neighbors = 191
08:22:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:22:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876e08133c
08:22:04 Searching Annoy index using 1 thread, search_k = 19100
08:22:05 Annoy recall = 100%
08:22:18 Commencing smooth kNN distance calibration using 1 thread
08:22:44 Initializing from normalized Laplacian + noise
08:22:44 Commencing optimization for 500 epochs, with 251174 positive edges
08:23:01 Optimization finished
[1] "191 0.01"08:23:02 UMAP embedding parameters a = 1.896 b = 0.8006
08:23:02 Read 1203 rows and found 38 numeric columns
08:23:02 Using Annoy for neighbor search, n_neighbors = 191
08:23:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:23:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87173c82e5
08:23:03 Searching Annoy index using 1 thread, search_k = 19100
08:23:04 Annoy recall = 100%
08:23:17 Commencing smooth kNN distance calibration using 1 thread
08:23:43 Initializing from normalized Laplacian + noise
08:23:43 Commencing optimization for 500 epochs, with 251174 positive edges
08:24:01 Optimization finished
[1] "191 0.02"08:24:01 UMAP embedding parameters a = 1.859 b = 0.8109
08:24:01 Read 1203 rows and found 38 numeric columns
08:24:01 Using Annoy for neighbor search, n_neighbors = 191
08:24:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:24:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743d6a9a8
08:24:02 Searching Annoy index using 1 thread, search_k = 19100
08:24:03 Annoy recall = 100%
08:24:16 Commencing smooth kNN distance calibration using 1 thread
08:24:42 Initializing from normalized Laplacian + noise
08:24:43 Commencing optimization for 500 epochs, with 251174 positive edges
08:25:00 Optimization finished
[1] "191 0.03"08:25:00 UMAP embedding parameters a = 1.822 b = 0.8212
08:25:00 Read 1203 rows and found 38 numeric columns
08:25:00 Using Annoy for neighbor search, n_neighbors = 191
08:25:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:25:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8755554373
08:25:01 Searching Annoy index using 1 thread, search_k = 19100
08:25:03 Annoy recall = 100%
08:25:16 Commencing smooth kNN distance calibration using 1 thread
08:25:42 Initializing from normalized Laplacian + noise
08:25:42 Commencing optimization for 500 epochs, with 251174 positive edges
08:26:00 Optimization finished
[1] "191 0.04"08:26:00 UMAP embedding parameters a = 1.786 b = 0.8316
08:26:00 Read 1203 rows and found 38 numeric columns
08:26:00 Using Annoy for neighbor search, n_neighbors = 191
08:26:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:26:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87471fecbf
08:26:01 Searching Annoy index using 1 thread, search_k = 19100
08:26:02 Annoy recall = 100%
08:26:15 Commencing smooth kNN distance calibration using 1 thread
08:26:41 Initializing from normalized Laplacian + noise
08:26:41 Commencing optimization for 500 epochs, with 251174 positive edges
08:26:59 Optimization finished
[1] "191 0.05"08:26:59 UMAP embedding parameters a = 1.75 b = 0.8421
08:26:59 Read 1203 rows and found 38 numeric columns
08:26:59 Using Annoy for neighbor search, n_neighbors = 191
08:26:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:27:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874dc5d8eb
08:27:00 Searching Annoy index using 1 thread, search_k = 19100
08:27:01 Annoy recall = 100%
08:27:14 Commencing smooth kNN distance calibration using 1 thread
08:27:40 Initializing from normalized Laplacian + noise
08:27:41 Commencing optimization for 500 epochs, with 251174 positive edges
08:27:58 Optimization finished
[1] "191 0.06"08:27:58 UMAP embedding parameters a = 1.715 b = 0.8526
08:27:59 Read 1203 rows and found 38 numeric columns
08:27:59 Using Annoy for neighbor search, n_neighbors = 191
08:27:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:28:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876824c7dd
08:28:00 Searching Annoy index using 1 thread, search_k = 19100
08:28:01 Annoy recall = 100%
08:28:14 Commencing smooth kNN distance calibration using 1 thread
08:28:40 Initializing from normalized Laplacian + noise
08:28:40 Commencing optimization for 500 epochs, with 251174 positive edges
08:28:58 Optimization finished
[1] "191 0.07"08:28:58 UMAP embedding parameters a = 1.68 b = 0.8631
08:28:58 Read 1203 rows and found 38 numeric columns
08:28:58 Using Annoy for neighbor search, n_neighbors = 191
08:28:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:28:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721ccd352
08:28:59 Searching Annoy index using 1 thread, search_k = 19100
08:29:00 Annoy recall = 100%
08:29:13 Commencing smooth kNN distance calibration using 1 thread
08:29:39 Initializing from normalized Laplacian + noise
08:29:39 Commencing optimization for 500 epochs, with 251174 positive edges
08:29:57 Optimization finished
[1] "191 0.08"08:29:57 UMAP embedding parameters a = 1.645 b = 0.8737
08:29:57 Read 1203 rows and found 38 numeric columns
08:29:57 Using Annoy for neighbor search, n_neighbors = 191
08:29:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:29:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ddde35a
08:29:58 Searching Annoy index using 1 thread, search_k = 19100
08:30:00 Annoy recall = 100%
08:30:13 Commencing smooth kNN distance calibration using 1 thread
08:30:39 Initializing from normalized Laplacian + noise
08:30:39 Commencing optimization for 500 epochs, with 251174 positive edges
08:30:56 Optimization finished
[1] "191 0.09"08:30:57 UMAP embedding parameters a = 1.611 b = 0.8844
08:30:57 Read 1203 rows and found 38 numeric columns
08:30:57 Using Annoy for neighbor search, n_neighbors = 191
08:30:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:30:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87467b0b49
08:30:58 Searching Annoy index using 1 thread, search_k = 19100
08:30:59 Annoy recall = 100%
08:31:12 Commencing smooth kNN distance calibration using 1 thread
08:31:38 Initializing from normalized Laplacian + noise
08:31:38 Commencing optimization for 500 epochs, with 251174 positive edges
08:31:56 Optimization finished
[1] "191 0.1"08:31:56 UMAP embedding parameters a = 1.577 b = 0.8951
08:31:56 Read 1203 rows and found 38 numeric columns
08:31:56 Using Annoy for neighbor search, n_neighbors = 191
08:31:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:31:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873652b6fc
08:31:57 Searching Annoy index using 1 thread, search_k = 19100
08:31:59 Annoy recall = 100%
08:32:12 Commencing smooth kNN distance calibration using 1 thread
08:32:38 Initializing from normalized Laplacian + noise
08:32:38 Commencing optimization for 500 epochs, with 251174 positive edges
08:32:55 Optimization finished
[1] "191 0.11"08:32:56 UMAP embedding parameters a = 1.544 b = 0.9058
08:32:56 Read 1203 rows and found 38 numeric columns
08:32:56 Using Annoy for neighbor search, n_neighbors = 191
08:32:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:32:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87384bb693
08:32:57 Searching Annoy index using 1 thread, search_k = 19100
08:32:58 Annoy recall = 100%
08:33:11 Commencing smooth kNN distance calibration using 1 thread
08:33:37 Initializing from normalized Laplacian + noise
08:33:37 Commencing optimization for 500 epochs, with 251174 positive edges
08:33:55 Optimization finished
[1] "191 0.12"08:33:55 UMAP embedding parameters a = 1.51 b = 0.9165
08:33:55 Read 1203 rows and found 38 numeric columns
08:33:55 Using Annoy for neighbor search, n_neighbors = 191
08:33:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:33:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8726f93f89
08:33:56 Searching Annoy index using 1 thread, search_k = 19100
08:33:57 Annoy recall = 100%
08:34:11 Commencing smooth kNN distance calibration using 1 thread
08:34:37 Initializing from normalized Laplacian + noise
08:34:37 Commencing optimization for 500 epochs, with 251174 positive edges
08:34:54 Optimization finished
[1] "191 0.13"08:34:55 UMAP embedding parameters a = 1.478 b = 0.9272
08:34:55 Read 1203 rows and found 38 numeric columns
08:34:55 Using Annoy for neighbor search, n_neighbors = 191
08:34:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:34:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87379f1598
08:34:56 Searching Annoy index using 1 thread, search_k = 19100
08:34:57 Annoy recall = 100%
08:35:10 Commencing smooth kNN distance calibration using 1 thread
08:35:36 Initializing from normalized Laplacian + noise
08:35:36 Commencing optimization for 500 epochs, with 251174 positive edges
08:35:54 Optimization finished
[1] "191 0.14"08:35:54 UMAP embedding parameters a = 1.446 b = 0.938
08:35:54 Read 1203 rows and found 38 numeric columns
08:35:54 Using Annoy for neighbor search, n_neighbors = 191
08:35:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:35:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ac73ff9
08:35:55 Searching Annoy index using 1 thread, search_k = 19100
08:35:56 Annoy recall = 100%
08:36:10 Commencing smooth kNN distance calibration using 1 thread
08:36:36 Initializing from normalized Laplacian + noise
08:36:36 Commencing optimization for 500 epochs, with 251174 positive edges
08:36:53 Optimization finished
[1] "191 0.15"08:36:54 UMAP embedding parameters a = 1.414 b = 0.9488
08:36:54 Read 1203 rows and found 38 numeric columns
08:36:54 Using Annoy for neighbor search, n_neighbors = 191
08:36:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:36:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f03513e
08:36:55 Searching Annoy index using 1 thread, search_k = 19100
08:36:56 Annoy recall = 100%
08:37:09 Commencing smooth kNN distance calibration using 1 thread
08:37:35 Initializing from normalized Laplacian + noise
08:37:35 Commencing optimization for 500 epochs, with 251174 positive edges
08:37:53 Optimization finished
[1] "191 0.16"08:37:53 UMAP embedding parameters a = 1.383 b = 0.9596
08:37:53 Read 1203 rows and found 38 numeric columns
08:37:53 Using Annoy for neighbor search, n_neighbors = 191
08:37:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:37:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874685e863
08:37:54 Searching Annoy index using 1 thread, search_k = 19100
08:37:56 Annoy recall = 100%
08:38:09 Commencing smooth kNN distance calibration using 1 thread
08:38:36 Initializing from normalized Laplacian + noise
08:38:36 Commencing optimization for 500 epochs, with 251174 positive edges
08:38:54 Optimization finished
[1] "191 0.17"08:38:54 UMAP embedding parameters a = 1.352 b = 0.9704
08:38:54 Read 1203 rows and found 38 numeric columns
08:38:54 Using Annoy for neighbor search, n_neighbors = 191
08:38:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:38:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87772f6104
08:38:55 Searching Annoy index using 1 thread, search_k = 19100
08:38:57 Annoy recall = 100%
08:39:10 Commencing smooth kNN distance calibration using 1 thread
08:39:37 Initializing from normalized Laplacian + noise
08:39:37 Commencing optimization for 500 epochs, with 251174 positive edges
08:39:55 Optimization finished
[1] "191 0.18"08:39:55 UMAP embedding parameters a = 1.321 b = 0.9813
08:39:55 Read 1203 rows and found 38 numeric columns
08:39:55 Using Annoy for neighbor search, n_neighbors = 191
08:39:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:39:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874f4636e7
08:39:56 Searching Annoy index using 1 thread, search_k = 19100
08:39:58 Annoy recall = 100%
08:40:11 Commencing smooth kNN distance calibration using 1 thread
08:40:38 Initializing from normalized Laplacian + noise
08:40:38 Commencing optimization for 500 epochs, with 251174 positive edges
08:40:56 Optimization finished
[1] "191 0.19"08:40:56 UMAP embedding parameters a = 1.292 b = 0.9921
08:40:56 Read 1203 rows and found 38 numeric columns
08:40:56 Using Annoy for neighbor search, n_neighbors = 191
08:40:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:40:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876341a49a
08:40:57 Searching Annoy index using 1 thread, search_k = 19100
08:40:59 Annoy recall = 100%
08:41:12 Commencing smooth kNN distance calibration using 1 thread
08:41:39 Initializing from normalized Laplacian + noise
08:41:39 Commencing optimization for 500 epochs, with 251174 positive edges
08:41:57 Optimization finished
[1] "191 0.2"08:41:57 UMAP embedding parameters a = 1.262 b = 1.003
08:41:57 Read 1203 rows and found 38 numeric columns
08:41:57 Using Annoy for neighbor search, n_neighbors = 191
08:41:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:41:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8767a3972a
08:41:58 Searching Annoy index using 1 thread, search_k = 19100
08:41:59 Annoy recall = 100%
08:42:13 Commencing smooth kNN distance calibration using 1 thread
08:42:40 Initializing from normalized Laplacian + noise
08:42:40 Commencing optimization for 500 epochs, with 251174 positive edges
08:42:58 Optimization finished
[1] "192 0"08:42:58 UMAP embedding parameters a = 1.933 b = 0.7905
08:42:58 Read 1203 rows and found 38 numeric columns
08:42:58 Using Annoy for neighbor search, n_neighbors = 192
08:42:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:42:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874dfd3c3e
08:42:59 Searching Annoy index using 1 thread, search_k = 19200
08:43:00 Annoy recall = 100%
08:43:14 Commencing smooth kNN distance calibration using 1 thread
08:43:41 Initializing from normalized Laplacian + noise
08:43:41 Commencing optimization for 500 epochs, with 252332 positive edges
08:43:59 Optimization finished
[1] "192 0.01"08:43:59 UMAP embedding parameters a = 1.896 b = 0.8006
08:43:59 Read 1203 rows and found 38 numeric columns
08:43:59 Using Annoy for neighbor search, n_neighbors = 192
08:43:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:44:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8716950c5c
08:44:00 Searching Annoy index using 1 thread, search_k = 19200
08:44:01 Annoy recall = 100%
08:44:15 Commencing smooth kNN distance calibration using 1 thread
08:44:42 Initializing from normalized Laplacian + noise
08:44:42 Commencing optimization for 500 epochs, with 252332 positive edges
08:45:00 Optimization finished
[1] "192 0.02"08:45:00 UMAP embedding parameters a = 1.859 b = 0.8109
08:45:00 Read 1203 rows and found 38 numeric columns
08:45:00 Using Annoy for neighbor search, n_neighbors = 192
08:45:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:45:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746b36bfb
08:45:01 Searching Annoy index using 1 thread, search_k = 19200
08:45:02 Annoy recall = 100%
08:45:16 Commencing smooth kNN distance calibration using 1 thread
08:45:43 Initializing from normalized Laplacian + noise
08:45:43 Commencing optimization for 500 epochs, with 252332 positive edges
08:46:01 Optimization finished
[1] "192 0.03"08:46:01 UMAP embedding parameters a = 1.822 b = 0.8212
08:46:01 Read 1203 rows and found 38 numeric columns
08:46:01 Using Annoy for neighbor search, n_neighbors = 192
08:46:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:46:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f879a2aa08
08:46:02 Searching Annoy index using 1 thread, search_k = 19200
08:46:03 Annoy recall = 100%
08:46:17 Commencing smooth kNN distance calibration using 1 thread
08:46:43 Initializing from normalized Laplacian + noise
08:46:43 Commencing optimization for 500 epochs, with 252332 positive edges
08:47:01 Optimization finished
[1] "192 0.04"08:47:02 UMAP embedding parameters a = 1.786 b = 0.8316
08:47:02 Read 1203 rows and found 38 numeric columns
08:47:02 Using Annoy for neighbor search, n_neighbors = 192
08:47:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:47:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b8caeb1
08:47:03 Searching Annoy index using 1 thread, search_k = 19200
08:47:04 Annoy recall = 100%
08:47:17 Commencing smooth kNN distance calibration using 1 thread
08:47:44 Initializing from normalized Laplacian + noise
08:47:44 Commencing optimization for 500 epochs, with 252332 positive edges
08:48:02 Optimization finished
[1] "192 0.05"08:48:02 UMAP embedding parameters a = 1.75 b = 0.8421
08:48:02 Read 1203 rows and found 38 numeric columns
08:48:02 Using Annoy for neighbor search, n_neighbors = 192
08:48:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:48:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e66eef1
08:48:03 Searching Annoy index using 1 thread, search_k = 19200
08:48:05 Annoy recall = 100%
08:48:18 Commencing smooth kNN distance calibration using 1 thread
08:48:45 Initializing from normalized Laplacian + noise
08:48:45 Commencing optimization for 500 epochs, with 252332 positive edges
08:49:03 Optimization finished
[1] "192 0.06"08:49:03 UMAP embedding parameters a = 1.715 b = 0.8526
08:49:03 Read 1203 rows and found 38 numeric columns
08:49:03 Using Annoy for neighbor search, n_neighbors = 192
08:49:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:49:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8753b93c46
08:49:04 Searching Annoy index using 1 thread, search_k = 19200
08:49:06 Annoy recall = 100%
08:49:19 Commencing smooth kNN distance calibration using 1 thread
08:49:46 Initializing from normalized Laplacian + noise
08:49:46 Commencing optimization for 500 epochs, with 252332 positive edges
08:50:04 Optimization finished
[1] "192 0.07"08:50:04 UMAP embedding parameters a = 1.68 b = 0.8631
08:50:04 Read 1203 rows and found 38 numeric columns
08:50:04 Using Annoy for neighbor search, n_neighbors = 192
08:50:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:50:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8722bb388b
08:50:05 Searching Annoy index using 1 thread, search_k = 19200
08:50:06 Annoy recall = 100%
08:50:20 Commencing smooth kNN distance calibration using 1 thread
08:50:47 Initializing from normalized Laplacian + noise
08:50:47 Commencing optimization for 500 epochs, with 252332 positive edges
08:51:05 Optimization finished
[1] "192 0.08"08:51:05 UMAP embedding parameters a = 1.645 b = 0.8737
08:51:05 Read 1203 rows and found 38 numeric columns
08:51:05 Using Annoy for neighbor search, n_neighbors = 192
08:51:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:51:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c220550
08:51:06 Searching Annoy index using 1 thread, search_k = 19200
08:51:07 Annoy recall = 100%
08:51:21 Commencing smooth kNN distance calibration using 1 thread
08:51:48 Initializing from normalized Laplacian + noise
08:51:48 Commencing optimization for 500 epochs, with 252332 positive edges
08:52:05 Optimization finished
[1] "192 0.09"08:52:06 UMAP embedding parameters a = 1.611 b = 0.8844
08:52:06 Read 1203 rows and found 38 numeric columns
08:52:06 Using Annoy for neighbor search, n_neighbors = 192
08:52:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:52:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871b1bca2a
08:52:07 Searching Annoy index using 1 thread, search_k = 19200
08:52:08 Annoy recall = 100%
08:52:22 Commencing smooth kNN distance calibration using 1 thread
08:52:49 Initializing from normalized Laplacian + noise
08:52:49 Commencing optimization for 500 epochs, with 252332 positive edges
08:53:06 Optimization finished
[1] "192 0.1"08:53:07 UMAP embedding parameters a = 1.577 b = 0.8951
08:53:07 Read 1203 rows and found 38 numeric columns
08:53:07 Using Annoy for neighbor search, n_neighbors = 192
08:53:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:53:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710c34bc8
08:53:08 Searching Annoy index using 1 thread, search_k = 19200
08:53:09 Annoy recall = 100%
08:53:23 Commencing smooth kNN distance calibration using 1 thread
08:53:50 Initializing from normalized Laplacian + noise
08:53:50 Commencing optimization for 500 epochs, with 252332 positive edges
08:54:07 Optimization finished
[1] "192 0.11"08:54:08 UMAP embedding parameters a = 1.544 b = 0.9058
08:54:08 Read 1203 rows and found 38 numeric columns
08:54:08 Using Annoy for neighbor search, n_neighbors = 192
08:54:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:54:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735e8835
08:54:09 Searching Annoy index using 1 thread, search_k = 19200
08:54:10 Annoy recall = 100%
08:54:23 Commencing smooth kNN distance calibration using 1 thread
08:54:50 Initializing from normalized Laplacian + noise
08:54:51 Commencing optimization for 500 epochs, with 252332 positive edges
08:55:08 Optimization finished
[1] "192 0.12"08:55:09 UMAP embedding parameters a = 1.51 b = 0.9165
08:55:09 Read 1203 rows and found 38 numeric columns
08:55:09 Using Annoy for neighbor search, n_neighbors = 192
08:55:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:55:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875ef273d2
08:55:10 Searching Annoy index using 1 thread, search_k = 19200
08:55:11 Annoy recall = 100%
08:55:24 Commencing smooth kNN distance calibration using 1 thread
08:55:51 Initializing from normalized Laplacian + noise
08:55:51 Commencing optimization for 500 epochs, with 252332 positive edges
08:56:09 Optimization finished
[1] "192 0.13"08:56:09 UMAP embedding parameters a = 1.478 b = 0.9272
08:56:10 Read 1203 rows and found 38 numeric columns
08:56:10 Using Annoy for neighbor search, n_neighbors = 192
08:56:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:56:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766188f3b
08:56:11 Searching Annoy index using 1 thread, search_k = 19200
08:56:12 Annoy recall = 100%
08:56:25 Commencing smooth kNN distance calibration using 1 thread
08:56:52 Initializing from normalized Laplacian + noise
08:56:52 Commencing optimization for 500 epochs, with 252332 positive edges
08:57:10 Optimization finished
[1] "192 0.14"08:57:10 UMAP embedding parameters a = 1.446 b = 0.938
08:57:10 Read 1203 rows and found 38 numeric columns
08:57:11 Using Annoy for neighbor search, n_neighbors = 192
08:57:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:57:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a7e74f4
08:57:12 Searching Annoy index using 1 thread, search_k = 19200
08:57:13 Annoy recall = 100%
08:57:26 Commencing smooth kNN distance calibration using 1 thread
08:57:53 Initializing from normalized Laplacian + noise
08:57:53 Commencing optimization for 500 epochs, with 252332 positive edges
08:58:11 Optimization finished
[1] "192 0.15"08:58:11 UMAP embedding parameters a = 1.414 b = 0.9488
08:58:11 Read 1203 rows and found 38 numeric columns
08:58:11 Using Annoy for neighbor search, n_neighbors = 192
08:58:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:58:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cb84cbe
08:58:13 Searching Annoy index using 1 thread, search_k = 19200
08:58:14 Annoy recall = 100%
08:58:27 Commencing smooth kNN distance calibration using 1 thread
08:58:54 Initializing from normalized Laplacian + noise
08:58:54 Commencing optimization for 500 epochs, with 252332 positive edges
08:59:12 Optimization finished
[1] "192 0.16"08:59:12 UMAP embedding parameters a = 1.383 b = 0.9596
08:59:12 Read 1203 rows and found 38 numeric columns
08:59:12 Using Annoy for neighbor search, n_neighbors = 192
08:59:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:59:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e3d5718
08:59:13 Searching Annoy index using 1 thread, search_k = 19200
08:59:15 Annoy recall = 100%
08:59:28 Commencing smooth kNN distance calibration using 1 thread
08:59:55 Initializing from normalized Laplacian + noise
08:59:55 Commencing optimization for 500 epochs, with 252332 positive edges
09:00:13 Optimization finished
[1] "192 0.17"09:00:13 UMAP embedding parameters a = 1.352 b = 0.9704
09:00:13 Read 1203 rows and found 38 numeric columns
09:00:13 Using Annoy for neighbor search, n_neighbors = 192
09:00:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:00:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c4b4846
09:00:14 Searching Annoy index using 1 thread, search_k = 19200
09:00:16 Annoy recall = 100%
09:00:29 Commencing smooth kNN distance calibration using 1 thread
09:00:56 Initializing from normalized Laplacian + noise
09:00:56 Commencing optimization for 500 epochs, with 252332 positive edges
09:01:14 Optimization finished
[1] "192 0.18"09:01:14 UMAP embedding parameters a = 1.321 b = 0.9813
09:01:14 Read 1203 rows and found 38 numeric columns
09:01:14 Using Annoy for neighbor search, n_neighbors = 192
09:01:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:01:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a963018
09:01:15 Searching Annoy index using 1 thread, search_k = 19200
09:01:17 Annoy recall = 100%
09:01:30 Commencing smooth kNN distance calibration using 1 thread
09:01:57 Initializing from normalized Laplacian + noise
09:01:57 Commencing optimization for 500 epochs, with 252332 positive edges
09:02:15 Optimization finished
[1] "192 0.19"09:02:15 UMAP embedding parameters a = 1.292 b = 0.9921
09:02:15 Read 1203 rows and found 38 numeric columns
09:02:15 Using Annoy for neighbor search, n_neighbors = 192
09:02:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:02:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714b86261
09:02:16 Searching Annoy index using 1 thread, search_k = 19200
09:02:18 Annoy recall = 100%
09:02:31 Commencing smooth kNN distance calibration using 1 thread
09:02:58 Initializing from normalized Laplacian + noise
09:02:58 Commencing optimization for 500 epochs, with 252332 positive edges
09:03:16 Optimization finished
[1] "192 0.2"09:03:16 UMAP embedding parameters a = 1.262 b = 1.003
09:03:16 Read 1203 rows and found 38 numeric columns
09:03:16 Using Annoy for neighbor search, n_neighbors = 192
09:03:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:03:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87229dff43
09:03:17 Searching Annoy index using 1 thread, search_k = 19200
09:03:19 Annoy recall = 100%
09:03:32 Commencing smooth kNN distance calibration using 1 thread
09:03:59 Initializing from normalized Laplacian + noise
09:03:59 Commencing optimization for 500 epochs, with 252332 positive edges
09:04:17 Optimization finished
[1] "193 0"09:04:17 UMAP embedding parameters a = 1.933 b = 0.7905
09:04:17 Read 1203 rows and found 38 numeric columns
09:04:17 Using Annoy for neighbor search, n_neighbors = 193
09:04:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:04:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762e1e6ab
09:04:18 Searching Annoy index using 1 thread, search_k = 19300
09:04:20 Annoy recall = 100%
09:04:33 Commencing smooth kNN distance calibration using 1 thread
09:05:00 Initializing from normalized Laplacian + noise
09:05:00 Commencing optimization for 500 epochs, with 253496 positive edges
09:05:18 Optimization finished
[1] "193 0.01"09:05:18 UMAP embedding parameters a = 1.896 b = 0.8006
09:05:18 Read 1203 rows and found 38 numeric columns
09:05:18 Using Annoy for neighbor search, n_neighbors = 193
09:05:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:05:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873bb1a1eb
09:05:19 Searching Annoy index using 1 thread, search_k = 19300
09:05:21 Annoy recall = 100%
09:05:34 Commencing smooth kNN distance calibration using 1 thread
09:06:01 Initializing from normalized Laplacian + noise
09:06:01 Commencing optimization for 500 epochs, with 253496 positive edges
09:06:19 Optimization finished
[1] "193 0.02"09:06:19 UMAP embedding parameters a = 1.859 b = 0.8109
09:06:19 Read 1203 rows and found 38 numeric columns
09:06:19 Using Annoy for neighbor search, n_neighbors = 193
09:06:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:06:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a3d14db
09:06:20 Searching Annoy index using 1 thread, search_k = 19300
09:06:22 Annoy recall = 100%
09:06:35 Commencing smooth kNN distance calibration using 1 thread
09:07:02 Initializing from normalized Laplacian + noise
09:07:02 Commencing optimization for 500 epochs, with 253496 positive edges
09:07:20 Optimization finished
[1] "193 0.03"09:07:20 UMAP embedding parameters a = 1.822 b = 0.8212
09:07:20 Read 1203 rows and found 38 numeric columns
09:07:20 Using Annoy for neighbor search, n_neighbors = 193
09:07:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:07:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874da926a4
09:07:21 Searching Annoy index using 1 thread, search_k = 19300
09:07:23 Annoy recall = 100%
09:07:36 Commencing smooth kNN distance calibration using 1 thread
09:08:03 Initializing from normalized Laplacian + noise
09:08:03 Commencing optimization for 500 epochs, with 253496 positive edges
09:08:21 Optimization finished
[1] "193 0.04"09:08:21 UMAP embedding parameters a = 1.786 b = 0.8316
09:08:21 Read 1203 rows and found 38 numeric columns
09:08:21 Using Annoy for neighbor search, n_neighbors = 193
09:08:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:08:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871ab4f329
09:08:22 Searching Annoy index using 1 thread, search_k = 19300
09:08:24 Annoy recall = 100%
09:08:37 Commencing smooth kNN distance calibration using 1 thread
09:09:04 Initializing from normalized Laplacian + noise
09:09:04 Commencing optimization for 500 epochs, with 253496 positive edges
09:09:22 Optimization finished
[1] "193 0.05"09:09:22 UMAP embedding parameters a = 1.75 b = 0.8421
09:09:22 Read 1203 rows and found 38 numeric columns
09:09:22 Using Annoy for neighbor search, n_neighbors = 193
09:09:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:09:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8720c2fd3e
09:09:23 Searching Annoy index using 1 thread, search_k = 19300
09:09:24 Annoy recall = 100%
09:09:38 Commencing smooth kNN distance calibration using 1 thread
09:10:05 Initializing from normalized Laplacian + noise
09:10:05 Commencing optimization for 500 epochs, with 253496 positive edges
09:10:23 Optimization finished
[1] "193 0.06"09:10:23 UMAP embedding parameters a = 1.715 b = 0.8526
09:10:23 Read 1203 rows and found 38 numeric columns
09:10:23 Using Annoy for neighbor search, n_neighbors = 193
09:10:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:10:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8744d887a8
09:10:24 Searching Annoy index using 1 thread, search_k = 19300
09:10:25 Annoy recall = 100%
09:10:39 Commencing smooth kNN distance calibration using 1 thread
09:11:06 Initializing from normalized Laplacian + noise
09:11:06 Commencing optimization for 500 epochs, with 253496 positive edges
09:11:23 Optimization finished
[1] "193 0.07"09:11:24 UMAP embedding parameters a = 1.68 b = 0.8631
09:11:24 Read 1203 rows and found 38 numeric columns
09:11:24 Using Annoy for neighbor search, n_neighbors = 193
09:11:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:11:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769fb2a10
09:11:25 Searching Annoy index using 1 thread, search_k = 19300
09:11:26 Annoy recall = 100%
09:11:40 Commencing smooth kNN distance calibration using 1 thread
09:12:07 Initializing from normalized Laplacian + noise
09:12:07 Commencing optimization for 500 epochs, with 253496 positive edges
09:12:24 Optimization finished
[1] "193 0.08"09:12:25 UMAP embedding parameters a = 1.645 b = 0.8737
09:12:25 Read 1203 rows and found 38 numeric columns
09:12:25 Using Annoy for neighbor search, n_neighbors = 193
09:12:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:12:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87404a1d8
09:12:26 Searching Annoy index using 1 thread, search_k = 19300
09:12:27 Annoy recall = 100%
09:12:40 Commencing smooth kNN distance calibration using 1 thread
09:13:07 Initializing from normalized Laplacian + noise
09:13:08 Commencing optimization for 500 epochs, with 253496 positive edges
09:13:25 Optimization finished
[1] "193 0.09"09:13:25 UMAP embedding parameters a = 1.611 b = 0.8844
09:13:26 Read 1203 rows and found 38 numeric columns
09:13:26 Using Annoy for neighbor search, n_neighbors = 193
09:13:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:13:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872c7c1ed2
09:13:27 Searching Annoy index using 1 thread, search_k = 19300
09:13:28 Annoy recall = 100%
09:13:41 Commencing smooth kNN distance calibration using 1 thread
09:14:08 Initializing from normalized Laplacian + noise
09:14:08 Commencing optimization for 500 epochs, with 253496 positive edges
09:14:26 Optimization finished
[1] "193 0.1"09:14:26 UMAP embedding parameters a = 1.577 b = 0.8951
09:14:26 Read 1203 rows and found 38 numeric columns
09:14:26 Using Annoy for neighbor search, n_neighbors = 193
09:14:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:14:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8737f8664f
09:14:27 Searching Annoy index using 1 thread, search_k = 19300
09:14:29 Annoy recall = 100%
09:14:42 Commencing smooth kNN distance calibration using 1 thread
09:15:09 Initializing from normalized Laplacian + noise
09:15:09 Commencing optimization for 500 epochs, with 253496 positive edges
09:15:27 Optimization finished
[1] "193 0.11"09:15:27 UMAP embedding parameters a = 1.544 b = 0.9058
09:15:27 Read 1203 rows and found 38 numeric columns
09:15:27 Using Annoy for neighbor search, n_neighbors = 193
09:15:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:15:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a99ae34
09:15:28 Searching Annoy index using 1 thread, search_k = 19300
09:15:30 Annoy recall = 100%
09:15:43 Commencing smooth kNN distance calibration using 1 thread
09:16:10 Initializing from normalized Laplacian + noise
09:16:10 Commencing optimization for 500 epochs, with 253496 positive edges
09:16:28 Optimization finished
[1] "193 0.12"09:16:28 UMAP embedding parameters a = 1.51 b = 0.9165
09:16:28 Read 1203 rows and found 38 numeric columns
09:16:28 Using Annoy for neighbor search, n_neighbors = 193
09:16:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:16:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87732f8acd
09:16:29 Searching Annoy index using 1 thread, search_k = 19300
09:16:31 Annoy recall = 100%
09:16:44 Commencing smooth kNN distance calibration using 1 thread
09:17:11 Initializing from normalized Laplacian + noise
09:17:11 Commencing optimization for 500 epochs, with 253496 positive edges
09:17:29 Optimization finished
[1] "193 0.13"09:17:29 UMAP embedding parameters a = 1.478 b = 0.9272
09:17:29 Read 1203 rows and found 38 numeric columns
09:17:29 Using Annoy for neighbor search, n_neighbors = 193
09:17:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:17:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87419b1057
09:17:30 Searching Annoy index using 1 thread, search_k = 19300
09:17:32 Annoy recall = 100%
09:17:45 Commencing smooth kNN distance calibration using 1 thread
09:18:12 Initializing from normalized Laplacian + noise
09:18:12 Commencing optimization for 500 epochs, with 253496 positive edges
09:18:30 Optimization finished
[1] "193 0.14"09:18:30 UMAP embedding parameters a = 1.446 b = 0.938
09:18:30 Read 1203 rows and found 38 numeric columns
09:18:30 Using Annoy for neighbor search, n_neighbors = 193
09:18:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:18:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8746265ce5
09:18:31 Searching Annoy index using 1 thread, search_k = 19300
09:18:33 Annoy recall = 100%
09:18:46 Commencing smooth kNN distance calibration using 1 thread
09:19:13 Initializing from normalized Laplacian + noise
09:19:13 Commencing optimization for 500 epochs, with 253496 positive edges
09:19:31 Optimization finished
[1] "193 0.15"09:19:31 UMAP embedding parameters a = 1.414 b = 0.9488
09:19:31 Read 1203 rows and found 38 numeric columns
09:19:31 Using Annoy for neighbor search, n_neighbors = 193
09:19:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:19:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87319679bf
09:19:32 Searching Annoy index using 1 thread, search_k = 19300
09:19:34 Annoy recall = 100%
09:19:47 Commencing smooth kNN distance calibration using 1 thread
09:20:14 Initializing from normalized Laplacian + noise
09:20:14 Commencing optimization for 500 epochs, with 253496 positive edges
09:20:32 Optimization finished
[1] "193 0.16"09:20:32 UMAP embedding parameters a = 1.383 b = 0.9596
09:20:32 Read 1203 rows and found 38 numeric columns
09:20:32 Using Annoy for neighbor search, n_neighbors = 193
09:20:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:20:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715544c9d
09:20:33 Searching Annoy index using 1 thread, search_k = 19300
09:20:35 Annoy recall = 100%
09:20:48 Commencing smooth kNN distance calibration using 1 thread
09:21:15 Initializing from normalized Laplacian + noise
09:21:15 Commencing optimization for 500 epochs, with 253496 positive edges
09:21:33 Optimization finished
[1] "193 0.17"09:21:33 UMAP embedding parameters a = 1.352 b = 0.9704
09:21:33 Read 1203 rows and found 38 numeric columns
09:21:33 Using Annoy for neighbor search, n_neighbors = 193
09:21:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:21:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768e19571
09:21:35 Searching Annoy index using 1 thread, search_k = 19300
09:21:36 Annoy recall = 100%
09:21:49 Commencing smooth kNN distance calibration using 1 thread
09:22:16 Initializing from normalized Laplacian + noise
09:22:16 Commencing optimization for 500 epochs, with 253496 positive edges
09:22:34 Optimization finished
[1] "193 0.18"09:22:35 UMAP embedding parameters a = 1.321 b = 0.9813
09:22:35 Read 1203 rows and found 38 numeric columns
09:22:35 Using Annoy for neighbor search, n_neighbors = 193
09:22:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:22:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871db87f0f
09:22:36 Searching Annoy index using 1 thread, search_k = 19300
09:22:37 Annoy recall = 100%
09:22:50 Commencing smooth kNN distance calibration using 1 thread
09:23:17 Initializing from normalized Laplacian + noise
09:23:17 Commencing optimization for 500 epochs, with 253496 positive edges
09:23:35 Optimization finished
[1] "193 0.19"09:23:36 UMAP embedding parameters a = 1.292 b = 0.9921
09:23:36 Read 1203 rows and found 38 numeric columns
09:23:36 Using Annoy for neighbor search, n_neighbors = 193
09:23:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:23:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87307016c7
09:23:37 Searching Annoy index using 1 thread, search_k = 19300
09:23:38 Annoy recall = 100%
09:23:52 Commencing smooth kNN distance calibration using 1 thread
09:24:18 Initializing from normalized Laplacian + noise
09:24:19 Commencing optimization for 500 epochs, with 253496 positive edges
09:24:37 Optimization finished
[1] "193 0.2"09:24:37 UMAP embedding parameters a = 1.262 b = 1.003
09:24:37 Read 1203 rows and found 38 numeric columns
09:24:37 Using Annoy for neighbor search, n_neighbors = 193
09:24:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:24:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8779a4e139
09:24:38 Searching Annoy index using 1 thread, search_k = 19300
09:24:39 Annoy recall = 100%
09:24:53 Commencing smooth kNN distance calibration using 1 thread
09:25:20 Initializing from normalized Laplacian + noise
09:25:20 Commencing optimization for 500 epochs, with 253496 positive edges
09:25:38 Optimization finished
[1] "194 0"09:25:38 UMAP embedding parameters a = 1.933 b = 0.7905
09:25:38 Read 1203 rows and found 38 numeric columns
09:25:38 Using Annoy for neighbor search, n_neighbors = 194
09:25:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:25:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721170745
09:25:39 Searching Annoy index using 1 thread, search_k = 19400
09:25:40 Annoy recall = 100%
09:25:54 Commencing smooth kNN distance calibration using 1 thread
09:26:21 Initializing from normalized Laplacian + noise
09:26:21 Commencing optimization for 500 epochs, with 254718 positive edges
09:26:39 Optimization finished
[1] "194 0.01"09:26:39 UMAP embedding parameters a = 1.896 b = 0.8006
09:26:39 Read 1203 rows and found 38 numeric columns
09:26:39 Using Annoy for neighbor search, n_neighbors = 194
09:26:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:26:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87f628a9a
09:26:40 Searching Annoy index using 1 thread, search_k = 19400
09:26:42 Annoy recall = 100%
09:26:55 Commencing smooth kNN distance calibration using 1 thread
09:27:22 Initializing from normalized Laplacian + noise
09:27:22 Commencing optimization for 500 epochs, with 254718 positive edges
09:27:40 Optimization finished
[1] "194 0.02"09:27:40 UMAP embedding parameters a = 1.859 b = 0.8109
09:27:41 Read 1203 rows and found 38 numeric columns
09:27:41 Using Annoy for neighbor search, n_neighbors = 194
09:27:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:27:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875fbd7074
09:27:42 Searching Annoy index using 1 thread, search_k = 19400
09:27:43 Annoy recall = 100%
09:27:56 Commencing smooth kNN distance calibration using 1 thread
09:28:23 Initializing from normalized Laplacian + noise
09:28:23 Commencing optimization for 500 epochs, with 254718 positive edges
09:28:41 Optimization finished
[1] "194 0.03"09:28:42 UMAP embedding parameters a = 1.822 b = 0.8212
09:28:42 Read 1203 rows and found 38 numeric columns
09:28:42 Using Annoy for neighbor search, n_neighbors = 194
09:28:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:28:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876b957c39
09:28:43 Searching Annoy index using 1 thread, search_k = 19400
09:28:44 Annoy recall = 100%
09:28:58 Commencing smooth kNN distance calibration using 1 thread
09:29:25 Initializing from normalized Laplacian + noise
09:29:25 Commencing optimization for 500 epochs, with 254718 positive edges
09:29:43 Optimization finished
[1] "194 0.04"09:29:43 UMAP embedding parameters a = 1.786 b = 0.8316
09:29:43 Read 1203 rows and found 38 numeric columns
09:29:43 Using Annoy for neighbor search, n_neighbors = 194
09:29:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:29:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873c1ad758
09:29:44 Searching Annoy index using 1 thread, search_k = 19400
09:29:45 Annoy recall = 100%
09:29:59 Commencing smooth kNN distance calibration using 1 thread
09:30:26 Initializing from normalized Laplacian + noise
09:30:26 Commencing optimization for 500 epochs, with 254718 positive edges
09:30:44 Optimization finished
[1] "194 0.05"09:30:44 UMAP embedding parameters a = 1.75 b = 0.8421
09:30:44 Read 1203 rows and found 38 numeric columns
09:30:44 Using Annoy for neighbor search, n_neighbors = 194
09:30:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:30:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dfac78d
09:30:45 Searching Annoy index using 1 thread, search_k = 19400
09:30:47 Annoy recall = 100%
09:31:00 Commencing smooth kNN distance calibration using 1 thread
09:31:27 Initializing from normalized Laplacian + noise
09:31:28 Commencing optimization for 500 epochs, with 254718 positive edges
09:31:45 Optimization finished
[1] "194 0.06"09:31:46 UMAP embedding parameters a = 1.715 b = 0.8526
09:31:46 Read 1203 rows and found 38 numeric columns
09:31:46 Using Annoy for neighbor search, n_neighbors = 194
09:31:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:31:47 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757e0c480
09:31:47 Searching Annoy index using 1 thread, search_k = 19400
09:31:48 Annoy recall = 100%
09:32:02 Commencing smooth kNN distance calibration using 1 thread
09:32:29 Initializing from normalized Laplacian + noise
09:32:29 Commencing optimization for 500 epochs, with 254718 positive edges
09:32:47 Optimization finished
[1] "194 0.07"09:32:47 UMAP embedding parameters a = 1.68 b = 0.8631
09:32:47 Read 1203 rows and found 38 numeric columns
09:32:47 Using Annoy for neighbor search, n_neighbors = 194
09:32:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:32:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8766b10770
09:32:48 Searching Annoy index using 1 thread, search_k = 19400
09:32:49 Annoy recall = 100%
09:33:03 Commencing smooth kNN distance calibration using 1 thread
09:33:30 Initializing from normalized Laplacian + noise
09:33:30 Commencing optimization for 500 epochs, with 254718 positive edges
09:33:48 Optimization finished
[1] "194 0.08"09:33:48 UMAP embedding parameters a = 1.645 b = 0.8737
09:33:48 Read 1203 rows and found 38 numeric columns
09:33:48 Using Annoy for neighbor search, n_neighbors = 194
09:33:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:33:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742b329ee
09:33:49 Searching Annoy index using 1 thread, search_k = 19400
09:33:51 Annoy recall = 100%
09:34:04 Commencing smooth kNN distance calibration using 1 thread
09:34:32 Initializing from normalized Laplacian + noise
09:34:32 Commencing optimization for 500 epochs, with 254718 positive edges
09:34:49 Optimization finished
[1] "194 0.09"09:34:50 UMAP embedding parameters a = 1.611 b = 0.8844
09:34:50 Read 1203 rows and found 38 numeric columns
09:34:50 Using Annoy for neighbor search, n_neighbors = 194
09:34:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:34:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877a7ec3c3
09:34:51 Searching Annoy index using 1 thread, search_k = 19400
09:34:52 Annoy recall = 100%
09:35:06 Commencing smooth kNN distance calibration using 1 thread
09:35:33 Initializing from normalized Laplacian + noise
09:35:33 Commencing optimization for 500 epochs, with 254718 positive edges
09:35:51 Optimization finished
[1] "194 0.1"09:35:51 UMAP embedding parameters a = 1.577 b = 0.8951
09:35:51 Read 1203 rows and found 38 numeric columns
09:35:51 Using Annoy for neighbor search, n_neighbors = 194
09:35:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:35:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874992ee1b
09:35:52 Searching Annoy index using 1 thread, search_k = 19400
09:35:54 Annoy recall = 100%
09:36:07 Commencing smooth kNN distance calibration using 1 thread
09:36:34 Initializing from normalized Laplacian + noise
09:36:34 Commencing optimization for 500 epochs, with 254718 positive edges
09:36:52 Optimization finished
[1] "194 0.11"09:36:52 UMAP embedding parameters a = 1.544 b = 0.9058
09:36:52 Read 1203 rows and found 38 numeric columns
09:36:52 Using Annoy for neighbor search, n_neighbors = 194
09:36:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:36:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877e64cbd9
09:36:54 Searching Annoy index using 1 thread, search_k = 19400
09:36:55 Annoy recall = 100%
09:37:08 Commencing smooth kNN distance calibration using 1 thread
09:37:36 Initializing from normalized Laplacian + noise
09:37:36 Commencing optimization for 500 epochs, with 254718 positive edges
09:37:54 Optimization finished
[1] "194 0.12"09:37:54 UMAP embedding parameters a = 1.51 b = 0.9165
09:37:54 Read 1203 rows and found 38 numeric columns
09:37:54 Using Annoy for neighbor search, n_neighbors = 194
09:37:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:37:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8754bbd89e
09:37:55 Searching Annoy index using 1 thread, search_k = 19400
09:37:56 Annoy recall = 100%
09:38:10 Commencing smooth kNN distance calibration using 1 thread
09:38:37 Initializing from normalized Laplacian + noise
09:38:37 Commencing optimization for 500 epochs, with 254718 positive edges
09:38:55 Optimization finished
[1] "194 0.13"09:38:55 UMAP embedding parameters a = 1.478 b = 0.9272
09:38:55 Read 1203 rows and found 38 numeric columns
09:38:55 Using Annoy for neighbor search, n_neighbors = 194
09:38:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:38:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87173c14bf
09:38:56 Searching Annoy index using 1 thread, search_k = 19400
09:38:58 Annoy recall = 100%
09:39:11 Commencing smooth kNN distance calibration using 1 thread
09:39:38 Initializing from normalized Laplacian + noise
09:39:38 Commencing optimization for 500 epochs, with 254718 positive edges
09:39:57 Optimization finished
[1] "194 0.14"09:39:57 UMAP embedding parameters a = 1.446 b = 0.938
09:39:57 Read 1203 rows and found 38 numeric columns
09:39:57 Using Annoy for neighbor search, n_neighbors = 194
09:39:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:39:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871919bf03
09:39:58 Searching Annoy index using 1 thread, search_k = 19400
09:39:59 Annoy recall = 100%
09:40:13 Commencing smooth kNN distance calibration using 1 thread
09:40:40 Initializing from normalized Laplacian + noise
09:40:40 Commencing optimization for 500 epochs, with 254718 positive edges
09:40:58 Optimization finished
[1] "194 0.15"09:40:58 UMAP embedding parameters a = 1.414 b = 0.9488
09:40:58 Read 1203 rows and found 38 numeric columns
09:40:58 Using Annoy for neighbor search, n_neighbors = 194
09:40:58 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:40:59 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87757ed5dd
09:40:59 Searching Annoy index using 1 thread, search_k = 19400
09:41:01 Annoy recall = 100%
09:41:14 Commencing smooth kNN distance calibration using 1 thread
09:41:41 Initializing from normalized Laplacian + noise
09:41:41 Commencing optimization for 500 epochs, with 254718 positive edges
09:41:59 Optimization finished
[1] "194 0.16"09:42:00 UMAP embedding parameters a = 1.383 b = 0.9596
09:42:00 Read 1203 rows and found 38 numeric columns
09:42:00 Using Annoy for neighbor search, n_neighbors = 194
09:42:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:42:01 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c149c67
09:42:01 Searching Annoy index using 1 thread, search_k = 19400
09:42:02 Annoy recall = 100%
09:42:16 Commencing smooth kNN distance calibration using 1 thread
09:42:43 Initializing from normalized Laplacian + noise
09:42:43 Commencing optimization for 500 epochs, with 254718 positive edges
09:43:01 Optimization finished
[1] "194 0.17"09:43:01 UMAP embedding parameters a = 1.352 b = 0.9704
09:43:01 Read 1203 rows and found 38 numeric columns
09:43:01 Using Annoy for neighbor search, n_neighbors = 194
09:43:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:43:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87314e913
09:43:02 Searching Annoy index using 1 thread, search_k = 19400
09:43:04 Annoy recall = 100%
09:43:17 Commencing smooth kNN distance calibration using 1 thread
09:43:44 Initializing from normalized Laplacian + noise
09:43:44 Commencing optimization for 500 epochs, with 254718 positive edges
09:44:03 Optimization finished
[1] "194 0.18"09:44:03 UMAP embedding parameters a = 1.321 b = 0.9813
09:44:03 Read 1203 rows and found 38 numeric columns
09:44:03 Using Annoy for neighbor search, n_neighbors = 194
09:44:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:44:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87798377b5
09:44:04 Searching Annoy index using 1 thread, search_k = 19400
09:44:05 Annoy recall = 100%
09:44:19 Commencing smooth kNN distance calibration using 1 thread
09:44:46 Initializing from normalized Laplacian + noise
09:44:46 Commencing optimization for 500 epochs, with 254718 positive edges
09:45:04 Optimization finished
[1] "194 0.19"09:45:04 UMAP embedding parameters a = 1.292 b = 0.9921
09:45:04 Read 1203 rows and found 38 numeric columns
09:45:04 Using Annoy for neighbor search, n_neighbors = 194
09:45:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:45:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87890bb39
09:45:05 Searching Annoy index using 1 thread, search_k = 19400
09:45:07 Annoy recall = 100%
09:45:20 Commencing smooth kNN distance calibration using 1 thread
09:45:47 Initializing from normalized Laplacian + noise
09:45:47 Commencing optimization for 500 epochs, with 254718 positive edges
09:46:06 Optimization finished
[1] "194 0.2"09:46:06 UMAP embedding parameters a = 1.262 b = 1.003
09:46:06 Read 1203 rows and found 38 numeric columns
09:46:06 Using Annoy for neighbor search, n_neighbors = 194
09:46:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:46:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873b0d4f62
09:46:07 Searching Annoy index using 1 thread, search_k = 19400
09:46:08 Annoy recall = 100%
09:46:22 Commencing smooth kNN distance calibration using 1 thread
09:46:49 Initializing from normalized Laplacian + noise
09:46:49 Commencing optimization for 500 epochs, with 254718 positive edges
09:47:07 Optimization finished
[1] "195 0"09:47:07 UMAP embedding parameters a = 1.933 b = 0.7905
09:47:08 Read 1203 rows and found 38 numeric columns
09:47:08 Using Annoy for neighbor search, n_neighbors = 195
09:47:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:47:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87141d25ea
09:47:09 Searching Annoy index using 1 thread, search_k = 19500
09:47:10 Annoy recall = 100%
09:47:24 Commencing smooth kNN distance calibration using 1 thread
09:47:51 Initializing from normalized Laplacian + noise
09:47:51 Commencing optimization for 500 epochs, with 255830 positive edges
09:48:09 Optimization finished
[1] "195 0.01"09:48:09 UMAP embedding parameters a = 1.896 b = 0.8006
09:48:09 Read 1203 rows and found 38 numeric columns
09:48:09 Using Annoy for neighbor search, n_neighbors = 195
09:48:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:48:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877bc04606
09:48:10 Searching Annoy index using 1 thread, search_k = 19500
09:48:12 Annoy recall = 100%
09:48:25 Commencing smooth kNN distance calibration using 1 thread
09:48:52 Initializing from normalized Laplacian + noise
09:48:52 Commencing optimization for 500 epochs, with 255830 positive edges
09:49:10 Optimization finished
[1] "195 0.02"09:49:11 UMAP embedding parameters a = 1.859 b = 0.8109
09:49:11 Read 1203 rows and found 38 numeric columns
09:49:11 Using Annoy for neighbor search, n_neighbors = 195
09:49:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:49:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ca85fb9
09:49:12 Searching Annoy index using 1 thread, search_k = 19500
09:49:13 Annoy recall = 100%
09:49:27 Commencing smooth kNN distance calibration using 1 thread
09:49:54 Initializing from normalized Laplacian + noise
09:49:54 Commencing optimization for 500 epochs, with 255830 positive edges
09:50:12 Optimization finished
[1] "195 0.03"09:50:12 UMAP embedding parameters a = 1.822 b = 0.8212
09:50:12 Read 1203 rows and found 38 numeric columns
09:50:12 Using Annoy for neighbor search, n_neighbors = 195
09:50:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:50:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875a4382cf
09:50:13 Searching Annoy index using 1 thread, search_k = 19500
09:50:15 Annoy recall = 100%
09:50:28 Commencing smooth kNN distance calibration using 1 thread
09:50:55 Initializing from normalized Laplacian + noise
09:50:55 Commencing optimization for 500 epochs, with 255830 positive edges
09:51:14 Optimization finished
[1] "195 0.04"09:51:14 UMAP embedding parameters a = 1.786 b = 0.8316
09:51:14 Read 1203 rows and found 38 numeric columns
09:51:14 Using Annoy for neighbor search, n_neighbors = 195
09:51:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:51:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d56bfc5
09:51:15 Searching Annoy index using 1 thread, search_k = 19500
09:51:16 Annoy recall = 100%
09:51:30 Commencing smooth kNN distance calibration using 1 thread
09:51:57 Initializing from normalized Laplacian + noise
09:51:57 Commencing optimization for 500 epochs, with 255830 positive edges
09:52:15 Optimization finished
[1] "195 0.05"09:52:16 UMAP embedding parameters a = 1.75 b = 0.8421
09:52:16 Read 1203 rows and found 38 numeric columns
09:52:16 Using Annoy for neighbor search, n_neighbors = 195
09:52:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:52:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8711fcac56
09:52:17 Searching Annoy index using 1 thread, search_k = 19500
09:52:18 Annoy recall = 100%
09:52:32 Commencing smooth kNN distance calibration using 1 thread
09:52:59 Initializing from normalized Laplacian + noise
09:52:59 Commencing optimization for 500 epochs, with 255830 positive edges
09:53:17 Optimization finished
[1] "195 0.06"09:53:17 UMAP embedding parameters a = 1.715 b = 0.8526
09:53:17 Read 1203 rows and found 38 numeric columns
09:53:17 Using Annoy for neighbor search, n_neighbors = 195
09:53:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:53:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8743251840
09:53:18 Searching Annoy index using 1 thread, search_k = 19500
09:53:20 Annoy recall = 100%
09:53:33 Commencing smooth kNN distance calibration using 1 thread
09:54:00 Initializing from normalized Laplacian + noise
09:54:00 Commencing optimization for 500 epochs, with 255830 positive edges
09:54:19 Optimization finished
[1] "195 0.07"09:54:19 UMAP embedding parameters a = 1.68 b = 0.8631
09:54:19 Read 1203 rows and found 38 numeric columns
09:54:19 Using Annoy for neighbor search, n_neighbors = 195
09:54:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:54:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874b0f3ed5
09:54:20 Searching Annoy index using 1 thread, search_k = 19500
09:54:21 Annoy recall = 100%
09:54:35 Commencing smooth kNN distance calibration using 1 thread
09:55:02 Initializing from normalized Laplacian + noise
09:55:02 Commencing optimization for 500 epochs, with 255830 positive edges
09:55:20 Optimization finished
[1] "195 0.08"09:55:20 UMAP embedding parameters a = 1.645 b = 0.8737
09:55:20 Read 1203 rows and found 38 numeric columns
09:55:20 Using Annoy for neighbor search, n_neighbors = 195
09:55:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:55:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87426cc31e
09:55:22 Searching Annoy index using 1 thread, search_k = 19500
09:55:23 Annoy recall = 100%
09:55:37 Commencing smooth kNN distance calibration using 1 thread
09:56:04 Initializing from normalized Laplacian + noise
09:56:04 Commencing optimization for 500 epochs, with 255830 positive edges
09:56:22 Optimization finished
[1] "195 0.09"09:56:22 UMAP embedding parameters a = 1.611 b = 0.8844
09:56:22 Read 1203 rows and found 38 numeric columns
09:56:22 Using Annoy for neighbor search, n_neighbors = 195
09:56:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:56:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873cc9f979
09:56:23 Searching Annoy index using 1 thread, search_k = 19500
09:56:25 Annoy recall = 100%
09:56:38 Commencing smooth kNN distance calibration using 1 thread
09:57:05 Initializing from normalized Laplacian + noise
09:57:06 Commencing optimization for 500 epochs, with 255830 positive edges
09:57:23 Optimization finished
[1] "195 0.1"09:57:24 UMAP embedding parameters a = 1.577 b = 0.8951
09:57:24 Read 1203 rows and found 38 numeric columns
09:57:24 Using Annoy for neighbor search, n_neighbors = 195
09:57:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:57:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c26461a
09:57:25 Searching Annoy index using 1 thread, search_k = 19500
09:57:26 Annoy recall = 100%
09:57:40 Commencing smooth kNN distance calibration using 1 thread
09:58:07 Initializing from normalized Laplacian + noise
09:58:07 Commencing optimization for 500 epochs, with 255830 positive edges
09:58:25 Optimization finished
[1] "195 0.11"09:58:25 UMAP embedding parameters a = 1.544 b = 0.9058
09:58:25 Read 1203 rows and found 38 numeric columns
09:58:25 Using Annoy for neighbor search, n_neighbors = 195
09:58:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:58:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8751cf4db8
09:58:27 Searching Annoy index using 1 thread, search_k = 19500
09:58:28 Annoy recall = 100%
09:58:42 Commencing smooth kNN distance calibration using 1 thread
09:59:09 Initializing from normalized Laplacian + noise
09:59:09 Commencing optimization for 500 epochs, with 255830 positive edges
09:59:27 Optimization finished
[1] "195 0.12"09:59:27 UMAP embedding parameters a = 1.51 b = 0.9165
09:59:27 Read 1203 rows and found 38 numeric columns
09:59:27 Using Annoy for neighbor search, n_neighbors = 195
09:59:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
09:59:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871c8769ee
09:59:28 Searching Annoy index using 1 thread, search_k = 19500
09:59:30 Annoy recall = 100%
09:59:43 Commencing smooth kNN distance calibration using 1 thread
10:00:11 Initializing from normalized Laplacian + noise
10:00:11 Commencing optimization for 500 epochs, with 255830 positive edges
10:00:29 Optimization finished
[1] "195 0.13"10:00:29 UMAP embedding parameters a = 1.478 b = 0.9272
10:00:29 Read 1203 rows and found 38 numeric columns
10:00:29 Using Annoy for neighbor search, n_neighbors = 195
10:00:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:00:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8757bbc253
10:00:30 Searching Annoy index using 1 thread, search_k = 19500
10:00:31 Annoy recall = 100%
10:00:45 Commencing smooth kNN distance calibration using 1 thread
10:01:12 Initializing from normalized Laplacian + noise
10:01:12 Commencing optimization for 500 epochs, with 255830 positive edges
10:01:30 Optimization finished
[1] "195 0.14"10:01:31 UMAP embedding parameters a = 1.446 b = 0.938
10:01:31 Read 1203 rows and found 38 numeric columns
10:01:31 Using Annoy for neighbor search, n_neighbors = 195
10:01:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:01:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87dea2510
10:01:32 Searching Annoy index using 1 thread, search_k = 19500
10:01:33 Annoy recall = 100%
10:01:47 Commencing smooth kNN distance calibration using 1 thread
10:02:14 Initializing from normalized Laplacian + noise
10:02:14 Commencing optimization for 500 epochs, with 255830 positive edges
10:02:32 Optimization finished
[1] "195 0.15"10:02:32 UMAP embedding parameters a = 1.414 b = 0.9488
10:02:32 Read 1203 rows and found 38 numeric columns
10:02:32 Using Annoy for neighbor search, n_neighbors = 195
10:02:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:02:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874a82317b
10:02:34 Searching Annoy index using 1 thread, search_k = 19500
10:02:35 Annoy recall = 100%
10:02:48 Commencing smooth kNN distance calibration using 1 thread
10:03:16 Initializing from normalized Laplacian + noise
10:03:16 Commencing optimization for 500 epochs, with 255830 positive edges
10:03:34 Optimization finished
[1] "195 0.16"10:03:34 UMAP embedding parameters a = 1.383 b = 0.9596
10:03:34 Read 1203 rows and found 38 numeric columns
10:03:34 Using Annoy for neighbor search, n_neighbors = 195
10:03:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:03:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872f9c86d3
10:03:35 Searching Annoy index using 1 thread, search_k = 19500
10:03:37 Annoy recall = 100%
10:03:50 Commencing smooth kNN distance calibration using 1 thread
10:04:18 Initializing from normalized Laplacian + noise
10:04:18 Commencing optimization for 500 epochs, with 255830 positive edges
10:04:36 Optimization finished
[1] "195 0.17"10:04:36 UMAP embedding parameters a = 1.352 b = 0.9704
10:04:36 Read 1203 rows and found 38 numeric columns
10:04:36 Using Annoy for neighbor search, n_neighbors = 195
10:04:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:04:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87749b2c80
10:04:37 Searching Annoy index using 1 thread, search_k = 19500
10:04:38 Annoy recall = 100%
10:04:52 Commencing smooth kNN distance calibration using 1 thread
10:05:19 Initializing from normalized Laplacian + noise
10:05:19 Commencing optimization for 500 epochs, with 255830 positive edges
10:05:38 Optimization finished
[1] "195 0.18"10:05:38 UMAP embedding parameters a = 1.321 b = 0.9813
10:05:38 Read 1203 rows and found 38 numeric columns
10:05:38 Using Annoy for neighbor search, n_neighbors = 195
10:05:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:05:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87d355b69
10:05:39 Searching Annoy index using 1 thread, search_k = 19500
10:05:40 Annoy recall = 100%
10:05:54 Commencing smooth kNN distance calibration using 1 thread
10:06:21 Initializing from normalized Laplacian + noise
10:06:21 Commencing optimization for 500 epochs, with 255830 positive edges
10:06:40 Optimization finished
[1] "195 0.19"10:06:40 UMAP embedding parameters a = 1.292 b = 0.9921
10:06:40 Read 1203 rows and found 38 numeric columns
10:06:40 Using Annoy for neighbor search, n_neighbors = 195
10:06:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:06:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a1b4a96
10:06:41 Searching Annoy index using 1 thread, search_k = 19500
10:06:42 Annoy recall = 100%
10:06:56 Commencing smooth kNN distance calibration using 1 thread
10:07:23 Initializing from normalized Laplacian + noise
10:07:23 Commencing optimization for 500 epochs, with 255830 positive edges
10:07:41 Optimization finished
[1] "195 0.2"10:07:42 UMAP embedding parameters a = 1.262 b = 1.003
10:07:42 Read 1203 rows and found 38 numeric columns
10:07:42 Using Annoy for neighbor search, n_neighbors = 195
10:07:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:07:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e2e1a9b
10:07:43 Searching Annoy index using 1 thread, search_k = 19500
10:07:44 Annoy recall = 100%
10:07:57 Commencing smooth kNN distance calibration using 1 thread
10:08:25 Initializing from normalized Laplacian + noise
10:08:25 Commencing optimization for 500 epochs, with 255830 positive edges
10:08:43 Optimization finished
[1] "196 0"10:08:44 UMAP embedding parameters a = 1.933 b = 0.7905
10:08:44 Read 1203 rows and found 38 numeric columns
10:08:44 Using Annoy for neighbor search, n_neighbors = 196
10:08:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:08:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b9a2743
10:08:45 Searching Annoy index using 1 thread, search_k = 19600
10:08:46 Annoy recall = 100%
10:08:59 Commencing smooth kNN distance calibration using 1 thread
10:09:27 Initializing from normalized Laplacian + noise
10:09:27 Commencing optimization for 500 epochs, with 256948 positive edges
10:09:45 Optimization finished
[1] "196 0.01"10:09:45 UMAP embedding parameters a = 1.896 b = 0.8006
10:09:45 Read 1203 rows and found 38 numeric columns
10:09:45 Using Annoy for neighbor search, n_neighbors = 196
10:09:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:09:46 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ed72335
10:09:46 Searching Annoy index using 1 thread, search_k = 19600
10:09:48 Annoy recall = 100%
10:10:01 Commencing smooth kNN distance calibration using 1 thread
10:10:29 Initializing from normalized Laplacian + noise
10:10:29 Commencing optimization for 500 epochs, with 256948 positive edges
10:10:47 Optimization finished
[1] "196 0.02"10:10:47 UMAP embedding parameters a = 1.859 b = 0.8109
10:10:47 Read 1203 rows and found 38 numeric columns
10:10:47 Using Annoy for neighbor search, n_neighbors = 196
10:10:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:10:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87556a2f5a
10:10:48 Searching Annoy index using 1 thread, search_k = 19600
10:10:50 Annoy recall = 100%
10:11:03 Commencing smooth kNN distance calibration using 1 thread
10:11:30 Initializing from normalized Laplacian + noise
10:11:31 Commencing optimization for 500 epochs, with 256948 positive edges
10:11:49 Optimization finished
[1] "196 0.03"10:11:49 UMAP embedding parameters a = 1.822 b = 0.8212
10:11:49 Read 1203 rows and found 38 numeric columns
10:11:49 Using Annoy for neighbor search, n_neighbors = 196
10:11:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:11:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8724b3e646
10:11:50 Searching Annoy index using 1 thread, search_k = 19600
10:11:52 Annoy recall = 100%
10:12:05 Commencing smooth kNN distance calibration using 1 thread
10:12:32 Initializing from normalized Laplacian + noise
10:12:33 Commencing optimization for 500 epochs, with 256948 positive edges
10:12:51 Optimization finished
[1] "196 0.04"10:12:51 UMAP embedding parameters a = 1.786 b = 0.8316
10:12:51 Read 1203 rows and found 38 numeric columns
10:12:51 Using Annoy for neighbor search, n_neighbors = 196
10:12:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:12:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877455f912
10:12:52 Searching Annoy index using 1 thread, search_k = 19600
10:12:54 Annoy recall = 100%
10:13:07 Commencing smooth kNN distance calibration using 1 thread
10:13:34 Initializing from normalized Laplacian + noise
10:13:34 Commencing optimization for 500 epochs, with 256948 positive edges
10:13:53 Optimization finished
[1] "196 0.05"10:13:53 UMAP embedding parameters a = 1.75 b = 0.8421
10:13:53 Read 1203 rows and found 38 numeric columns
10:13:53 Using Annoy for neighbor search, n_neighbors = 196
10:13:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:13:54 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87317ecbc1
10:13:54 Searching Annoy index using 1 thread, search_k = 19600
10:13:55 Annoy recall = 100%
10:14:09 Commencing smooth kNN distance calibration using 1 thread
10:14:36 Initializing from normalized Laplacian + noise
10:14:36 Commencing optimization for 500 epochs, with 256948 positive edges
10:14:55 Optimization finished
[1] "196 0.06"10:14:55 UMAP embedding parameters a = 1.715 b = 0.8526
10:14:55 Read 1203 rows and found 38 numeric columns
10:14:55 Using Annoy for neighbor search, n_neighbors = 196
10:14:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:14:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8727c8cf59
10:14:56 Searching Annoy index using 1 thread, search_k = 19600
10:14:57 Annoy recall = 100%
10:15:11 Commencing smooth kNN distance calibration using 1 thread
10:15:38 Initializing from normalized Laplacian + noise
10:15:38 Commencing optimization for 500 epochs, with 256948 positive edges
10:15:57 Optimization finished
[1] "196 0.07"10:15:57 UMAP embedding parameters a = 1.68 b = 0.8631
10:15:57 Read 1203 rows and found 38 numeric columns
10:15:57 Using Annoy for neighbor search, n_neighbors = 196
10:15:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:15:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876dd970c7
10:15:58 Searching Annoy index using 1 thread, search_k = 19600
10:15:59 Annoy recall = 100%
10:16:13 Commencing smooth kNN distance calibration using 1 thread
10:16:40 Initializing from normalized Laplacian + noise
10:16:40 Commencing optimization for 500 epochs, with 256948 positive edges
10:16:59 Optimization finished
[1] "196 0.08"10:16:59 UMAP embedding parameters a = 1.645 b = 0.8737
10:16:59 Read 1203 rows and found 38 numeric columns
10:16:59 Using Annoy for neighbor search, n_neighbors = 196
10:16:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:17:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a0f86fa
10:17:00 Searching Annoy index using 1 thread, search_k = 19600
10:17:01 Annoy recall = 100%
10:17:15 Commencing smooth kNN distance calibration using 1 thread
10:17:42 Initializing from normalized Laplacian + noise
10:17:42 Commencing optimization for 500 epochs, with 256948 positive edges
10:18:01 Optimization finished
[1] "196 0.09"10:18:01 UMAP embedding parameters a = 1.611 b = 0.8844
10:18:01 Read 1203 rows and found 38 numeric columns
10:18:01 Using Annoy for neighbor search, n_neighbors = 196
10:18:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:18:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8762d61ebc
10:18:02 Searching Annoy index using 1 thread, search_k = 19600
10:18:03 Annoy recall = 100%
10:18:17 Commencing smooth kNN distance calibration using 1 thread
10:18:44 Initializing from normalized Laplacian + noise
10:18:44 Commencing optimization for 500 epochs, with 256948 positive edges
10:19:03 Optimization finished
[1] "196 0.1"10:19:03 UMAP embedding parameters a = 1.577 b = 0.8951
10:19:03 Read 1203 rows and found 38 numeric columns
10:19:03 Using Annoy for neighbor search, n_neighbors = 196
10:19:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:19:04 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f696b1
10:19:04 Searching Annoy index using 1 thread, search_k = 19600
10:19:05 Annoy recall = 100%
10:19:19 Commencing smooth kNN distance calibration using 1 thread
10:19:46 Initializing from normalized Laplacian + noise
10:19:46 Commencing optimization for 500 epochs, with 256948 positive edges
10:20:05 Optimization finished
[1] "196 0.11"10:20:05 UMAP embedding parameters a = 1.544 b = 0.9058
10:20:05 Read 1203 rows and found 38 numeric columns
10:20:05 Using Annoy for neighbor search, n_neighbors = 196
10:20:05 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:20:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8735cfcd00
10:20:06 Searching Annoy index using 1 thread, search_k = 19600
10:20:07 Annoy recall = 100%
10:20:21 Commencing smooth kNN distance calibration using 1 thread
10:20:48 Initializing from normalized Laplacian + noise
10:20:49 Commencing optimization for 500 epochs, with 256948 positive edges
10:21:07 Optimization finished
[1] "196 0.12"10:21:07 UMAP embedding parameters a = 1.51 b = 0.9165
10:21:07 Read 1203 rows and found 38 numeric columns
10:21:07 Using Annoy for neighbor search, n_neighbors = 196
10:21:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:21:08 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875f7e7e75
10:21:08 Searching Annoy index using 1 thread, search_k = 19600
10:21:09 Annoy recall = 100%
10:21:23 Commencing smooth kNN distance calibration using 1 thread
10:21:50 Initializing from normalized Laplacian + noise
10:21:51 Commencing optimization for 500 epochs, with 256948 positive edges
10:22:09 Optimization finished
[1] "196 0.13"10:22:09 UMAP embedding parameters a = 1.478 b = 0.9272
10:22:09 Read 1203 rows and found 38 numeric columns
10:22:09 Using Annoy for neighbor search, n_neighbors = 196
10:22:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:22:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c3a1981
10:22:10 Searching Annoy index using 1 thread, search_k = 19600
10:22:11 Annoy recall = 100%
10:22:25 Commencing smooth kNN distance calibration using 1 thread
10:22:52 Initializing from normalized Laplacian + noise
10:22:53 Commencing optimization for 500 epochs, with 256948 positive edges
10:23:11 Optimization finished
[1] "196 0.14"10:23:11 UMAP embedding parameters a = 1.446 b = 0.938
10:23:11 Read 1203 rows and found 38 numeric columns
10:23:11 Using Annoy for neighbor search, n_neighbors = 196
10:23:11 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:23:12 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8763268cc6
10:23:12 Searching Annoy index using 1 thread, search_k = 19600
10:23:13 Annoy recall = 100%
10:23:27 Commencing smooth kNN distance calibration using 1 thread
10:23:55 Initializing from normalized Laplacian + noise
10:23:55 Commencing optimization for 500 epochs, with 256948 positive edges
10:24:13 Optimization finished
[1] "196 0.15"10:24:13 UMAP embedding parameters a = 1.414 b = 0.9488
10:24:13 Read 1203 rows and found 38 numeric columns
10:24:13 Using Annoy for neighbor search, n_neighbors = 196
10:24:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:24:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87717b2acc
10:24:14 Searching Annoy index using 1 thread, search_k = 19600
10:24:15 Annoy recall = 100%
10:24:29 Commencing smooth kNN distance calibration using 1 thread
10:24:56 Initializing from normalized Laplacian + noise
10:24:56 Commencing optimization for 500 epochs, with 256948 positive edges
10:25:14 Optimization finished
[1] "196 0.16"10:25:14 UMAP embedding parameters a = 1.383 b = 0.9596
10:25:14 Read 1203 rows and found 38 numeric columns
10:25:14 Using Annoy for neighbor search, n_neighbors = 196
10:25:14 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:25:15 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871f5f31c1
10:25:15 Searching Annoy index using 1 thread, search_k = 19600
10:25:16 Annoy recall = 100%
10:25:30 Commencing smooth kNN distance calibration using 1 thread
10:25:57 Initializing from normalized Laplacian + noise
10:25:57 Commencing optimization for 500 epochs, with 256948 positive edges
10:26:14 Optimization finished
[1] "196 0.17"10:26:15 UMAP embedding parameters a = 1.352 b = 0.9704
10:26:15 Read 1203 rows and found 38 numeric columns
10:26:15 Using Annoy for neighbor search, n_neighbors = 196
10:26:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:26:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872e35cb9b
10:26:16 Searching Annoy index using 1 thread, search_k = 19600
10:26:17 Annoy recall = 100%
10:26:31 Commencing smooth kNN distance calibration using 1 thread
10:26:58 Initializing from normalized Laplacian + noise
10:26:58 Commencing optimization for 500 epochs, with 256948 positive edges
10:27:15 Optimization finished
[1] "196 0.18"10:27:16 UMAP embedding parameters a = 1.321 b = 0.9813
10:27:16 Read 1203 rows and found 38 numeric columns
10:27:16 Using Annoy for neighbor search, n_neighbors = 196
10:27:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:27:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8733e7edea
10:27:17 Searching Annoy index using 1 thread, search_k = 19600
10:27:18 Annoy recall = 100%
10:27:31 Commencing smooth kNN distance calibration using 1 thread
10:27:58 Initializing from normalized Laplacian + noise
10:27:59 Commencing optimization for 500 epochs, with 256948 positive edges
10:28:16 Optimization finished
[1] "196 0.19"10:28:17 UMAP embedding parameters a = 1.292 b = 0.9921
10:28:17 Read 1203 rows and found 38 numeric columns
10:28:17 Using Annoy for neighbor search, n_neighbors = 196
10:28:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:28:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c292b3b
10:28:18 Searching Annoy index using 1 thread, search_k = 19600
10:28:19 Annoy recall = 100%
10:28:32 Commencing smooth kNN distance calibration using 1 thread
10:28:59 Initializing from normalized Laplacian + noise
10:28:59 Commencing optimization for 500 epochs, with 256948 positive edges
10:29:17 Optimization finished
[1] "196 0.2"10:29:17 UMAP embedding parameters a = 1.262 b = 1.003
10:29:17 Read 1203 rows and found 38 numeric columns
10:29:18 Using Annoy for neighbor search, n_neighbors = 196
10:29:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:29:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871a5c11b5
10:29:19 Searching Annoy index using 1 thread, search_k = 19600
10:29:20 Annoy recall = 100%
10:29:33 Commencing smooth kNN distance calibration using 1 thread
10:30:00 Initializing from normalized Laplacian + noise
10:30:00 Commencing optimization for 500 epochs, with 256948 positive edges
10:30:18 Optimization finished
[1] "197 0"10:30:18 UMAP embedding parameters a = 1.933 b = 0.7905
10:30:18 Read 1203 rows and found 38 numeric columns
10:30:18 Using Annoy for neighbor search, n_neighbors = 197
10:30:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:30:20 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b73ba2
10:30:20 Searching Annoy index using 1 thread, search_k = 19700
10:30:21 Annoy recall = 100%
10:30:34 Commencing smooth kNN distance calibration using 1 thread
10:31:01 Initializing from normalized Laplacian + noise
10:31:01 Commencing optimization for 500 epochs, with 258076 positive edges
10:31:19 Optimization finished
[1] "197 0.01"10:31:19 UMAP embedding parameters a = 1.896 b = 0.8006
10:31:20 Read 1203 rows and found 38 numeric columns
10:31:20 Using Annoy for neighbor search, n_neighbors = 197
10:31:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:31:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8778b09529
10:31:21 Searching Annoy index using 1 thread, search_k = 19700
10:31:22 Annoy recall = 100%
10:31:35 Commencing smooth kNN distance calibration using 1 thread
10:32:02 Initializing from normalized Laplacian + noise
10:32:02 Commencing optimization for 500 epochs, with 258076 positive edges
10:32:20 Optimization finished
[1] "197 0.02"10:32:20 UMAP embedding parameters a = 1.859 b = 0.8109
10:32:21 Read 1203 rows and found 38 numeric columns
10:32:21 Using Annoy for neighbor search, n_neighbors = 197
10:32:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:32:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877217d408
10:32:22 Searching Annoy index using 1 thread, search_k = 19700
10:32:23 Annoy recall = 100%
10:32:36 Commencing smooth kNN distance calibration using 1 thread
10:33:03 Initializing from normalized Laplacian + noise
10:33:03 Commencing optimization for 500 epochs, with 258076 positive edges
10:33:21 Optimization finished
[1] "197 0.03"10:33:21 UMAP embedding parameters a = 1.822 b = 0.8212
10:33:21 Read 1203 rows and found 38 numeric columns
10:33:21 Using Annoy for neighbor search, n_neighbors = 197
10:33:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:33:23 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8713a160b2
10:33:23 Searching Annoy index using 1 thread, search_k = 19700
10:33:24 Annoy recall = 100%
10:33:37 Commencing smooth kNN distance calibration using 1 thread
10:34:04 Initializing from normalized Laplacian + noise
10:34:04 Commencing optimization for 500 epochs, with 258076 positive edges
10:34:22 Optimization finished
[1] "197 0.04"10:34:22 UMAP embedding parameters a = 1.786 b = 0.8316
10:34:22 Read 1203 rows and found 38 numeric columns
10:34:22 Using Annoy for neighbor search, n_neighbors = 197
10:34:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:34:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874332c6a4
10:34:24 Searching Annoy index using 1 thread, search_k = 19700
10:34:25 Annoy recall = 100%
10:34:38 Commencing smooth kNN distance calibration using 1 thread
10:35:05 Initializing from normalized Laplacian + noise
10:35:05 Commencing optimization for 500 epochs, with 258076 positive edges
10:35:23 Optimization finished
[1] "197 0.05"10:35:23 UMAP embedding parameters a = 1.75 b = 0.8421
10:35:23 Read 1203 rows and found 38 numeric columns
10:35:23 Using Annoy for neighbor search, n_neighbors = 197
10:35:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:35:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8721b45adc
10:35:24 Searching Annoy index using 1 thread, search_k = 19700
10:35:26 Annoy recall = 100%
10:35:39 Commencing smooth kNN distance calibration using 1 thread
10:36:06 Initializing from normalized Laplacian + noise
10:36:06 Commencing optimization for 500 epochs, with 258076 positive edges
10:36:24 Optimization finished
[1] "197 0.06"10:36:24 UMAP embedding parameters a = 1.715 b = 0.8526
10:36:24 Read 1203 rows and found 38 numeric columns
10:36:24 Using Annoy for neighbor search, n_neighbors = 197
10:36:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:36:25 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8783c8d32
10:36:25 Searching Annoy index using 1 thread, search_k = 19700
10:36:27 Annoy recall = 100%
10:36:40 Commencing smooth kNN distance calibration using 1 thread
10:37:07 Initializing from normalized Laplacian + noise
10:37:07 Commencing optimization for 500 epochs, with 258076 positive edges
10:37:25 Optimization finished
[1] "197 0.07"10:37:25 UMAP embedding parameters a = 1.68 b = 0.8631
10:37:25 Read 1203 rows and found 38 numeric columns
10:37:25 Using Annoy for neighbor search, n_neighbors = 197
10:37:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:37:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875068220d
10:37:26 Searching Annoy index using 1 thread, search_k = 19700
10:37:28 Annoy recall = 100%
10:37:41 Commencing smooth kNN distance calibration using 1 thread
10:38:08 Initializing from normalized Laplacian + noise
10:38:08 Commencing optimization for 500 epochs, with 258076 positive edges
10:38:26 Optimization finished
[1] "197 0.08"10:38:26 UMAP embedding parameters a = 1.645 b = 0.8737
10:38:26 Read 1203 rows and found 38 numeric columns
10:38:26 Using Annoy for neighbor search, n_neighbors = 197
10:38:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:38:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874bcfa572
10:38:27 Searching Annoy index using 1 thread, search_k = 19700
10:38:29 Annoy recall = 100%
10:38:42 Commencing smooth kNN distance calibration using 1 thread
10:39:09 Initializing from normalized Laplacian + noise
10:39:09 Commencing optimization for 500 epochs, with 258076 positive edges
10:39:27 Optimization finished
[1] "197 0.09"10:39:27 UMAP embedding parameters a = 1.611 b = 0.8844
10:39:27 Read 1203 rows and found 38 numeric columns
10:39:27 Using Annoy for neighbor search, n_neighbors = 197
10:39:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:39:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87466aa7cd
10:39:29 Searching Annoy index using 1 thread, search_k = 19700
10:39:30 Annoy recall = 100%
10:39:43 Commencing smooth kNN distance calibration using 1 thread
10:40:10 Initializing from normalized Laplacian + noise
10:40:10 Commencing optimization for 500 epochs, with 258076 positive edges
10:40:28 Optimization finished
[1] "197 0.1"10:40:28 UMAP embedding parameters a = 1.577 b = 0.8951
10:40:28 Read 1203 rows and found 38 numeric columns
10:40:28 Using Annoy for neighbor search, n_neighbors = 197
10:40:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:40:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c024950
10:40:30 Searching Annoy index using 1 thread, search_k = 19700
10:40:31 Annoy recall = 100%
10:40:44 Commencing smooth kNN distance calibration using 1 thread
10:41:11 Initializing from normalized Laplacian + noise
10:41:11 Commencing optimization for 500 epochs, with 258076 positive edges
10:41:29 Optimization finished
[1] "197 0.11"10:41:29 UMAP embedding parameters a = 1.544 b = 0.9058
10:41:29 Read 1203 rows and found 38 numeric columns
10:41:29 Using Annoy for neighbor search, n_neighbors = 197
10:41:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:41:31 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874aa6c8a7
10:41:31 Searching Annoy index using 1 thread, search_k = 19700
10:41:32 Annoy recall = 100%
10:41:45 Commencing smooth kNN distance calibration using 1 thread
10:42:12 Initializing from normalized Laplacian + noise
10:42:12 Commencing optimization for 500 epochs, with 258076 positive edges
10:42:30 Optimization finished
[1] "197 0.12"10:42:30 UMAP embedding parameters a = 1.51 b = 0.9165
10:42:31 Read 1203 rows and found 38 numeric columns
10:42:31 Using Annoy for neighbor search, n_neighbors = 197
10:42:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:42:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871bd4d727
10:42:32 Searching Annoy index using 1 thread, search_k = 19700
10:42:33 Annoy recall = 100%
10:42:46 Commencing smooth kNN distance calibration using 1 thread
10:43:13 Initializing from normalized Laplacian + noise
10:43:13 Commencing optimization for 500 epochs, with 258076 positive edges
10:43:31 Optimization finished
[1] "197 0.13"10:43:32 UMAP embedding parameters a = 1.478 b = 0.9272
10:43:32 Read 1203 rows and found 38 numeric columns
10:43:32 Using Annoy for neighbor search, n_neighbors = 197
10:43:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:43:33 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b62f96
10:43:33 Searching Annoy index using 1 thread, search_k = 19700
10:43:34 Annoy recall = 100%
10:43:48 Commencing smooth kNN distance calibration using 1 thread
10:44:14 Initializing from normalized Laplacian + noise
10:44:15 Commencing optimization for 500 epochs, with 258076 positive edges
10:44:32 Optimization finished
[1] "197 0.14"10:44:33 UMAP embedding parameters a = 1.446 b = 0.938
10:44:33 Read 1203 rows and found 38 numeric columns
10:44:33 Using Annoy for neighbor search, n_neighbors = 197
10:44:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:44:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873efcc1b9
10:44:34 Searching Annoy index using 1 thread, search_k = 19700
10:44:35 Annoy recall = 100%
10:44:49 Commencing smooth kNN distance calibration using 1 thread
10:45:16 Initializing from normalized Laplacian + noise
10:45:16 Commencing optimization for 500 epochs, with 258076 positive edges
10:45:34 Optimization finished
[1] "197 0.15"10:45:34 UMAP embedding parameters a = 1.414 b = 0.9488
10:45:34 Read 1203 rows and found 38 numeric columns
10:45:34 Using Annoy for neighbor search, n_neighbors = 197
10:45:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:45:35 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874d53a2e8
10:45:35 Searching Annoy index using 1 thread, search_k = 19700
10:45:36 Annoy recall = 100%
10:45:50 Commencing smooth kNN distance calibration using 1 thread
10:46:17 Initializing from normalized Laplacian + noise
10:46:17 Commencing optimization for 500 epochs, with 258076 positive edges
10:46:35 Optimization finished
[1] "197 0.16"10:46:35 UMAP embedding parameters a = 1.383 b = 0.9596
10:46:35 Read 1203 rows and found 38 numeric columns
10:46:35 Using Annoy for neighbor search, n_neighbors = 197
10:46:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:46:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87287efef0
10:46:36 Searching Annoy index using 1 thread, search_k = 19700
10:46:37 Annoy recall = 100%
10:46:51 Commencing smooth kNN distance calibration using 1 thread
10:47:18 Initializing from normalized Laplacian + noise
10:47:18 Commencing optimization for 500 epochs, with 258076 positive edges
10:47:36 Optimization finished
[1] "197 0.17"10:47:36 UMAP embedding parameters a = 1.352 b = 0.9704
10:47:36 Read 1203 rows and found 38 numeric columns
10:47:36 Using Annoy for neighbor search, n_neighbors = 197
10:47:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:47:37 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872cd63281
10:47:37 Searching Annoy index using 1 thread, search_k = 19700
10:47:39 Annoy recall = 100%
10:47:52 Commencing smooth kNN distance calibration using 1 thread
10:48:19 Initializing from normalized Laplacian + noise
10:48:20 Commencing optimization for 500 epochs, with 258076 positive edges
10:48:38 Optimization finished
[1] "197 0.18"10:48:38 UMAP embedding parameters a = 1.321 b = 0.9813
10:48:38 Read 1203 rows and found 38 numeric columns
10:48:38 Using Annoy for neighbor search, n_neighbors = 197
10:48:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:48:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8776329e2
10:48:39 Searching Annoy index using 1 thread, search_k = 19700
10:48:40 Annoy recall = 100%
10:48:54 Commencing smooth kNN distance calibration using 1 thread
10:49:22 Initializing from normalized Laplacian + noise
10:49:22 Commencing optimization for 500 epochs, with 258076 positive edges
10:49:41 Optimization finished
[1] "197 0.19"10:49:41 UMAP embedding parameters a = 1.292 b = 0.9921
10:49:41 Read 1203 rows and found 38 numeric columns
10:49:41 Using Annoy for neighbor search, n_neighbors = 197
10:49:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:49:42 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b551dac
10:49:42 Searching Annoy index using 1 thread, search_k = 19700
10:49:43 Annoy recall = 100%
10:49:57 Commencing smooth kNN distance calibration using 1 thread
10:50:25 Initializing from normalized Laplacian + noise
10:50:25 Commencing optimization for 500 epochs, with 258076 positive edges
10:50:43 Optimization finished
[1] "197 0.2"10:50:44 UMAP embedding parameters a = 1.262 b = 1.003
10:50:44 Read 1203 rows and found 38 numeric columns
10:50:44 Using Annoy for neighbor search, n_neighbors = 197
10:50:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:50:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872eccc932
10:50:45 Searching Annoy index using 1 thread, search_k = 19700
10:50:46 Annoy recall = 100%
10:51:00 Commencing smooth kNN distance calibration using 1 thread
10:51:28 Initializing from normalized Laplacian + noise
10:51:28 Commencing optimization for 500 epochs, with 258076 positive edges
10:51:46 Optimization finished
[1] "198 0"10:51:46 UMAP embedding parameters a = 1.933 b = 0.7905
10:51:46 Read 1203 rows and found 38 numeric columns
10:51:47 Using Annoy for neighbor search, n_neighbors = 198
10:51:47 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:51:48 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873d32f6e2
10:51:48 Searching Annoy index using 1 thread, search_k = 19800
10:51:49 Annoy recall = 100%
10:52:03 Commencing smooth kNN distance calibration using 1 thread
10:52:31 Initializing from normalized Laplacian + noise
10:52:31 Commencing optimization for 500 epochs, with 259232 positive edges
10:52:49 Optimization finished
[1] "198 0.01"10:52:49 UMAP embedding parameters a = 1.896 b = 0.8006
10:52:49 Read 1203 rows and found 38 numeric columns
10:52:49 Using Annoy for neighbor search, n_neighbors = 198
10:52:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:52:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876ad39c21
10:52:50 Searching Annoy index using 1 thread, search_k = 19800
10:52:52 Annoy recall = 100%
10:53:06 Commencing smooth kNN distance calibration using 1 thread
10:53:33 Initializing from normalized Laplacian + noise
10:53:34 Commencing optimization for 500 epochs, with 259232 positive edges
10:53:52 Optimization finished
[1] "198 0.02"10:53:52 UMAP embedding parameters a = 1.859 b = 0.8109
10:53:52 Read 1203 rows and found 38 numeric columns
10:53:52 Using Annoy for neighbor search, n_neighbors = 198
10:53:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:53:53 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87b06e2b3
10:53:54 Searching Annoy index using 1 thread, search_k = 19800
10:53:55 Annoy recall = 100%
10:54:08 Commencing smooth kNN distance calibration using 1 thread
10:54:36 Initializing from normalized Laplacian + noise
10:54:36 Commencing optimization for 500 epochs, with 259232 positive edges
10:54:54 Optimization finished
[1] "198 0.03"10:54:55 UMAP embedding parameters a = 1.822 b = 0.8212
10:54:55 Read 1203 rows and found 38 numeric columns
10:54:55 Using Annoy for neighbor search, n_neighbors = 198
10:54:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:54:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87205983a8
10:54:56 Searching Annoy index using 1 thread, search_k = 19800
10:54:57 Annoy recall = 100%
10:55:11 Commencing smooth kNN distance calibration using 1 thread
10:55:38 Initializing from normalized Laplacian + noise
10:55:38 Commencing optimization for 500 epochs, with 259232 positive edges
10:55:56 Optimization finished
[1] "198 0.04"10:55:57 UMAP embedding parameters a = 1.786 b = 0.8316
10:55:57 Read 1203 rows and found 38 numeric columns
10:55:57 Using Annoy for neighbor search, n_neighbors = 198
10:55:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:55:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875c4ec6ed
10:55:58 Searching Annoy index using 1 thread, search_k = 19800
10:55:59 Annoy recall = 100%
10:56:13 Commencing smooth kNN distance calibration using 1 thread
10:56:40 Initializing from normalized Laplacian + noise
10:56:40 Commencing optimization for 500 epochs, with 259232 positive edges
10:56:59 Optimization finished
[1] "198 0.05"10:56:59 UMAP embedding parameters a = 1.75 b = 0.8421
10:56:59 Read 1203 rows and found 38 numeric columns
10:56:59 Using Annoy for neighbor search, n_neighbors = 198
10:56:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:57:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872a661475
10:57:00 Searching Annoy index using 1 thread, search_k = 19800
10:57:01 Annoy recall = 100%
10:57:15 Commencing smooth kNN distance calibration using 1 thread
10:57:43 Initializing from normalized Laplacian + noise
10:57:43 Commencing optimization for 500 epochs, with 259232 positive edges
10:58:01 Optimization finished
[1] "198 0.06"10:58:01 UMAP embedding parameters a = 1.715 b = 0.8526
10:58:01 Read 1203 rows and found 38 numeric columns
10:58:01 Using Annoy for neighbor search, n_neighbors = 198
10:58:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:58:02 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874e8f4f43
10:58:03 Searching Annoy index using 1 thread, search_k = 19800
10:58:04 Annoy recall = 100%
10:58:17 Commencing smooth kNN distance calibration using 1 thread
10:58:45 Initializing from normalized Laplacian + noise
10:58:45 Commencing optimization for 500 epochs, with 259232 positive edges
10:59:03 Optimization finished
[1] "198 0.07"10:59:04 UMAP embedding parameters a = 1.68 b = 0.8631
10:59:04 Read 1203 rows and found 38 numeric columns
10:59:04 Using Annoy for neighbor search, n_neighbors = 198
10:59:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:59:05 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871036b4d7
10:59:05 Searching Annoy index using 1 thread, search_k = 19800
10:59:06 Annoy recall = 100%
10:59:20 Commencing smooth kNN distance calibration using 1 thread
10:59:47 Initializing from normalized Laplacian + noise
10:59:48 Commencing optimization for 500 epochs, with 259232 positive edges
11:00:06 Optimization finished
[1] "198 0.08"11:00:06 UMAP embedding parameters a = 1.645 b = 0.8737
11:00:06 Read 1203 rows and found 38 numeric columns
11:00:06 Using Annoy for neighbor search, n_neighbors = 198
11:00:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:00:07 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768f3fb0
11:00:07 Searching Annoy index using 1 thread, search_k = 19800
11:00:09 Annoy recall = 100%
11:00:22 Commencing smooth kNN distance calibration using 1 thread
11:00:50 Initializing from normalized Laplacian + noise
11:00:50 Commencing optimization for 500 epochs, with 259232 positive edges
11:01:08 Optimization finished
[1] "198 0.09"11:01:08 UMAP embedding parameters a = 1.611 b = 0.8844
11:01:08 Read 1203 rows and found 38 numeric columns
11:01:08 Using Annoy for neighbor search, n_neighbors = 198
11:01:08 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:01:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768eb60f8
11:01:09 Searching Annoy index using 1 thread, search_k = 19800
11:01:11 Annoy recall = 100%
11:01:24 Commencing smooth kNN distance calibration using 1 thread
11:01:51 Initializing from normalized Laplacian + noise
11:01:51 Commencing optimization for 500 epochs, with 259232 positive edges
11:02:10 Optimization finished
[1] "198 0.1"11:02:10 UMAP embedding parameters a = 1.577 b = 0.8951
11:02:10 Read 1203 rows and found 38 numeric columns
11:02:10 Using Annoy for neighbor search, n_neighbors = 198
11:02:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:02:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8715edf079
11:02:11 Searching Annoy index using 1 thread, search_k = 19800
11:02:12 Annoy recall = 100%
11:02:26 Commencing smooth kNN distance calibration using 1 thread
11:02:53 Initializing from normalized Laplacian + noise
11:02:53 Commencing optimization for 500 epochs, with 259232 positive edges
11:03:11 Optimization finished
[1] "198 0.11"11:03:11 UMAP embedding parameters a = 1.544 b = 0.9058
11:03:11 Read 1203 rows and found 38 numeric columns
11:03:11 Using Annoy for neighbor search, n_neighbors = 198
11:03:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:03:13 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f3fd4d9
11:03:13 Searching Annoy index using 1 thread, search_k = 19800
11:03:14 Annoy recall = 100%
11:03:27 Commencing smooth kNN distance calibration using 1 thread
11:03:55 Initializing from normalized Laplacian + noise
11:03:55 Commencing optimization for 500 epochs, with 259232 positive edges
11:04:13 Optimization finished
[1] "198 0.12"11:04:13 UMAP embedding parameters a = 1.51 b = 0.9165
11:04:13 Read 1203 rows and found 38 numeric columns
11:04:13 Using Annoy for neighbor search, n_neighbors = 198
11:04:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:04:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875b033501
11:04:14 Searching Annoy index using 1 thread, search_k = 19800
11:04:16 Annoy recall = 100%
11:04:29 Commencing smooth kNN distance calibration using 1 thread
11:04:56 Initializing from normalized Laplacian + noise
11:04:56 Commencing optimization for 500 epochs, with 259232 positive edges
11:05:14 Optimization finished
[1] "198 0.13"11:05:15 UMAP embedding parameters a = 1.478 b = 0.9272
11:05:15 Read 1203 rows and found 38 numeric columns
11:05:15 Using Annoy for neighbor search, n_neighbors = 198
11:05:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:05:16 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87298f512b
11:05:16 Searching Annoy index using 1 thread, search_k = 19800
11:05:17 Annoy recall = 100%
11:05:31 Commencing smooth kNN distance calibration using 1 thread
11:05:58 Initializing from normalized Laplacian + noise
11:05:58 Commencing optimization for 500 epochs, with 259232 positive edges
11:06:16 Optimization finished
[1] "198 0.14"11:06:16 UMAP embedding parameters a = 1.446 b = 0.938
11:06:16 Read 1203 rows and found 38 numeric columns
11:06:16 Using Annoy for neighbor search, n_neighbors = 198
11:06:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:06:17 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8742729b7d
11:06:17 Searching Annoy index using 1 thread, search_k = 19800
11:06:19 Annoy recall = 100%
11:06:32 Commencing smooth kNN distance calibration using 1 thread
11:06:59 Initializing from normalized Laplacian + noise
11:07:00 Commencing optimization for 500 epochs, with 259232 positive edges
11:07:18 Optimization finished
[1] "198 0.15"11:07:18 UMAP embedding parameters a = 1.414 b = 0.9488
11:07:18 Read 1203 rows and found 38 numeric columns
11:07:18 Using Annoy for neighbor search, n_neighbors = 198
11:07:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:07:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877cb78fdd
11:07:19 Searching Annoy index using 1 thread, search_k = 19800
11:07:20 Annoy recall = 100%
11:07:34 Commencing smooth kNN distance calibration using 1 thread
11:08:01 Initializing from normalized Laplacian + noise
11:08:01 Commencing optimization for 500 epochs, with 259232 positive edges
11:08:19 Optimization finished
[1] "198 0.16"11:08:20 UMAP embedding parameters a = 1.383 b = 0.9596
11:08:20 Read 1203 rows and found 38 numeric columns
11:08:20 Using Annoy for neighbor search, n_neighbors = 198
11:08:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:08:21 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731cbde5d
11:08:21 Searching Annoy index using 1 thread, search_k = 19800
11:08:22 Annoy recall = 100%
11:08:36 Commencing smooth kNN distance calibration using 1 thread
11:09:03 Initializing from normalized Laplacian + noise
11:09:03 Commencing optimization for 500 epochs, with 259232 positive edges
11:09:21 Optimization finished
[1] "198 0.17"11:09:21 UMAP embedding parameters a = 1.352 b = 0.9704
11:09:21 Read 1203 rows and found 38 numeric columns
11:09:21 Using Annoy for neighbor search, n_neighbors = 198
11:09:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:09:22 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8712dabd8a
11:09:22 Searching Annoy index using 1 thread, search_k = 19800
11:09:24 Annoy recall = 100%
11:09:37 Commencing smooth kNN distance calibration using 1 thread
11:10:05 Initializing from normalized Laplacian + noise
11:10:05 Commencing optimization for 500 epochs, with 259232 positive edges
11:10:24 Optimization finished
[1] "198 0.18"11:10:25 UMAP embedding parameters a = 1.321 b = 0.9813
11:10:25 Read 1203 rows and found 38 numeric columns
11:10:25 Using Annoy for neighbor search, n_neighbors = 198
11:10:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:10:26 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f874887354f
11:10:26 Searching Annoy index using 1 thread, search_k = 19800
11:10:27 Annoy recall = 100%
11:10:44 Commencing smooth kNN distance calibration using 1 thread
11:11:15 Initializing from normalized Laplacian + noise
11:11:15 Commencing optimization for 500 epochs, with 259232 positive edges
11:11:35 Optimization finished
[1] "198 0.19"11:11:35 UMAP embedding parameters a = 1.292 b = 0.9921
11:11:35 Read 1203 rows and found 38 numeric columns
11:11:35 Using Annoy for neighbor search, n_neighbors = 198
11:11:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:11:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877836862a
11:11:36 Searching Annoy index using 1 thread, search_k = 19800
11:11:38 Annoy recall = 100%
11:11:53 Commencing smooth kNN distance calibration using 1 thread
11:12:21 Initializing from normalized Laplacian + noise
11:12:21 Commencing optimization for 500 epochs, with 259232 positive edges
11:12:40 Optimization finished
[1] "198 0.2"11:12:40 UMAP embedding parameters a = 1.262 b = 1.003
11:12:40 Read 1203 rows and found 38 numeric columns
11:12:40 Using Annoy for neighbor search, n_neighbors = 198
11:12:40 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:12:41 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876edd06db
11:12:41 Searching Annoy index using 1 thread, search_k = 19800
11:12:43 Annoy recall = 100%
11:12:56 Commencing smooth kNN distance calibration using 1 thread
11:13:24 Initializing from normalized Laplacian + noise
11:13:24 Commencing optimization for 500 epochs, with 259232 positive edges
11:13:43 Optimization finished
[1] "199 0"11:13:43 UMAP embedding parameters a = 1.933 b = 0.7905
11:13:43 Read 1203 rows and found 38 numeric columns
11:13:43 Using Annoy for neighbor search, n_neighbors = 199
11:13:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:13:45 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87132dfdf7
11:13:45 Searching Annoy index using 1 thread, search_k = 19900
11:13:46 Annoy recall = 100%
11:14:00 Commencing smooth kNN distance calibration using 1 thread
11:14:28 Initializing from normalized Laplacian + noise
11:14:29 Commencing optimization for 500 epochs, with 260394 positive edges
11:14:48 Optimization finished
[1] "199 0.01"11:14:48 UMAP embedding parameters a = 1.896 b = 0.8006
11:14:48 Read 1203 rows and found 38 numeric columns
11:14:48 Using Annoy for neighbor search, n_neighbors = 199
11:14:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:14:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87140b5d51
11:14:49 Searching Annoy index using 1 thread, search_k = 19900
11:14:50 Annoy recall = 100%
11:15:04 Commencing smooth kNN distance calibration using 1 thread
11:15:36 Initializing from normalized Laplacian + noise
11:15:36 Commencing optimization for 500 epochs, with 260394 positive edges
11:15:56 Optimization finished
[1] "199 0.02"11:15:56 UMAP embedding parameters a = 1.859 b = 0.8109
11:15:56 Read 1203 rows and found 38 numeric columns
11:15:56 Using Annoy for neighbor search, n_neighbors = 199
11:15:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:15:57 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876f933671
11:15:57 Searching Annoy index using 1 thread, search_k = 19900
11:15:59 Annoy recall = 100%
11:16:14 Commencing smooth kNN distance calibration using 1 thread
11:16:42 Initializing from normalized Laplacian + noise
11:16:42 Commencing optimization for 500 epochs, with 260394 positive edges
11:17:01 Optimization finished
[1] "199 0.03"11:17:01 UMAP embedding parameters a = 1.822 b = 0.8212
11:17:01 Read 1203 rows and found 38 numeric columns
11:17:01 Using Annoy for neighbor search, n_neighbors = 199
11:17:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:17:03 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87522abfb0
11:17:03 Searching Annoy index using 1 thread, search_k = 19900
11:17:04 Annoy recall = 100%
11:17:19 Commencing smooth kNN distance calibration using 1 thread
11:17:50 Initializing from normalized Laplacian + noise
11:17:50 Commencing optimization for 500 epochs, with 260394 positive edges
11:18:10 Optimization finished
[1] "199 0.04"11:18:10 UMAP embedding parameters a = 1.786 b = 0.8316
11:18:10 Read 1203 rows and found 38 numeric columns
11:18:10 Using Annoy for neighbor search, n_neighbors = 199
11:18:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:18:11 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87615f0039
11:18:11 Searching Annoy index using 1 thread, search_k = 19900
11:18:12 Annoy recall = 100%
11:18:26 Commencing smooth kNN distance calibration using 1 thread
11:18:55 Initializing from normalized Laplacian + noise
11:18:55 Commencing optimization for 500 epochs, with 260394 positive edges
11:19:13 Optimization finished
[1] "199 0.05"11:19:13 UMAP embedding parameters a = 1.75 b = 0.8421
11:19:13 Read 1203 rows and found 38 numeric columns
11:19:13 Using Annoy for neighbor search, n_neighbors = 199
11:19:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:19:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8718123561
11:19:14 Searching Annoy index using 1 thread, search_k = 19900
11:19:15 Annoy recall = 100%
11:19:29 Commencing smooth kNN distance calibration using 1 thread
11:19:57 Initializing from normalized Laplacian + noise
11:19:57 Commencing optimization for 500 epochs, with 260394 positive edges
11:20:17 Optimization finished
[1] "199 0.06"11:20:17 UMAP embedding parameters a = 1.715 b = 0.8526
11:20:17 Read 1203 rows and found 38 numeric columns
11:20:17 Using Annoy for neighbor search, n_neighbors = 199
11:20:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:20:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877f00f231
11:20:19 Searching Annoy index using 1 thread, search_k = 19900
11:20:20 Annoy recall = 100%
11:20:36 Commencing smooth kNN distance calibration using 1 thread
11:21:07 Initializing from normalized Laplacian + noise
11:21:07 Commencing optimization for 500 epochs, with 260394 positive edges
11:21:26 Optimization finished
[1] "199 0.07"11:21:26 UMAP embedding parameters a = 1.68 b = 0.8631
11:21:26 Read 1203 rows and found 38 numeric columns
11:21:26 Using Annoy for neighbor search, n_neighbors = 199
11:21:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:21:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8768c22a1b
11:21:28 Searching Annoy index using 1 thread, search_k = 19900
11:21:29 Annoy recall = 100%
11:21:44 Commencing smooth kNN distance calibration using 1 thread
11:22:12 Initializing from normalized Laplacian + noise
11:22:12 Commencing optimization for 500 epochs, with 260394 positive edges
11:22:31 Optimization finished
[1] "199 0.08"11:22:31 UMAP embedding parameters a = 1.645 b = 0.8737
11:22:31 Read 1203 rows and found 38 numeric columns
11:22:31 Using Annoy for neighbor search, n_neighbors = 199
11:22:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:22:32 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872367530d
11:22:32 Searching Annoy index using 1 thread, search_k = 19900
11:22:34 Annoy recall = 100%
11:22:48 Commencing smooth kNN distance calibration using 1 thread
11:23:16 Initializing from normalized Laplacian + noise
11:23:16 Commencing optimization for 500 epochs, with 260394 positive edges
11:23:35 Optimization finished
[1] "199 0.09"11:23:35 UMAP embedding parameters a = 1.611 b = 0.8844
11:23:35 Read 1203 rows and found 38 numeric columns
11:23:35 Using Annoy for neighbor search, n_neighbors = 199
11:23:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:23:36 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872dcdbb64
11:23:36 Searching Annoy index using 1 thread, search_k = 19900
11:23:37 Annoy recall = 100%
11:23:51 Commencing smooth kNN distance calibration using 1 thread
11:24:20 Initializing from normalized Laplacian + noise
11:24:20 Commencing optimization for 500 epochs, with 260394 positive edges
11:24:38 Optimization finished
[1] "199 0.1"11:24:39 UMAP embedding parameters a = 1.577 b = 0.8951
11:24:39 Read 1203 rows and found 38 numeric columns
11:24:39 Using Annoy for neighbor search, n_neighbors = 199
11:24:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:24:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8725f520fe
11:24:40 Searching Annoy index using 1 thread, search_k = 19900
11:24:41 Annoy recall = 100%
11:24:55 Commencing smooth kNN distance calibration using 1 thread
11:25:24 Initializing from normalized Laplacian + noise
11:25:24 Commencing optimization for 500 epochs, with 260394 positive edges
11:25:43 Optimization finished
[1] "199 0.11"11:25:43 UMAP embedding parameters a = 1.544 b = 0.9058
11:25:43 Read 1203 rows and found 38 numeric columns
11:25:43 Using Annoy for neighbor search, n_neighbors = 199
11:25:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:25:44 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87e3aef2f
11:25:44 Searching Annoy index using 1 thread, search_k = 19900
11:25:45 Annoy recall = 100%
11:25:59 Commencing smooth kNN distance calibration using 1 thread
11:26:29 Initializing from normalized Laplacian + noise
11:26:29 Commencing optimization for 500 epochs, with 260394 positive edges
11:26:50 Optimization finished
[1] "199 0.12"11:26:50 UMAP embedding parameters a = 1.51 b = 0.9165
11:26:50 Read 1203 rows and found 38 numeric columns
11:26:50 Using Annoy for neighbor search, n_neighbors = 199
11:26:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:26:52 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8738d49e17
11:26:52 Searching Annoy index using 1 thread, search_k = 19900
11:26:53 Annoy recall = 100%
11:27:08 Commencing smooth kNN distance calibration using 1 thread
11:27:37 Initializing from normalized Laplacian + noise
11:27:37 Commencing optimization for 500 epochs, with 260394 positive edges
11:27:57 Optimization finished
[1] "199 0.13"11:27:57 UMAP embedding parameters a = 1.478 b = 0.9272
11:27:57 Read 1203 rows and found 38 numeric columns
11:27:57 Using Annoy for neighbor search, n_neighbors = 199
11:27:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:27:58 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87464ea4a6
11:27:58 Searching Annoy index using 1 thread, search_k = 19900
11:28:00 Annoy recall = 100%
11:28:17 Commencing smooth kNN distance calibration using 1 thread
11:28:47 Initializing from normalized Laplacian + noise
11:28:48 Commencing optimization for 500 epochs, with 260394 positive edges
11:29:07 Optimization finished
[1] "199 0.14"11:29:07 UMAP embedding parameters a = 1.446 b = 0.938
11:29:07 Read 1203 rows and found 38 numeric columns
11:29:07 Using Annoy for neighbor search, n_neighbors = 199
11:29:07 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:29:09 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876a89b61c
11:29:09 Searching Annoy index using 1 thread, search_k = 19900
11:29:10 Annoy recall = 100%
11:29:25 Commencing smooth kNN distance calibration using 1 thread
11:29:53 Initializing from normalized Laplacian + noise
11:29:53 Commencing optimization for 500 epochs, with 260394 positive edges
11:30:12 Optimization finished
[1] "199 0.15"11:30:13 UMAP embedding parameters a = 1.414 b = 0.9488
11:30:13 Read 1203 rows and found 38 numeric columns
11:30:13 Using Annoy for neighbor search, n_neighbors = 199
11:30:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:30:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87633ab28c
11:30:14 Searching Annoy index using 1 thread, search_k = 19900
11:30:15 Annoy recall = 100%
11:30:29 Commencing smooth kNN distance calibration using 1 thread
11:30:57 Initializing from normalized Laplacian + noise
11:30:57 Commencing optimization for 500 epochs, with 260394 positive edges
11:31:15 Optimization finished
[1] "199 0.16"11:31:16 UMAP embedding parameters a = 1.383 b = 0.9596
11:31:16 Read 1203 rows and found 38 numeric columns
11:31:16 Using Annoy for neighbor search, n_neighbors = 199
11:31:16 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:31:18 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8714ddf3ea
11:31:18 Searching Annoy index using 1 thread, search_k = 19900
11:31:19 Annoy recall = 100%
11:31:35 Commencing smooth kNN distance calibration using 1 thread
11:32:08 Initializing from normalized Laplacian + noise
11:32:08 Commencing optimization for 500 epochs, with 260394 positive edges
11:32:28 Optimization finished
[1] "199 0.17"11:32:28 UMAP embedding parameters a = 1.352 b = 0.9704
11:32:28 Read 1203 rows and found 38 numeric columns
11:32:28 Using Annoy for neighbor search, n_neighbors = 199
11:32:28 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:32:30 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877ac06af4
11:32:30 Searching Annoy index using 1 thread, search_k = 19900
11:32:31 Annoy recall = 100%
11:32:46 Commencing smooth kNN distance calibration using 1 thread
11:33:14 Initializing from normalized Laplacian + noise
11:33:14 Commencing optimization for 500 epochs, with 260394 positive edges
11:33:36 Optimization finished
[1] "199 0.18"11:33:36 UMAP embedding parameters a = 1.321 b = 0.9813
11:33:36 Read 1203 rows and found 38 numeric columns
11:33:36 Using Annoy for neighbor search, n_neighbors = 199
11:33:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:33:38 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8769c9f23c
11:33:38 Searching Annoy index using 1 thread, search_k = 19900
11:33:39 Annoy recall = 100%
11:33:57 Commencing smooth kNN distance calibration using 1 thread
11:34:28 Initializing from normalized Laplacian + noise
11:34:28 Commencing optimization for 500 epochs, with 260394 positive edges
11:34:49 Optimization finished
[1] "199 0.19"11:34:49 UMAP embedding parameters a = 1.292 b = 0.9921
11:34:49 Read 1203 rows and found 38 numeric columns
11:34:49 Using Annoy for neighbor search, n_neighbors = 199
11:34:50 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:34:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f877dc954e2
11:34:51 Searching Annoy index using 1 thread, search_k = 19900
11:34:52 Annoy recall = 100%
11:35:08 Commencing smooth kNN distance calibration using 1 thread
11:35:38 Initializing from normalized Laplacian + noise
11:35:38 Commencing optimization for 500 epochs, with 260394 positive edges
11:35:59 Optimization finished
[1] "199 0.2"11:35:59 UMAP embedding parameters a = 1.262 b = 1.003
11:35:59 Read 1203 rows and found 38 numeric columns
11:35:59 Using Annoy for neighbor search, n_neighbors = 199
11:35:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:36:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8710ae5b6d
11:36:01 Searching Annoy index using 1 thread, search_k = 19900
11:36:02 Annoy recall = 100%
11:36:19 Commencing smooth kNN distance calibration using 1 thread
11:36:52 Initializing from normalized Laplacian + noise
11:36:53 Commencing optimization for 500 epochs, with 260394 positive edges
11:37:13 Optimization finished
[1] "200 0"11:37:13 UMAP embedding parameters a = 1.933 b = 0.7905
11:37:13 Read 1203 rows and found 38 numeric columns
11:37:13 Using Annoy for neighbor search, n_neighbors = 200
11:37:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:37:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876909c715
11:37:14 Searching Annoy index using 1 thread, search_k = 20000
11:37:16 Annoy recall = 100%
11:37:30 Commencing smooth kNN distance calibration using 1 thread
11:37:59 Initializing from normalized Laplacian + noise
11:37:59 Commencing optimization for 500 epochs, with 261572 positive edges
11:38:17 Optimization finished
[1] "200 0.01"11:38:18 UMAP embedding parameters a = 1.896 b = 0.8006
11:38:18 Read 1203 rows and found 38 numeric columns
11:38:18 Using Annoy for neighbor search, n_neighbors = 200
11:38:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:38:19 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8758cc89e3
11:38:19 Searching Annoy index using 1 thread, search_k = 20000
11:38:21 Annoy recall = 100%
11:38:35 Commencing smooth kNN distance calibration using 1 thread
11:39:06 Initializing from normalized Laplacian + noise
11:39:06 Commencing optimization for 500 epochs, with 261572 positive edges
11:39:27 Optimization finished
[1] "200 0.02"11:39:27 UMAP embedding parameters a = 1.859 b = 0.8109
11:39:27 Read 1203 rows and found 38 numeric columns
11:39:27 Using Annoy for neighbor search, n_neighbors = 200
11:39:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:39:29 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873a3dac99
11:39:29 Searching Annoy index using 1 thread, search_k = 20000
11:39:30 Annoy recall = 100%
11:39:49 Commencing smooth kNN distance calibration using 1 thread
11:40:21 Initializing from normalized Laplacian + noise
11:40:21 Commencing optimization for 500 epochs, with 261572 positive edges
11:40:41 Optimization finished
[1] "200 0.03"11:40:41 UMAP embedding parameters a = 1.822 b = 0.8212
11:40:41 Read 1203 rows and found 38 numeric columns
11:40:41 Using Annoy for neighbor search, n_neighbors = 200
11:40:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:40:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872b7c6292
11:40:43 Searching Annoy index using 1 thread, search_k = 20000
11:40:44 Annoy recall = 100%
11:41:00 Commencing smooth kNN distance calibration using 1 thread
11:41:29 Initializing from normalized Laplacian + noise
11:41:29 Commencing optimization for 500 epochs, with 261572 positive edges
11:41:49 Optimization finished
[1] "200 0.04"11:41:49 UMAP embedding parameters a = 1.786 b = 0.8316
11:41:49 Read 1203 rows and found 38 numeric columns
11:41:49 Using Annoy for neighbor search, n_neighbors = 200
11:41:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:41:51 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87558419c0
11:41:51 Searching Annoy index using 1 thread, search_k = 20000
11:41:52 Annoy recall = 100%
11:42:07 Commencing smooth kNN distance calibration using 1 thread
11:42:36 Initializing from normalized Laplacian + noise
11:42:36 Commencing optimization for 500 epochs, with 261572 positive edges
11:42:55 Optimization finished
[1] "200 0.05"11:42:55 UMAP embedding parameters a = 1.75 b = 0.8421
11:42:55 Read 1203 rows and found 38 numeric columns
11:42:55 Using Annoy for neighbor search, n_neighbors = 200
11:42:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:42:56 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876c098af6
11:42:56 Searching Annoy index using 1 thread, search_k = 20000
11:42:57 Annoy recall = 100%
11:43:14 Commencing smooth kNN distance calibration using 1 thread
11:43:48 Initializing from normalized Laplacian + noise
11:43:48 Commencing optimization for 500 epochs, with 261572 positive edges
11:44:08 Optimization finished
[1] "200 0.06"11:44:09 UMAP embedding parameters a = 1.715 b = 0.8526
11:44:09 Read 1203 rows and found 38 numeric columns
11:44:09 Using Annoy for neighbor search, n_neighbors = 200
11:44:09 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:44:10 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873e57201d
11:44:10 Searching Annoy index using 1 thread, search_k = 20000
11:44:12 Annoy recall = 100%
11:44:29 Commencing smooth kNN distance calibration using 1 thread
11:45:03 Initializing from normalized Laplacian + noise
11:45:03 Commencing optimization for 500 epochs, with 261572 positive edges
11:45:23 Optimization finished
[1] "200 0.07"11:45:23 UMAP embedding parameters a = 1.68 b = 0.8631
11:45:23 Read 1203 rows and found 38 numeric columns
11:45:23 Using Annoy for neighbor search, n_neighbors = 200
11:45:23 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:45:24 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871e0b4f10
11:45:24 Searching Annoy index using 1 thread, search_k = 20000
11:45:26 Annoy recall = 100%
11:45:40 Commencing smooth kNN distance calibration using 1 thread
11:46:08 Initializing from normalized Laplacian + noise
11:46:08 Commencing optimization for 500 epochs, with 261572 positive edges
11:46:27 Optimization finished
[1] "200 0.08"11:46:27 UMAP embedding parameters a = 1.645 b = 0.8737
11:46:27 Read 1203 rows and found 38 numeric columns
11:46:27 Using Annoy for neighbor search, n_neighbors = 200
11:46:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:46:28 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8764401121
11:46:28 Searching Annoy index using 1 thread, search_k = 20000
11:46:30 Annoy recall = 100%
11:46:44 Commencing smooth kNN distance calibration using 1 thread
11:47:13 Initializing from normalized Laplacian + noise
11:47:13 Commencing optimization for 500 epochs, with 261572 positive edges
11:47:32 Optimization finished
[1] "200 0.09"11:47:33 UMAP embedding parameters a = 1.611 b = 0.8844
11:47:33 Read 1203 rows and found 38 numeric columns
11:47:33 Using Annoy for neighbor search, n_neighbors = 200
11:47:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:47:34 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f872d3426f8
11:47:34 Searching Annoy index using 1 thread, search_k = 20000
11:47:35 Annoy recall = 100%
11:47:49 Commencing smooth kNN distance calibration using 1 thread
11:48:18 Initializing from normalized Laplacian + noise
11:48:18 Commencing optimization for 500 epochs, with 261572 positive edges
11:48:38 Optimization finished
[1] "200 0.1"11:48:38 UMAP embedding parameters a = 1.577 b = 0.8951
11:48:38 Read 1203 rows and found 38 numeric columns
11:48:38 Using Annoy for neighbor search, n_neighbors = 200
11:48:38 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:48:39 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8731394d07
11:48:39 Searching Annoy index using 1 thread, search_k = 20000
11:48:40 Annoy recall = 100%
11:48:54 Commencing smooth kNN distance calibration using 1 thread
11:49:23 Initializing from normalized Laplacian + noise
11:49:23 Commencing optimization for 500 epochs, with 261572 positive edges
11:49:41 Optimization finished
[1] "200 0.11"11:49:42 UMAP embedding parameters a = 1.544 b = 0.9058
11:49:42 Read 1203 rows and found 38 numeric columns
11:49:42 Using Annoy for neighbor search, n_neighbors = 200
11:49:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:49:43 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87784b6e72
11:49:43 Searching Annoy index using 1 thread, search_k = 20000
11:49:44 Annoy recall = 100%
11:49:59 Commencing smooth kNN distance calibration using 1 thread
11:50:28 Initializing from normalized Laplacian + noise
11:50:28 Commencing optimization for 500 epochs, with 261572 positive edges
11:50:48 Optimization finished
[1] "200 0.12"11:50:48 UMAP embedding parameters a = 1.51 b = 0.9165
11:50:48 Read 1203 rows and found 38 numeric columns
11:50:48 Using Annoy for neighbor search, n_neighbors = 200
11:50:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:50:49 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f871cc75d69
11:50:49 Searching Annoy index using 1 thread, search_k = 20000
11:50:51 Annoy recall = 100%
11:51:05 Commencing smooth kNN distance calibration using 1 thread
11:51:34 Initializing from normalized Laplacian + noise
11:51:34 Commencing optimization for 500 epochs, with 261572 positive edges
11:51:53 Optimization finished
[1] "200 0.13"11:51:53 UMAP embedding parameters a = 1.478 b = 0.9272
11:51:53 Read 1203 rows and found 38 numeric columns
11:51:53 Using Annoy for neighbor search, n_neighbors = 200
11:51:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:51:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873640cb7
11:51:55 Searching Annoy index using 1 thread, search_k = 20000
11:51:56 Annoy recall = 100%
11:52:12 Commencing smooth kNN distance calibration using 1 thread
11:52:43 Initializing from normalized Laplacian + noise
11:52:43 Commencing optimization for 500 epochs, with 261572 positive edges
11:53:04 Optimization finished
[1] "200 0.14"11:53:04 UMAP embedding parameters a = 1.446 b = 0.938
11:53:04 Read 1203 rows and found 38 numeric columns
11:53:04 Using Annoy for neighbor search, n_neighbors = 200
11:53:04 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:53:06 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8759aa6eac
11:53:06 Searching Annoy index using 1 thread, search_k = 20000
11:53:07 Annoy recall = 100%
11:53:22 Commencing smooth kNN distance calibration using 1 thread
11:53:52 Initializing from normalized Laplacian + noise
11:53:52 Commencing optimization for 500 epochs, with 261572 positive edges
11:54:12 Optimization finished
[1] "200 0.15"11:54:12 UMAP embedding parameters a = 1.414 b = 0.9488
11:54:12 Read 1203 rows and found 38 numeric columns
11:54:12 Using Annoy for neighbor search, n_neighbors = 200
11:54:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:54:14 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f8734d992cb
11:54:14 Searching Annoy index using 1 thread, search_k = 20000
11:54:16 Annoy recall = 100%
11:54:33 Commencing smooth kNN distance calibration using 1 thread
11:55:05 Initializing from normalized Laplacian + noise
11:55:06 Commencing optimization for 500 epochs, with 261572 positive edges
11:55:25 Optimization finished
[1] "200 0.16"11:55:26 UMAP embedding parameters a = 1.383 b = 0.9596
11:55:26 Read 1203 rows and found 38 numeric columns
11:55:26 Using Annoy for neighbor search, n_neighbors = 200
11:55:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:55:27 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87264fee9
11:55:27 Searching Annoy index using 1 thread, search_k = 20000
11:55:28 Annoy recall = 100%
11:55:44 Commencing smooth kNN distance calibration using 1 thread
11:56:16 Initializing from normalized Laplacian + noise
11:56:17 Commencing optimization for 500 epochs, with 261572 positive edges
11:56:38 Optimization finished
[1] "200 0.17"11:56:39 UMAP embedding parameters a = 1.352 b = 0.9704
11:56:39 Read 1203 rows and found 38 numeric columns
11:56:39 Using Annoy for neighbor search, n_neighbors = 200
11:56:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:56:40 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f87426c98c7
11:56:40 Searching Annoy index using 1 thread, search_k = 20000
11:56:41 Annoy recall = 100%
11:56:58 Commencing smooth kNN distance calibration using 1 thread
11:57:29 Initializing from normalized Laplacian + noise
11:57:29 Commencing optimization for 500 epochs, with 261572 positive edges
11:57:48 Optimization finished
[1] "200 0.18"11:57:49 UMAP embedding parameters a = 1.321 b = 0.9813
11:57:49 Read 1203 rows and found 38 numeric columns
11:57:49 Using Annoy for neighbor search, n_neighbors = 200
11:57:49 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:57:50 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f875840e5d8
11:57:50 Searching Annoy index using 1 thread, search_k = 20000
11:57:51 Annoy recall = 100%
11:58:06 Commencing smooth kNN distance calibration using 1 thread
11:58:35 Initializing from normalized Laplacian + noise
11:58:35 Commencing optimization for 500 epochs, with 261572 positive edges
11:58:54 Optimization finished
[1] "200 0.19"11:58:54 UMAP embedding parameters a = 1.292 b = 0.9921
11:58:54 Read 1203 rows and found 38 numeric columns
11:58:54 Using Annoy for neighbor search, n_neighbors = 200
11:58:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:58:55 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f873032ba4d
11:58:55 Searching Annoy index using 1 thread, search_k = 20000
11:58:57 Annoy recall = 100%
11:59:11 Commencing smooth kNN distance calibration using 1 thread
11:59:39 Initializing from normalized Laplacian + noise
11:59:39 Commencing optimization for 500 epochs, with 261572 positive edges
11:59:59 Optimization finished
[1] "200 0.2"11:59:59 UMAP embedding parameters a = 1.262 b = 1.003
11:59:59 Read 1203 rows and found 38 numeric columns
11:59:59 Using Annoy for neighbor search, n_neighbors = 200
11:59:59 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:00:00 Writing NN index file to temp file /tmp/Rtmp8Jplzd/file21f876861b9c5
12:00:00 Searching Annoy index using 1 thread, search_k = 20000
12:00:02 Annoy recall = 100%
12:00:16 Commencing smooth kNN distance calibration using 1 thread
12:00:44 Initializing from normalized Laplacian + noise
12:00:45 Commencing optimization for 500 epochs, with 261572 positive edges
12:01:03 Optimization finished
p1_SCT <- DimPlot(SCT.integrated, group.by = c("orig.ident"))
p2_SCT <- DimPlot(SCT.integrated, group.by = c("labels"))
p3_SCT <- DimPlot(SCT.integrated, group.by = c("seurat_clusters"))
grid.arrange(p1_SCT, p2_SCT, p3_SCT, nrow = 3)
# The above seems to do a good job integrating across individuals
SCT.integrated@active.ident <- SCT.integrated$integrated_snn_res.0.43d umap plot
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gridExtra_2.3 Seurat_3.1.3
loaded via a namespace (and not attached):
[1] tsne_0.1-3 nlme_3.1-140 bitops_1.0-6
[4] fs_1.3.1 RcppAnnoy_0.0.12 RColorBrewer_1.1-2
[7] httr_1.4.2 rprojroot_1.3-2 sctransform_0.2.0
[10] tools_3.6.1 backports_1.1.9 R6_2.4.1
[13] irlba_2.3.3 KernSmooth_2.23-15 uwot_0.1.5
[16] lazyeval_0.2.2 colorspace_1.4-1 npsurv_0.4-0
[19] tidyselect_1.1.0 compiler_3.6.1 git2r_0.26.1
[22] plotly_4.9.2.1 labeling_0.3 caTools_1.17.1.2
[25] scales_1.1.1 lmtest_0.9-37 ggridges_0.5.1
[28] pbapply_1.4-0 rappdirs_0.3.1 stringr_1.4.0
[31] digest_0.6.25 rmarkdown_1.13 pkgconfig_2.0.3
[34] htmltools_0.5.0 bibtex_0.4.2 htmlwidgets_1.5.1
[37] rlang_0.4.7 farver_2.0.3 generics_0.0.2
[40] zoo_1.8-8 jsonlite_1.7.0 ica_1.0-2
[43] gtools_3.8.1 dplyr_1.0.2 magrittr_1.5
[46] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
[49] ape_5.3 reticulate_1.16 lifecycle_0.2.0
[52] stringi_1.4.6 whisker_0.3-2 yaml_2.2.1
[55] gbRd_0.4-11 MASS_7.3-52 gplots_3.0.1.1
[58] Rtsne_0.15 plyr_1.8.6 grid_3.6.1
[61] parallel_3.6.1 gdata_2.18.0 listenv_0.8.0
[64] promises_1.1.1 ggrepel_0.8.2 crayon_1.3.4
[67] lattice_0.20-41 cowplot_1.0.0 splines_3.6.1
[70] knitr_1.23 pillar_1.4.6 igraph_1.2.4.1
[73] reshape2_1.4.3 future.apply_1.3.0 codetools_0.2-16
[76] leiden_0.3.1 glue_1.4.2 evaluate_0.14
[79] lsei_1.2-0 metap_1.1 RcppParallel_5.0.2
[82] data.table_1.13.0 png_0.1-7 vctrs_0.3.4
[85] httpuv_1.5.1 Rdpack_0.11-0 gtable_0.3.0
[88] RANN_2.6.1 purrr_0.3.4 tidyr_1.1.2
[91] future_1.18.0 ggplot2_3.3.2 xfun_0.8
[94] rsvd_1.0.1 RSpectra_0.15-0 later_1.1.0.1
[97] viridisLite_0.3.0 survival_2.44-1.1 tibble_3.0.3
[100] workflowr_1.6.2 cluster_2.1.0 globals_0.12.5
[103] fitdistrplus_1.0-14 ellipsis_0.3.1 ROCR_1.0-7